Stanford Online: University Courses & Lectures

Stanford XCS224U: NLU I Presenting Your Research, Part 4: Giving Talks I Spring 2023

Mon, 02 Nov 2026 05:00:00 -0000

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai

This lecture is from the Stanford Online professional course XCS224U: https://online.stanford.edu/courses/xcs224u-natural-language-understanding

Every lecture from this professional course is taken from content within the Stanford University graduate course - CS224U. To follow along, visit the graduate course website: http://web.stanford.edu/class/cs224u/index.html

Uploaded 17-08-23

Christopher Potts Professor and Chair, Department of Linguistics, and Professor, by courtesy, Department of Computer Science at Stanford University http://web.stanford.edu/~cgpotts/

View the entire playlist of lectures: https://www.youtube.com/playlist?list=PLoROMvodv4rOwvldxftJTmoR3kRcWkJBp

To browse all available online courses and programs offered by Stanford Online, visit: http://online.stanford.edu

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford XCS224U: NLU I Presenting Your Research, Part 3: NLP Conference Submission I Spring 2023

Mon, 02 Nov 2026 04:00:00 -0000

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Stanford XCS224U: NLU I Presenting Your Research, Part 2: Writing NLP Papers I Spring 2023

Mon, 02 Nov 2026 03:00:00 -0000

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Stanford Seminar - Technology Management During a Time of U.S.- China Friction

Mon, 02 Nov 2026 02:00:00 -0000

Dr. Richard Dasher
Stanford University

October 23, 2019
China and the United States have a complex codependent relationship in the electronics and computer industries. This relationship is currently under stress. In this talk, I begin by putting the current friction into the context of similar friction between the U.S. and Japan in semiconductors during the 1980s. I will then focus on the issues and forces that will shape the future for IT industries in both countries.

Uploaded 31-10-19

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMWw6rRoeSpkiseTHzWj6vu "

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Stanford CME296 Diffusion and Large Vision Models | Spring 2026 | Lecture 7 - Evaluation

Mon, 02 Nov 2026 01:32:00 -0000

Learn more details about this course: https://online.stanford.edu/courses/cme296-diffusion-and-large-vision-models

To follow along with the course schedule and syllabus, visit: https://cme296.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:05:19 Motivation
00:10:48 Human ratings
00:19:43 Elo rating system
00:26:37 Reference-free metrics
00:29:15 Fréchet inception distance (FID)
00:42:30 CLIPScore
00:44:51 PickScore
00:45:41 Reference-based metrics
00:48:07 Mean squared error (MSE)
00:49:36 Peak signal-to-noise ratio (PSNR)
00:51:54 Structural similarity (SSIM)
01:01:09 Perceptual similarity (LPIPS)
01:05:03 Multimodal LLMs
01:13:10 Faithfulness evaluation (TIFA)
01:17:29 Visual question answering score (VQA)
01:24:40 MLLM-as-a-Judge
01:34:17 Benchmarks

Uploaded 28-05-26

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Afshine Amidi is an Adjunct Lecturer at Stanford University.

Shervine Amidi is an Adjunct Lecturer at Stanford University.

View the course playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rNdy8rt2rZ4T2xM0OjADnfu

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Stanford Seminar - Field-hardened Robotic Autonomy

Mon, 02 Nov 2026 01:00:00 -0000

Kostas Alexis
University of Nevada, Reno

October 18, 2019
This talk will present our contributions in the domain of field-hardened resilient robotic autonomy and specifically on multi-modal sensing-degraded GPS-denied localization and mapping, informative path planning, and robust control to facilitate reliable access, exploration, mapping and search of challenging environments such as subterranean settings. The presented work will, among others, emphasize on fundamental developments taking place in the framework of the DARPA Subterranean Challenge and the research of the CERBERUS (https://www.subt-cerberus.org/) team, alongside work on nuclear site characterization and infrastructure inspection. Relevant field results from both active and abandoned underground mines as well as tunnels in the U.S. and in Switzerland will be presented. In addition, a selected set of prior works on long-term autonomy, including the world-record on unmanned aircraft endurance will be briefly overviewed. The talk will conclude with directions for future research to enable advanced autonomy and resilience, alongside the necessary connection to education and the potential for major broader impacts to the benefit of our economy and society.

Uploaded 31-10-19

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD

Learn more about our Robotics and Autonomous Systems Graduate Program: https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-program

0:00 Introduction
0:34 Resilient Autonomy: A Goal for Robotics
1:29 A Vision for Robotics Research
3:18 Resilient Autonomy in High-Risk Environments
7:50 Sensing-degraded Multi-modal Perception
13:57 Informative Path-Planning in High-Risk Settings
29:27 Last Resort Resilience via Controlled Interaction
32:03 Reconfigurable Systems of Systems
38:33 Funding Support
42:08 Selected Previous Results: Physical Interaction
43:06 Open Source Contributions
43:51 Application Domains

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Stanford Seminar - NEC’s New Strategy for Inbound/Outbound Open Innovation

Mon, 02 Nov 2026 00:00:00 -0000

Motoo Nishihara
NEC Corporation

May 7, 2019
Motoo Nishihara is Executive Vice President and CTO (Chief Technology Officer) of NEC Corporation. He is responsible for NEC’s worldwide research laboratories located in the US, Germany, China, Singapore, Israel, India, and Japan. As the CTO, he is also responsible for the company’s technology innovation strategy and investment management of the company’s technologies and intellectual properties.

Uploaded 11-06-19

Since he was appointed as Senior Vice President in April 2016, he started promoting the concept of “Ecosystem R and D”. Under the concept, he has enhanced collaborative research activities with partners including start-up companies around NEC’s strong technologies. Through the activities, he has realized a unique R and D scheme to set start-ups by researchers, seeking for talented people from outside of the company, and faster monetization opportunities under greater valuations. To accelerate open innovations activities, he established NEC Israel Research Center in 2016 and NEC Laboratories India in 2018.

He has built his carrier in NEC through a business division, a product development division and laboratories. He received his Master of Science degree in Electrical Computer Engineering from Carnegie Mellon University, and Bachelor of Science degree in mathematical engineering and information physics from the University of Tokyo. He received the 18th Advanced Technology Award of Fuji Sankei Business Eye Prize in 2014.

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMa0VlyvCv0h9dOyQcYxFZ0

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Stanford Seminar - Zero-Knowledge and the Next Digital Revolution

Sun, 01 Nov 2026 23:00:00 -0000

March 6, 2023
Alex Pruden of Aleo

Uploaded 29-03-23

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Stanford Webinar - Ecosystems of Change: Partnering to increase the impact of sustainability efforts

Sun, 01 Nov 2026 22:00:00 -0000

In this webinar Professor Pamela Matson and Professor Jim Leape share lessons from their decades of experience working with NGOs, governments, and the private sector to advance sustainability efforts.

Uploaded 17-06-19

You Will Learn:
- Why diverse collaborations are important to drive change in complex systems
- A framework for addressing sustainability as a systems challenge
- How this approach played out in an actual case—the global palm oil sector

This webinar will give you a preview of the Strategies for Sustainability professional education program: https://globalimpact.stanford.edu/

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Stanford Webinar - How Technology is Changing the Way We Work

Sun, 01 Nov 2026 21:32:00 -0000

Stephen Barley has spent thirty-five years studying how new technologies change work and promote organizational change. In this webinar, Steve shares key insights about technological change that he has gained researching firms in the automobile, aerospace, biotechnology, computer and software industries, American hospitals, large architectural firms, and universities. Using studies of technologies as diverse as CT Scanners, Finite Element Analysis, email and the internet, this webinar explores three broad questions:

Uploaded 03-05-13

· How do the rhetorics associated new technologies blind us to what may actually be happening as we adopt them?
· What are the pitfalls of focusing on first order changes brought about by new technologies, and not their second order effects which often have stronger and more lasting implications?
· What is the connection between changes in technologies and changes in role relationships?

Gain valuable insights into how you can optimize technological change in your organization.

Presented by the Stanford Executive Institute: http://stanford.io/nlRtpU

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Stanford Seminar - On and Out-of-Body Interactions and Experiences

Sun, 01 Nov 2026 21:00:00 -0000

March 17, 2023
Jessica Cauchard of Ben Gurion University of the Negev

Mobile devices have become ubiquitous to our everyday life, changing the way we behave, interact with technology and with one another. While initially carried in our hands and pockets, mobile technologies are changing form to fit our lifestyles, enabling interactions on-body (e.g., wearables) and out-of-body (e.g., robots and drones). Yet, much work is needed for them to make sense of complex human situations and enable symbiotic integration of man and machine. My research explores the design, development, and evaluation of natural techniques for on- and out-of-body experience, using a human-centered approach, and exploring diverse form factors and modalities towards human-machine co-existence. In this talk, I will present how we can leverage techniques at the intersection between technology and design to build novel interaction techniques. I will then highlight how interfaces are traditionally bound by form factor, so that on-body ones cater for wearables that remain on the person's body while out-of-body interfaces are built to interact at arm's reach. Finally, I will describe opportunities for future technology development by transitioning between the two states, therefore enabling novel applications, such as extending one's physical abilities.

Uploaded 28-03-23

About the speaker:
Dr. Jessica Cauchard is an assistant professor in the department of Industrial Engineering and Management at Ben Gurion University of the Negev in Israel, where she founded and heads the Magic Lab. Her research is rooted in the fields of Human-Computer and Human-Robot Interaction with a focus on novel interaction techniques and ubiquitous computing. Previously, she was faculty of Computer Science at the Interdisciplinary Center Herzliya between 2017 and 2019. Before moving to Israel, Dr. Cauchard worked as a postdoctoral scholar at Stanford University. She has a strong interest in autonomous vehicles and intelligent devices and how they change our device ecology. She completed her PhD in Computer Science at the University of Bristol, UK in 2013.

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Stanford Seminar - A Superscalar Out-of-Order x86 Soft Processor for FPGA

Sun, 01 Nov 2026 20:00:00 -0000

Henry Wong
University of Toronto, Intel

June 5, 2019
Although FPGAs continue to grow in capacity, FPGA-based soft processors have grown little because of the difficulty of achieving higher performance in exchange for area. Superscalar out-of-order processor microarchitectures have been used successfully for hard processors for many years, but have so far been avoided for FPGAs due to the area increase and the expectation that a loss in clock frequency would more than offset the instructions-per-cycle (IPC) gains.

Uploaded 17-06-19

This talk summarizes my attempt at designing an out-of-order x86 CPU for FPGA. With careful microarchitectural choices and circuit design, I show that it is possible to build a complex microarchitecture on an FPGA, getting about 2.7x performance per clock and 0.8x clock frequency of Altera's Nios II/f single-issue in-order processor. This talk will cover a high-level overview of the microarchitecture and some of the interesting LUT-based circuits used in the processor.

To learn more, read Henry Wong's dissertation at http://hdl.handle.net/1807/80713

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMWw6rRoeSpkiseTHzWj6vu

0:00 Introduction
1:20 FPGA: Field-Programmable Gate Array
3:33 FPGA Soft Processors
6:01 Faster Soft Processors
9:18 ISA: Why x86
10:34 Soft Processor Design Goals
11:46 How are FPGAs different from CMOS?
16:31 Soft Processor Design Methodology
18:20 Our Processor's Microarchitecture
20:49 Processor Area and Frequency Component
25:08 Per-clock performance (SPECint2000)
29:11 Summary 1
30:00 Part 2: Pipeline Details
30:56 Front end: Fetch-decode
34:26 Register Renamer
36:56 Renamer Circuit
38:00 Scheduling: Track dependencies
38:32 Scheduler Size
39:20 Scheduler Circuit
42:11 Scheduler Picker: Bit Scan
45:07 Execution Circuits: Simple ALU
45:24 Execution Circuit: Shifter
47:28 Execution Circuit: Adder
49:20 Memory System Microarchitecture
50:28 Basic Cache Trade-offs
51:34 More Memory System Trade-offs
52:50 L1 Memory System
54:24 What happens to a load: simplified
54:39 Memory System: L1 Circuit
56:01 Summary 2 • Out of order soft processors are useful and feasible

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Stanford Seminar - Autonomous NASA robots breaking records on Mars

Sun, 01 Nov 2026 19:00:00 -0000

March 17, 2023
Vandi Verma of NASA (JPL)

The goal of NASA’s robotics missions is to maximize science return. As instructions can only be sent once every one or more Martian solar days, robots need to be autonomous to be effective. In this seminar I’ll discuss autonomous navigation, flight, sampling and targeting data from the Mars 2020 mission which consists of the Perseverance rover and Ingenuity helicopter. The goal of the mission is to core and store Martian samples for eventual return to Earth. Perseverance’s manipulation and sampling systems have collected samples from unique locations at twice the rate of any prior mission and broken several planetary rover driving records. 88% of all driving has been autonomous - an order of magnitude more than any prior NASA Mars rover. Perseverance can autonomously select scientifically interesting rocks to shoot with its laser for analysis and is in the process of deploying autonomous planning and scheduling of activities. I’ll discuss some open problems that if addressed could further enhance space robotics at NASA JPL.

Uploaded 27-03-23

#nasa #autonomousrobot

0:00 Introduction
0:25 ARTISTS CONCEPTS
1:47 PROJECTS
2:08 MARS 2020 ROBOTICS
3:39 SUCCESSFUL INITIAL MARS SURFACE MISSION
5:57 JEZERO CRATER
6:26 AUTONOMOUS HAZARD AVOIDANCE
10:09 CUMULATIVE ODOMETRY BY DRIVE MODE
11:45 TARGETED MOBILITY IMPROVEMENT
14:44 NAVIGATION AND STATE ESTIMATION
16:39 SUPERVISION
18:25 ROBOTIC OPERATIONS SPACECRAFT COMMANDS
21:10 MULTIPLE SOL DRIVE
21:48 OPPORTUNISTIC EXTENSION DRIVE
23:21 INGENUITY HELICOPTER
25:25 INGENUITY WINTERING
31:21 CORING
31:30 PROADAPT DRILLING AND ABRADING
33:37 SEEDING PROADAPT
34:52 SAMPLE CORES COLLECTED
35:16 MARS SAMPLE RETURN PLANNING
36:51 SAMPLE DEPOT
37:58 DELTA TOP
39:03 HELICOPTER BASE STATION
40:06 DRIVE FAULTS
40:42 GLOBAL LOCALIZATION
41:27 CHALLENGES: MAP VS. IMAGE APPEARANCE
41:52 CHALLENGES: OCCLUSIONS
42:30 PATH PLANNING

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Lecture 8 – NLI 1 | Stanford CS224U: Natural Language Understanding | Spring 2019

Sun, 01 Nov 2026 18:00:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Professor Christopher Potts and Consulting Assistant Professor Bill MacCartney, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Potts
Professor of Linguistics and, by courtesy, Computer Science
Director, Stanford Center for the Study of Language and Information
http://web.stanford.edu/~cgpotts/

Uploaded 18-06-19

Consulting Assistant Professor Bill MacCartney
Senior Engineering Manager, Apple
https://nlp.stanford.edu/~wcmac/

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/

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Stanford CS153 Frontier Systems | The Road Ahead: Resilience Required

Sun, 01 Nov 2026 17:32:00 -0000

For more information about Stanford's online Artificial Intelligence programs, visit: https://stanford.io/ai

Follow along with the course schedule and syllabus, visit: https://cs153.stanford.edu/

In a CS153 Frontier Systems lecture, Joe Sullivan, a veteran security leader who built security teams at Facebook, Uber, and Cloudflare, walks the class through his career at the intersection of government and technology — from federal prosecutor in the 1990s through eBay/PayPal, Facebook, Uber, and Cloudflare — and uses his own criminal prosecution as the central case study.

Uploaded 28-05-26

In 2016, Uber paid researchers $100,000 through what Sullivan's team treated as a bug bounty after they accessed an old AWS database; legal signed off and the CEO approved, but in 2020 Sullivan was personally charged with obstruction of justice for the company's failure to disclose the incident to regulators. He lost at trial in 2022 after the judge instructed the jury that companies cannot retroactively authorize access, but at sentencing in 2023 the judge declared "it wasn't a cover-up" and gave him three years' probation instead of the prison time prosecutors sought — buoyed by over 200 letters of support from the security community.

From this story he draws his core theme: leadership in modern tech requires resilience and a bias toward transparency (he contrasts Uber's 2016 approach with Cloudflare's reflex to write a blog post the moment an incident hits), and he closes with a wide-ranging Q and A on vibe-coding security risks, the shift from data-loss to operational-resilience threats like the Jaguar Land Rover ransomware attack, Anthropic's cyber model rollout, quantum cryptography, executive protection, and the growing case for proactive government action against ransomware gangs.

Joe Sullivan is the CEO of Joe Sullivan Security LLC, advising companies, leading security projects, and mentoring leaders. He also leads Ukraine Friends, a nonprofit aiding children in war zones. A former federal cybercrime prosecutor, Joe worked on safety and security at eBay and PayPal, then went on to lead security at Facebook, Uber, and Cloudflare. He also served on President Obama’s Commission on Enhancing National Cybersecurity.

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Stanford Seminar - Robots in Dynamic Tasks: Learning, Risk, and Safety

Sun, 01 Nov 2026 17:00:00 -0000

March 10, 2023
Joel Burdick of Caltech

Autonomous robots are increasing applied to tasks that involve complex maneuvers and dynamic environments that are difficult to model a priori. Various types of learning methods have been proposed to fill this modeling gap. To motivate the need for learning complex fluid-structure interactions, we first review the SQUID (a ballistically launched and self-stabilizing drone) and PARSEC (an aerial manipulator that can deliver self-anchoring sensor network modules) systems. Next we show how to learn basic fluid-structure interactions using Koopman spectral techniques, and incorporate the learned model into a real-time nonlinear model predictive control framework. The performance of this approach is demonstrated on small drones that operate very close to the ground, where the ground effect normally destabilizes flight. Operational risk abounds in complex robotic tasks. This risk arises both from the uncertain environment, and from incompletely learned models. After reviewing the basics of coherent risk measures, we will show how simple risk aware terrain analysis improved the performance of our legged and wheeled robots in the DARPA Subterranean challenge. Then we will introduce an on-line method to learn the dynamics of an apriori unknown dynamical obstacle, and robustly avoid the obstacle using a novel risk-based, distributionally robust, chance constraints derived from the evolving learned model. We then introduce the concept of risk surfaces to enable fast on-line learning of a priori unknown dynamical disturbances, and show how this approach can adapt a drone to wind disturbances with only 45 seconds of on-line data gathering.

Uploaded 21-03-23

0:00 Introduction
2:47 SQUID I: Key Design Elements
4:00 SQUID II: Vision-based Autonomous Stabilization
6:22 Planetary Exploration Applications
8:01 PARSEC: Payload Anchoring Robotic System for the Exploration of Cliffs Task Motivation and Description
8:32 PARSEC: Aerial Manipulator
9:23 Deployment Interface and Payload Design
10:06 Mission Architecture for Autonomous Deployment
11:39 But what about the real world?
12:30 Machine Learning and Nonlinear Vehicle Control
17:24 Using learned lifted bilinear models for nonlinear MPC
20:27 Learning quadrotor dynamics to improve close-to-ground trajectory tracking
21:00 Learning quadrotor dynamics to improve close-to- ground trajectory tracking performance
24:00 Planning under uncertainty
24:56 Risk-Aware Planning: Chance Constraints
28:15 The DARPA Subterranean Challenge
29:05 STEP: Stochastic Traversability Evaluation and Planning
34:07 Risk-Aware Avoidance of Unknown Dynamic Ostacles
42:54 Robust Risk-Based Learning of Disturbances
46:41 Learning and Introspective Control (LINC)

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Lecture 5 – Sentiment Analysis 1 | Stanford CS224U: Natural Language Understanding | Spring 2019

Sun, 01 Nov 2026 16:00:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Professor Christopher Potts
Professor of Linguistics and, by courtesy, Computer Science
Director, Stanford Center for the Study of Language and Information
http://web.stanford.edu/~cgpotts/

Uploaded 18-06-19

Consulting Assistant Professor Bill MacCartney
Senior Engineering Manager, Apple
https://nlp.stanford.edu/~wcmac/

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/

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Stanford Seminar - Thinking Beyond Tokens, Evan Cheng of Mysten Labs

Sun, 01 Nov 2026 15:00:00 -0000

February 27, 2023
Thinking Beyond Tokens

Uploaded 20-03-23

Evan Cheng of Mysten Labs

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Lecture 9 – NLI 2 | Stanford CS224U: Natural Language Understanding | Spring 2019

Sun, 01 Nov 2026 14:00:00 -0000

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Stanford Seminar - Fixing Media's Business Model

Sun, 01 Nov 2026 13:32:00 -0000

"Fixing Media's Business Model" -Frederic Filloux, Monday Note
Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Uploaded 13-05-13

Learn more: http://bit.ly/WinYX5

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Stanford Seminar - Failure is Not an Option: Our Techniques at the DARPA Subterranean Challenge

Sun, 01 Nov 2026 13:00:00 -0000

Failure is Not an Option: Our Techniques at the DARPA Subterranean Challenge, Lessons Learned, and Next Steps

March 3, 2023

Chris Heckman of CU Boulder

When we in the robotics research community think of what we'd like autonomous agents to tackle in the future, we often target 'dull, dirty, and dangerous' tasks. However, despite a sustained boom in robotics research over the last decade, the number of places we've seen robotics in use for these tasks has been uninspiring. Successful commercialization of autonomous robots have required significant human scaffolding through teleoperation, and incredible amounts of capital, to achieve, and despite this are still limited by brittle systems and hand-engineered components. The reality seems to be that these tasks are not nearly as dull as they might seem on the surface, and instead require ingenuity for success some small but critical fraction of the time. In this talk, I focus on my recent investigation into where the limits of autonomy are for the highly sought-after application to subterranean emergency response operations. This application was motivated by the DARPA Subterranean Challenge, which just last year concluded with the CU Boulder team 'MARBLE' taking third place and winning a $500,000 prize. In this talk, I will give an overview into the genesis of our solution over three years of effort, especially with respect to mobility, autonomy, perception, and communications. I'll also discuss the implications for present-day robotic autonomy and where we go from here

Uploaded 11-03-23

About the speaker:
Chris Heckman is an Assistant Professor in the Department of Computer Science at the University of Colorado at Boulder and the Jacques I. Pankove Faculty Fellow in the College of Engineering and Applied Science. He earned his BS in Mechanical Engineering from UC Berkeley in 2008 and his PhD in Theoretical and Applied Mechanics from Cornell University in 2012, where he was an NSF Graduate Research Fellow. He had postdoctoral appointments at the Naval Research Laboratory in Washington, DC as an NRC Research Associate, and at CU Boulder as a Research Scientist, before joining the faculty there in 2016. From 2021-2023, Heckman also worked with Amazon as a Visiting Academic with their Scout program, focused on mobile robots for sidewalk delivery.

Heckman's research focuses on autonomy, perception, field robotics, machine learning and artificial intelligence. He directs the Autonomous Robotics and Perception Group, a dynamic and close-knit research team aiming to develop practical and explainable techniques in probabilistic artificial intelligence. His work in computer vision, machine learning and sensor fusion has applications to intelligence, defense, and environmental monitoring. His robotics work is used by both government and industry partners in the development of autonomous vehicles, agricultural platforms and other mobile robots including for medicine, search and rescue, and automation.

0:00 Introduction
3:30 Snapshots from the Finals
4:35 What I'm talking about today
4:53 Multi-agent Autonomy with Radar-Based Localization for Exploration
6:21 Tunnel Circuit Mobility
9:39 Urban Circuit
14:24 Final Event
19:05 Perception Sensor Head
19:57 The Human Supervisor's role
22:57 Anyone founding a startup?
26:43 A "solved problem"?
29:27 Fog and lidar-based mapping
31:03 Visual Challenges
31:57 What about radar?
34:12 Radar Sensors
34:48 Compact Radar
35:00 Radar Measurements
35:19 Doppler Measurements
37:32 Updated System Structure
38:26 Updated Doppler Model
39:29 Radar-Inertial Control
40:00 Sparse Mapping
40:28 Learned Outlier Rejection
41:26 Full Pipeline
42:15 The ColoRadar Dataset
42:48 ColoRadar Demo: Outdoor
42:59 ColoRadar Demo: Subterranean

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Lecture 7 – Relation Extraction | Stanford CS224U: Natural Language Understanding | Spring 2019

Sun, 01 Nov 2026 12:00:00 -0000

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Stanford Seminar - Designing the Interactive Paper

Sun, 01 Nov 2026 11:00:00 -0000

March 3, 2023
Andrew Head of University of Pennsylvania

In this talk, I share a vision of interactive research papers, where user interfaces surface information for readers when and where they need it. Grounded in tools that I and my collaborators have developed, I discuss what it takes to design reading interfaces that (1) surface definitions of terms where readers need them (2) explain the meaning of math notation and (3) convey the meaning of jargon-dense passages in simpler terms. In our research, we have found that effective reading support requires not only sufficient document processing techniques, but also the careful presentation of derived information atop visually complex documents. I discuss tensions and solutions in designing interactive papers, and identify future research directions that can bring about powerful augmenting reading experiences.

Uploaded 10-03-23

About the speaker:
Andrew Head is an assistant professor at the University of Pennsylvania in the Department of Computer and Information Science. He co-leads Penn HCI, Penn's research group in human-computer interaction, alongside Stanford alum Danaë Metaxa. Andrew's research focuses on how interactive systems can support the tasks of reading, writing, and programming. His most recent work has focused on how scientific documents can be enhanced with interactivity to help people understand them. Often, his systems incorporate novel backends for document authoring, processing, and understanding. Andrew partners with the Allen Institute for AI in conducting this line of research. His research frequently appears in the top ACM and IEEE-sponsored venues for HCI research, and has been recognized with best paper awards and nominations on numerous occasions.

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Lecture 6 – Sentiment Analysis 2 | Stanford CS224U: Natural Language Understanding | Spring 2019

Sun, 01 Nov 2026 10:00:00 -0000

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Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 16: Post-Training - RLVR

Sun, 01 Nov 2026 09:32:00 -0000

For more information about Stanford's online Artificial Intelligence programs, visit: https://stanford.io/ai

Uploaded 27-05-26

To learn more about enrolling in this course, visit: https://online.stanford.edu/courses/cs336-language-modeling-scratch

Follow along with the course schedule and syllabus: https://cs336.stanford.edu/

Percy Liang
Professor of Computer Science (and courtesy in Statistics)

Tatsunori Hashimoto
Assistant Professor of Computer Science

View the course playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMqXOcazWaTUHhq-yembLCV

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Stanford Webinar - Engineering a Safer Internet with Alex Stamos

Sun, 01 Nov 2026 09:00:00 -0000

The internet can be misused to cause real human harm, but engineers can proactively spot potential future abuses by learning how similar technologies have been abused in the past and the technical and sociological roots of these harms.

Uploaded 09-03-23

In this webinar you will learn about trust and safety engineering, and how it can be implemented to help prevent future cyber abuse. Hear Alex Stamos discuss this important topic, and hear more about his course: https://online.stanford.edu/courses/cs152-trust-and-safety-engineering

Learn more about the Stanford Cyber Policy Center: https://cyber.fsi.stanford.edu/

https://www.youtube.com/channel/UCCECs5SS0kCChuWohA3u5Zw

#internetsafetyexpert #internetsafety

Chapters
0:00 Introduction
2:04 Unique Aspects of Trust and Safety
6:37 The biggest challenges in trust and safety
10:15 Areas of trust and safety work
26:42 Dealing with difficult content

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Lecture 3 – Word Vectors 2 | Stanford CS224U: Natural Language Understanding | Spring 2019

Sun, 01 Nov 2026 08:00:00 -0000

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Stanford Seminar - Hidden Histories - The Lost Women of Interaction Design

Sun, 01 Nov 2026 07:00:00 -0000

February 24, 2023
Erin Malone of California College of The Arts

The history of computing and interaction design is full of tales of the lone genius white male and occasionally gives a nod to the same handful of women we see in history book after history book. This talk looks to challenge the status-quo with stories about a few of the interaction and information pioneers the women of all colors who were trailblazers in male dominated areas. The rise of women in the field maps to the different waves of feminism and the spread of technology through American culture. We'll look at these women's stories and achievements against the backdrop of these cultural and technological shifts.

Uploaded 08-03-23

About the speaker:
Erin is principal at Experience Matters Design, a UX consulting firm specializing in designing systems, complex internal tools and social interfaces, as well as providing research and creating products that matter to under-serviced populations. She consults with the ADL's Center for Technology and Society working on projects to explain, reveal, mitigate and prevent online hate in online social and game platforms. Erin chairs the Interaction Design program at California College of the Arts and teaches a variety of Interaction courses including IXD History and many of the introductory foundational classes.

Erin is the author of the forthcoming book about the women in interaction design history to be published by MIT Press spring 2024 and the co-author of the book Designing Social Interfaces 2nd and 1st editions, published by O'Reilly Media

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Lecture 4 – Word Vectors 3 | Stanford CS224U: Natural Language Understanding | Spring 2019

Sun, 01 Nov 2026 06:00:00 -0000

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Stanford Seminar - Entrepreneurial Thought Leaders: Monica Lam of MokaFive

Sun, 01 Nov 2026 05:32:00 -0000

Monica Lam, MokaFive
In this seminar, entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. Speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks are followed by a half hour of class interaction.

Uploaded 17-05-13

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Seminar - Robotics algorithms that take people into account

Sun, 01 Nov 2026 05:00:00 -0000

February 17, 2023
Anca Dragan of UC Berkeley

I discovered AI by reading “Artificial Intelligence: A Modern Approach” (AIMA). What drew me in was the concept that you could specify a goal or objective for a robot, and it would be able to figure out on its own how to sequence actions in order to achieve it. In other words, we don’t have to hand-engineer the robot’s behavior — it emerges from optimal decision making. Throughout my career in robotics and AI, it has always felt satisfying when the robot would autonomously generate a strategy that I felt was the right way to solve the task, and it was even better when the optimal solution would take me a bit by surprise. In “Intro to AI” I share with students an example of this, where a mobile robot figures out it can avoid getting stuck in a pit by moving along the edge. In my group’s research, we tackle the problem of enabling robots to coordinate with and assist people: for example, autonomous cars driving among pedestrians and human-driven vehicles, or robot arms helping people with motor impairments (together with UCSF Neurology). And time and time again, what has sparked the most joy for me is when robots figure out their own strategies that lead to good interaction — when, as in the work your very own faculty Dorsa Sadigh did in her PhD, we don’t have to hand-engineer that an autonomous car should inch forward at a 4-way stop to assert its turn. Instead, the behavior emerges from optimal decision making. So for this seminar, I'd like to step back a bit. Rather than going through one particular piece of research, I will take the opportunity to share what I've found the underlying optimal decision making problem formulation is for HRI -- and reflect on how we've set up optimal decision making problems that require the robot to account for the people it is interacting with, along with the surprising strategies that have emerged from that along the way. This has come back full circle for me, as I got to include some of this perspective in the very book that drew me into the field, by editing the robotics chapter for the 4th edition of AIMA.

Uploaded 06-03-23

About the speaker:
I am an Associate Professor in the EECS Department at UC Berkeley. My goal is to enable robots to work with, around, and in support of people. I run the InterACT Lab, where we focus on algorithms for human-robot interaction -- algorithms that move beyond the robot's function in isolation, and generate robot behavior that coordinates well with people, and is aligned with what we actually want the robot to do. We work across different applications, from assistive arms, to quadrotors, to autonomous cars, and draw from optimal control, game theory, reinforcement learning, Bayesian inference, and cognitive science. I also helped found and serve on the steering committee for the Berkeley AI Research (BAIR) Lab, and am a co-PI of the Center for Human-Compatible AI. I've been honored by the Sloan Fellowship, MIT TR35, the Okawa award, an NSF CAREER award, and the PECASE award. Learn more: http://people.eecs.berkeley.edu/~anca/

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Lecture 2 – Word Vectors 1 | Stanford CS224U: Natural Language Understanding | Spring 2019

Sun, 01 Nov 2026 04:00:00 -0000

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Stanford Seminar - Human-Centered Explainable AI: From Algorithms to User Experiences

Sun, 01 Nov 2026 03:00:00 -0000

February 17, 2023
Q. Vera Liao of Microsoft Research

Artificial Intelligence technologies are increasingly used to aid human decisions and perform autonomous tasks in critical domains. The need to understand AI in order to improve, contest, develop appropriate trust, and better interact with AI systems has spurred great academic and public interest in Explainable AI (XAI). The technical field of XAI has produced a vast collection of algorithms in recent years. However, explainability is an inherently human-centric property and the field is starting to embrace human-centered approaches. Human-computer interaction (HCI) research and user experience (UX) design in this area are increasingly important especially as practitioners begin to leverage XAI algorithms to build XAI applications. In this talk, I will draw on my own research and broad HCI works to highlight the central role that human-centered approaches should play in shaping XAI technologies, including driving technical choices by understanding users' explainability needs, uncovering pitfalls of existing XAI methods, and providing conceptual frameworks for human-compatible XAI.

Uploaded 04-03-23

About the speaker:
Q. Vera Liao is a Principal Researcher at Microsoft Research Montréal, where she is part of the FATE (Fairness, Accountability, Transparency, and Ethics in AI) group. Her current research interests are in human-AI interaction, explainable AI, and responsible AI. Prior to joining MSR, she worked at IBM Research and studied at the University of Illinois at Urbana-Champaign and Tsinghua University. Her research received multiple paper awards at ACM and AAAI venues. She currently serves as the Co-Editor-in-Chief for Springer HCI Book Series, on the Editorial Board of ACM Transactions on Interactive Intelligent Systems (TiiS), an Editor for CSCW, and an Area Chair for FAccT 2023.

Chapters
0:00 Introduction
10:27 Explainability needs expressed as questions
18:36 XAI for decision support
22:13 A blind spot in XAI? Dual Cognitive Processes
24:01 Model decision boundary vs. (human intuition about) task decision
26:14 Understanding the human-XAI decision-making process
27:13 Comparing feature-based vs. example-based explanations
28:53 Pathways to override AI to have appropriate reliance
28:58 An instantiation: augmenting explanation by relevance
42:11 Selective explanation can signal model errors
48:22 Conclusions: human-centered AI as bridging work

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Stanford Webinar - Tech, Tools, and Team Design: How you can utilize new platforms to work smarter

Sun, 01 Nov 2026 02:00:00 -0000

Learn more at https://online.stanford.edu/programs/stanford-digital-transformation-certificate
Assistant Professor Valentine teaches XDGT121 Rethinking Teams: Designing for the Modern Workforce
https://online.stanford.edu/courses/xdgt121-rethinking-teams-designing-modern-workforce

In order to stay nimble in today’s competitive landscape, it’s crucial that companies are able to swiftly shift their organizational design and meet changing demands. But traditional team formation and resource staffing are often slow, laborious processes. So how do you overcome this barrier? By utilizing our increasingly interconnected and digital world to form “flash teams.” Flash teams, together with new, online platforms, enable you to more efficiently convene talent, measure output, and make informed adjustments in real-time.

Uploaded 30-10-19

In this webinar, Melisa Valentine, Assistant Professor of Management Science and Engineering (MS and E) at Stanford, will introduce this new technique of rapid team formation and propose online platforms that can improve the way you plan and execute work.

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Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 15: Mid/Post-Training

Sun, 01 Nov 2026 01:32:00 -0000

For more information about Stanford's online Artificial Intelligence programs, visit: https://stanford.io/ai

Uploaded 27-05-26

To learn more about enrolling in this course, visit: https://online.stanford.edu/courses/cs336-language-modeling-scratch

This lecture covers mid/post-training (SFT/RLHF).

Follow along with the course schedule and syllabus, visit: https://cs336.stanford.edu/

Percy Liang
Professor of Computer Science (and courtesy in Statistics)

Tatsunori Hashimoto
Assistant Professor of Computer Science

View the course playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMqXOcazWaTUHhq-yembLCV

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Stanford Robotics Seminar ENGR319 | Autumn 2026 | Skydio: Autonomy for a Family of Flying Robots

Sun, 01 Nov 2026 01:00:00 -0000

For more information about Stanford’s graduate programs, visit:
https://stanford.io/grad-ed-engr319-1009

October 9, 2026
This seminar covers:
• How Skydio’s autonomy stack enables flying robots to navigate complex environments, track subjects, and execute missions from high-level operator commands
• The full autonomy stack powering Skydio’s flying robots, spanning both cloud and edge computing
• The F10 Lightrunner, Skydio’s recently unveiled aircraft with a distinctive asymmetric design
• How the F10 extends autonomy to long-range fixed-wing flight, including robotic takeoff and landing from MegaDock
• How these capabilities let a single operator orchestrate multiple drones remotely through a web browser
• How this brings autonomous robotics to public safety and critical infrastructure applications

Uploaded 30-10-26

Follow along with the seminar schedule: https://stanfordasl.github.io/robotics_seminar/archive/

Juraj Kabzan is the VP of Autonomy at Skydio, where he leads robotics software development for advanced flying robots.

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Stanford Seminar - Understanding the Utility of Haptic Feedback in Telerobotic Devices

Sun, 01 Nov 2026 00:00:00 -0000

Jeremy Brown of Johns Hopkins University
December 2, 2022

The human body is capable of dexterous manipulation in many different environments. Some environments, however, are challenging to access because of distance, scale, and limitations of the body itself. In many of these situations, access can be effectively restored via a telerobot. Dexterous manipulation through a telerobot is possible only if the telerobot can accurately relay any sensory feedback resulting from its interactions in the environment to the operator. In this talk, I will discuss recent work from our lab focused on the application of haptic feedback in various telerobotic applications. I will begin by describing findings from recent investigations comparing different haptic feedback and autonomous control approaches for upper-extremity prosthetic limbs, as well as the cognitive load of haptic feedback in these prosthetic devices. I will then discuss recent discoveries on the potential benefits of haptic feedback in robotic minimally invasive surgery (RAMIS) training. Finally, I will discuss current efforts in our lab to measure haptic perception through novel telerobotic interfaces.

Uploaded 08-12-22

About the speaker:
Jeremy D. Brown, the John C. Malone Assistant Professor in the Department of Mechanical Engineering, explores the interface between humans and robotics, with a specific focus on medical applications and haptic feedback. Learn more here: https://engineering.jhu.edu/faculty/jeremy-brown/

► Check out our entire catalog of courses and programs: https://online.stanford.edu/explore

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Stanford Seminar - Democratizing Robot Learning

Sat, 31 Oct 2026 23:00:00 -0000

Dr. Matthew Gombolay, Assistant Professor of Interactive Computing at the Georgia Institute of Technology
November 18, 2022

Uploaded 02-12-22

New advances in robotics and autonomy offer a promise of revitalizing final assembly manufacturing, assisting in personalized at-home healthcare, and even scaling the power of earth-bound scientists for robotic space exploration. Yet, in real-world applications, autonomy is often run in the O-F-F mode because researchers fail to understand the human in human-in-the-loop systems. In this talk, I will share exciting research we are conducting at the nexus of human factors engineering and cognitive robotics to inform the design of human-robot interaction. In my talk, I will focus on our recent work on:
1) enabling machines to learn skills from and model heterogeneous, suboptimal human decision-makers
2) “white-box” that knowledge through explainable Artificial Intelligence (XAI) techniques
3) scale to coordinated control of stochastic human-robot teams

The goal of this research is to inform the design of autonomous teammates so that users want to turn – and benefit from turning – to the O-N mode.

About the speaker:
Dr. Matthew Gombolay is an Assistant Professor of Interactive Computing at the Georgia Institute of Technology. He received a B.S. in Mechanical Engineering from the Johns Hopkins University in 2011, an S.M. in Aeronautics and Astronautics from MIT in 2013, and a Ph.D. in Autonomous Systems from MIT in 2017. Gombolay’s research interests span robotics, AI/ML, human-robot interaction, and operations research. Between defending his dissertation and joining the faculty at Georgia Tech, Dr. Gombolay served as a technical staff member at MIT Lincoln Laboratory, transitioning his research to the U.S. Navy, earning him an R and D 100 Award. His publication record includes a best paper award from American Institute for Aeronautics and Astronautics, a finalist for best student paper at the American Controls Conference, and a finalist for best paper at the Conference on Robot Learning. Dr Gombolay was selected as a DARPA Riser in 2018, received 1st place for the Early Career Award from the National Fire Control Symposium, and was awarded a NASA Early Career Fellowship for increasing science autonomy in space. Learn more here: https://core-robotics.gatech.edu/people/matthew-gombolay/

#robots #robotics

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Stanford Seminar: Human-Centered Methods for Improving API Usability

Sat, 31 Oct 2026 22:00:00 -0000

Brad Myers
Carnegie Mellon University

Dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field will be presented in this seminar. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 31-10-17

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford Seminar - Exploring Alternative Global Futures

Sat, 31 Oct 2026 21:32:00 -0000

"Exploring Alternative Global Futures with the International Futures (IFs) Forecasting System" -Barry Highes, University of Denver
Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Uploaded 17-05-13

Learn more: http://bit.ly/WinYX5

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Stanford Webinar - From Product Management to Venture Capital: A Conversation with Ashmeet Sidana

Sat, 31 Oct 2026 21:00:00 -0000

Learn more: stanford.io/3OJ6T6U

In this webinar, we’ll be hosting Ashmeet Sidana - product manager-turned-venture capitalist. Michael Lepech (Academic Director for our Product Management Program) will sit down with Ashmeet to learn more about his career path–how he got into product management, what skills he learned along the way, and how product management ultimately led him to venture capital.

Uploaded 29-11-22

You Will Learn:
-Which skills are most transferable from product management to venture capital
-How to make the transition from a corporate role into the investment world
-How you can curate your role as a product manager to set yourself up for success later in your career

#productmanagement #venturecapital

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Stanford CS229M - Lecture 20: Spectral clustering

Sat, 31 Oct 2026 20:00:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Uploaded 29-11-22

To follow along with the course, visit:
https://web.stanford.edu/class/stats214/

Lecture notes: https://docs.google.com/viewer?url=https://raw.githubusercontent.com/tengyuma/cs229m_notes/main/master.pdf

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Tengyu Ma
Assistant Professor of Computer Science and Statistics
http://ai.stanford.edu/~tengyuma/

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Stanford CS229M - Lecture 19: Mixture of Gaussians, spectral clustering

Sat, 31 Oct 2026 19:00:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Uploaded 28-11-22

To follow along with the course, visit:
https://web.stanford.edu/class/stats214/

Lecture notes: https://docs.google.com/viewer?url=https://raw.githubusercontent.com/tengyuma/cs229m_notes/main/master.pdf

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Tengyu Ma
Assistant Professor of Computer Science and Statistics
http://ai.stanford.edu/~tengyuma/

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Stanford CS229M - Lecture 18: Unsupervised learning, mixture of Gaussians, moment methods

Sat, 31 Oct 2026 18:00:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Uploaded 27-11-22

To follow along with the course, visit:
https://web.stanford.edu/class/stats214/

Lecture notes: https://docs.google.com/viewer?url=https://raw.githubusercontent.com/tengyuma/cs229m_notes/main/master.pdf

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Tengyu Ma
Assistant Professor of Computer Science and Statistics
http://ai.stanford.edu/~tengyuma/

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Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 14: Data

Sat, 31 Oct 2026 17:32:00 -0000

For more information about Stanford's online Artificial Intelligence programs, visit: https://stanford.io/ai

To learn more about enrolling in this course, visit: https://online.stanford.edu/courses/cs336-language-modeling-scratch

This lecture covers data (filtering, deduplication, mixing, and synthetic data).

Uploaded 27-05-26

Follow along with the course schedule and syllabus, visit: https://cs336.stanford.edu/

Percy Liang
Professor of Computer Science (and courtesy in Statistics)

Tatsunori Hashimoto
Assistant Professor of Computer Science

View the course playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMqXOcazWaTUHhq-yembLCV

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Stanford CS229M - Lecture 17: Implicit regularization effect of the noise

Sat, 31 Oct 2026 17:00:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Uploaded 26-11-22

To follow along with the course, visit:
https://web.stanford.edu/class/stats214/

Lecture notes: https://docs.google.com/viewer?url=https://raw.githubusercontent.com/tengyuma/cs229m_notes/main/master.pdf

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Tengyu Ma
Assistant Professor of Computer Science and Statistics
http://ai.stanford.edu/~tengyuma/

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Stanford CS229M - Lecture 16: Implicit regularization in classification problems

Sat, 31 Oct 2026 16:00:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Uploaded 25-11-22

To follow along with the course, visit:
https://web.stanford.edu/class/stats214/

Lecture notes: https://docs.google.com/viewer?url=https://raw.githubusercontent.com/tengyuma/cs229m_notes/main/master.pdf

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Tengyu Ma
Assistant Professor of Computer Science and Statistics
http://ai.stanford.edu/~tengyuma/

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Stanford CS229M - Lecture 15: Implicit regularization effect of initialization

Sat, 31 Oct 2026 15:00:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Uploaded 24-11-22

To follow along with the course, visit:
https://web.stanford.edu/class/stats214/

Lecture notes: https://docs.google.com/viewer?url=https://raw.githubusercontent.com/tengyuma/cs229m_notes/main/master.pdf

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Tengyu Ma
Assistant Professor of Computer Science and Statistics
http://ai.stanford.edu/~tengyuma/

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Stanford Seminar - Highly optimized quantum circuits synthesized via data-flow engines

Sat, 31 Oct 2026 14:00:00 -0000

Peter Rakyta, Department of Physics of Complex Systems at Eötvös Loránd University
November 9, 2022

The formulation of quantum programs in terms of the fewest number of gate operations is
crucial to retrieve meaningful results from the noisy quantum processors accessible these
days. In this talk I will describe the adaptive circuit compression algorithm [1] developed by
our group and implemented in the SQUANDER package [2]. We scaled up the scope of our
quantum compiler to synthesize circuits up to 9 qubits solely from the unitary associated
with the quantum program. Such capabilities were not demonstrated before by any
optimization based quantum gate synthesis tool. This significant improvement was achieved
by the utilization of a data-flow engine (DFE) based quantum computer simulator, developed
in the collaboration of the Eötvös Loránd University and the Wigner Research Center
(Hungary), Maxeler Technologies and Groq. The quantum computer simulator was designed
to simulate arbitrary quantum circuit consisting of single qubit rotations and controlled
two-qubit gates on Field Programmable Gate Array (FPGA) chips. In our benchmark
comparison with the QISKIT package, the circuits produced by the SQUANDER package (with
the DFE accelerator support) were compressed by 97% in average, while the fidelity of the
circuits were still close to unity by an error of $10^{-4}$. In the talk I will also discuss
possibilities to further improve the synthesis procedure and scale up our approach to higher
number of qubits.

Uploaded 23-11-22

[1] P. Rakyta, Z. Zimborás, Efficient quantum gate decomposition via adaptive circuit
compression, arXiv:2203.04426 (2022).[2]
https://github.com/rakytap/sequential-quantum-gate-decomposer

About the speaker:

Peter Rakyta is a researcher fellow at the Department of Physics of Complex Systems of
Eötvös Loránd University. During his work he developed deep experiences in modelling quantum mechanical systems. Simulations performed by the software package Eötvös Quantum Utilities provided the central results in most of his research work. In 2020 he turned his attention to quantum
informatics. Currently he is involved in several projects related to programming and
simulating quantum computers. His first result in the area is the currently most efficient
algorithm to decompose an arbitrary unitary into a sequence of quantum logical gates

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Stanford Seminar - The Things I Wish I Knew - Lessons Learned from Making Data Product

Sat, 31 Oct 2026 13:32:00 -0000

DJ Patil
Greylock Partners

This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 21-05-13

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford CS229M - Lecture 14: Neural Tangent Kernel, Implicit regularization of gradient descent

Sat, 31 Oct 2026 13:00:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Uploaded 23-11-22

To follow along with the course, visit:
https://web.stanford.edu/class/stats214/

Lecture notes: https://docs.google.com/viewer?url=https://raw.githubusercontent.com/tengyuma/cs229m_notes/main/master.pdf

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Tengyu Ma
Assistant Professor of Computer Science and Statistics
http://ai.stanford.edu/~tengyuma/

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Stanford Seminar - Anytime Anywhere All At Once: Data Analytics in the Metaverse

Sat, 31 Oct 2026 12:00:00 -0000

Niklas Elmqvist of University of Maryland, College Park
November 11, 2022

Mobile computing, virtual and augmented reality, and the internet of things (IoT) have transformed the way we interact with computers. Artificial intelligence and machine learning have unprecedented potential for amplifying human abilities. But how have these technologies impacted data analysis, and how will they cause data analysis to change in the future? In this talk, I will review my group's sustained efforts of going beyond the mouse and the keyboard into the "metaverse" of analytics: large-scale, distributed, ubiquitous, immersive, and increasingly mobile forms of data analytics augmented and amplified by AI/ML models. I will also present my vision for the fundamental theories, applications, design studies, technologies, and frameworks we will need to fulfill the vast potential of this exciting new area in the future.

Uploaded 22-11-22

About the speaker:
Niklas Elmqvist (he/him/his) is a full professor in the iSchool (College of Information Studies) at University of Maryland, College Park. He received his Ph.D. in computer science in 2006 from Chalmers University in Gothenburg, Sweden. Prior to joining University of Maryland, he was an assistant professor of electrical and computer engineering at Purdue University in West Lafayette, IN. From 2016 to 2021, he served as the director of the Human-Computer Interaction Laboratory (HCIL) at University of Maryland, one of the oldest and most well-known HCI research labs in the United States. His research area is information visualization, human-computer interaction, and visual analytics. He is the recipient of an NSF CAREER award as well as best paper awards from the IEEE Information Visualization conference, the ACM CHI conference, the International Journal of Virtual Reality, and the ASME IDETC/CIE conference. He was papers co-chair for IEEE InfoVis 2016, 2017, and 2020, as well as a subcommittee chair for ACM CHI 2020 and 2021. He is also a past associate editor of IEEE Transactions on Visualization and Computer Graphics, as well as a current associate editor for the International Journal of Human-Computer Studies and the Information Visualization journal. In addition, he serves as series editor of the Springer Nature Synthesis Lectures on Visualization. His research has been funded by both federal agencies such as NSF, NIH, and DHS as well as by companies such as Google, NVIDIA, and Microsoft. He is the recipient of the Purdue Student Government Graduate Mentoring Award (2014), the Ruth and Joel Spira Outstanding Teacher Award (2012), and the Purdue ECE Chicago Alumni New Faculty award (2010). He was elevated to the rank of Distinguished Scientist of the ACM in 2018.

#metaverse #dataanalytics

► Check out our entire catalog of courses and programs: https://online.stanford.edu/explore

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Stanford CS229M - Lecture 13: Neural Tangent Kernel

Sat, 31 Oct 2026 11:00:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Uploaded 22-11-22

To follow along with the course, visit:
https://web.stanford.edu/class/stats214/

Lecture notes: https://docs.google.com/viewer?url=https://raw.githubusercontent.com/tengyuma/cs229m_notes/main/master.pdf

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Tengyu Ma
Assistant Professor of Computer Science and Statistics
http://ai.stanford.edu/~tengyuma/

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Stanford CS229M - Lecture 11: All-layer margin

Sat, 31 Oct 2026 10:00:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Uploaded 21-11-22

To follow along with the course, visit:
https://web.stanford.edu/class/stats214/

Lecture notes: https://docs.google.com/viewer?url=https://raw.githubusercontent.com/tengyuma/cs229m_notes/main/master.pdf

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Tengyu Ma
Assistant Professor of Computer Science and Statistics
http://ai.stanford.edu/~tengyuma/

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Stanford MSandE435 Economics of the AI Supercycle | Spring 2026 | Infrastructure, Capstone Case

Sat, 31 Oct 2026 09:32:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

This seminar covers infrastructure and a capstone case on a frontier lab.

Uploaded 27-05-26

Follow along with the course schedule: https://mse435.stanford.edu/

Guest Speaker: Sachin Katti, Head of Industrial Computer, OpenAI

Instructor: Apoorv Agrawal is a Stanford Adjunct Lecturer in Management Science and Engineering and a partner at Altimeter Capital.

Playlist: https://youtube.com/playlist?list=PLoROMvodv4rOAe_DtQYOCKy6_x3irEYV1&si=hI20BgdZqMFtAKS-

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Stanford CS229M - Lecture 10: Generalization bounds for deep nets

Sat, 31 Oct 2026 09:00:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Uploaded 20-11-22

To follow along with the course, visit:
https://web.stanford.edu/class/stats214/

Lecture notes: https://docs.google.com/viewer?url=https://raw.githubusercontent.com/tengyuma/cs229m_notes/main/master.pdf

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Tengyu Ma
Assistant Professor of Computer Science and Statistics
http://ai.stanford.edu/~tengyuma/

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Stanford CS229M - Lecture 9: Covering number approach, Dudley Theorem

Sat, 31 Oct 2026 08:00:00 -0000

Lecture 9: Covering number approach, Dudley Theorem (and implications). Generalization bounds for deep nets

Uploaded 19-11-22

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

To follow along with the course, visit:
https://web.stanford.edu/class/stats214/

Lecture notes: https://docs.google.com/viewer?url=https://raw.githubusercontent.com/tengyuma/cs229m_notes/main/master.pdf

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Tengyu Ma
Assistant Professor of Computer Science and Statistics
http://ai.stanford.edu/~tengyuma/

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Stanford Seminar - True Ventures

Sat, 31 Oct 2026 07:00:00 -0000

Puneet Agarwal
True Ventures

Entrepreneurial Thought Leaders Seminar Series

In this seminar entrepreneurial leaders share lessons from real-world experiences across a range of business settings. Speakers include leaders from global technology companies, startup CEOs, venture capitalists, educators and best-selling authors. Half-hour talks are followed by a half hour of class interaction. Required online discussion for those seeking credit.

Uploaded 22-10-18

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Seminar - Scaling Community by Leading with Love, Manish Chandra of Poshmark

Sat, 31 Oct 2026 06:00:00 -0000

Manish Chandra
Poshmark

In this seminar entrepreneurial leaders share lessons from real-world experiences across a range of business settings. Speakers include leaders from global technology companies, startup CEOs, venture capitalists, educators and best-selling authors. Half-hour talks are followed by a half hour of class interaction. Required online discussion for those seeking credit.

Uploaded 22-10-18

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Seminar - Entrepreneurs and Opportunities in India: the Ecosystem for Startup Growth

Sat, 31 Oct 2026 05:32:00 -0000

"Entrepreneurs and Opportunities in India: the Ecosystem for Startup Growth" -Paula Mariwala, Seedfund; Deepak Ravindran, innoz; Phanindra Sama, redBus

Uploaded 21-05-13

This lecture series presented by the US-Japan Technology Management Center and the US-Asia Technology Management Center explores patterns and challenges of entrepreneurship in Asia. A wide range of expert executive guest speakers will discuss the latest exciting new trends in entrepreneurship, start-up companies and investment in major Asian economies. Explore the culture of entrepreneurship in Asian countries and the aggressive change happening in business across this area.

Learn more: http://stanford.io/16vBuLV

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Stanford Seminar: Computational Ecosystems

Sat, 31 Oct 2026 05:00:00 -0000

Haoqi Zhang
Northwestern University

Dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field will be presented in this seminar. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 24-10-17

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

0:00 Introduction
2:40 Best human solution
3:17 Best machine solution
3:57 Options
5:09 A call for systems: having great components is not enough.
5:53 A call for systems thinking in HCI
7:11 Advancing the approach...
9:01 Computational ecosystems are systems, designed as integrative solutions
10:12 Rest of the talk
13:50 Challenges for organizers
15:20 Cobi: Community-informed planning
15:48 1. Engage the entire community in the planning process
17:33 Core idea: two-phased collaborative planning process w/ crowds and groups
19:32 Core idea: incentive chaining
21:00 2. Help organizers resolve conflicts
22:28 Core idea: Community-informed mixed-initiative interface
24:44 Computational Ecosystem: Community-Informed Planning
28:50 Students need regulation skills
30:26 Agile Research Studio ARS
30:54 ARS scales faculty time
32:00 ARS is a computational ecosystem for developing regulation skills
34:17 ARS: planning
37:39 Distributed help is not one tool...
38:56 Outcomes 3 yrs
39:49 Planning Strategies
40:11 Help and Help-seeking
41:04 Faculty Time: 10-12 hours/week
42:00 Computational Ecosystem: Agile Research Studios
42:36 Regulation skills beyond ARS?
45:25 What's next
45:35 Preview #1: Ecosystem-level architectures
45:55 Example: On-the-go crowdsourcing
48:32 Example: Readily Available Learning Experiences RALE
50:30 a leap: mixed-initiative scaffolds
51:29 Role of technology in advancing human values al scale
52:10 Scaling amplifies compromise
56:01 Delta Lab

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Stanford Seminar: Power to the People, Rich Barton of Zillow Group

Sat, 31 Oct 2026 04:00:00 -0000

Rich Barton
Zillow Group

Uploaded 24-10-17

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Seminar: Beyond Personalization Using Platforms

Sat, 31 Oct 2026 03:00:00 -0000

Gillian Hayes
University of California, Irvine

Dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field will be presented in this seminar. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 24-10-17

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford Seminar - Computing with High-Dimensional Vectors

Sat, 31 Oct 2026 02:00:00 -0000

EE380: Computer Systems Colloquium Seminar
Computing with High-Dimensional Vectors
Speaker: Pentti Kanerva, Stanford CSLI and UC Berkeley Redwood Center for Theoretical Neuroscience

Uploaded 26-10-17

Computing with high-dimensional vectors complements traditional computing and occupies the gap between symbolic AI and artificial neural nets. Traditional computing treats bits, numbers, and memory pointers as basic objects on which all else is built. I will consider the possibility of computing with high-dimensional vectors as basic objects, for example with 10,000-bit words, when no individual bit nor subset of bits has a meaning of its own--when any piece of information encoded into a vector is distributed over all components. Thus a traditional data record subdivided into fields is encoded as a high-dimensional vector with the fields superposed.

Computing power arises from the operations on the basic objects--from what is called their algebra. Operations on bits form Boolean algebra, and the addition and multiplication of numbers form an algebraic structure called a "field." Two operations on high-dimensional vectors correspond to the addition and multiplication of numbers. With permutation of coordinates as the third operation, we end up with a system of computing that in some ways is richer and more powerful than arithmetic, and also different from linear algebra. Computing of this kind was anticipated by von Neumann, described by Plate, and has proven to be possible in high-dimensional spaces of different kinds.

The three operations, when applied to orthogonal or nearly orthogonal vectors, allow us to encode, decode and manipulate sets, sequences, lists, and arbitrary data structures. One reason for high dimensionality is that it provides a nearly endless supply of nearly orthogonal vectors. Making of them is simple because a randomly generated vector is approximately orthogonal to any vector encountered so far. The architecture includes a memory which, when cued with a high-dimensional vector, finds its nearest neighbors among the stored vectors. A neural-net associative memory is an example of such.

Circuits for computing in high-D are thousands of bits wide but the components need not be ultra-reliable nor fast. Thus the architecture is a good match to emerging nanotechnology, with applications in many areas of machine learning. I will demonstrate high-dimensional computing with a simple algorithm for identifying languages.

About the Speaker:
Pentti Kanerva came to Stanford from Finland in 1967 to work in Patrick Suppes' computer laboratory dedicated to computer-assisted instruction. In addition to programming, he designed and built hardware to network city-wide clusters of computer terminals. Kanerva's life-long interest in understanding brains in computing terms motivates his study of computation with high-dimensional vectors. His PhD thesis in Philosophy was published in the book Sparse Distributed Memory, MIT Press, and led to research at NASA Ames Research Center, Swedish Institute of Computer Science, Redwood Neuroscience Institute and, presently, UC Berkeley's Redwood Center for Theoretical Neuroscience. This research has been published in papers on Binary Spatter Code, Random Indexing, and Hyperdimensional Computing.

For more information about this seminar and its speaker, you can visit http://ee380.stanford.edu/Abstracts/171025.html

Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum.

Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Learn more: http://bit.ly/WinYX5

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Stanford CS25: Transformers United V6 I Advancing Science and Medicine with Collaborative AI Agents

Sat, 31 Oct 2026 01:32:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

May 14, 2026
This seminar covers:
• An introduction to general-purpose AI systems from Google DeepMind designed to accelerate scientific discovery and democratize medical expertise
• First, the AI co-scientist, a multi-agent Gemini-based system, assists researchers by systematically generating and refining novel hypotheses for complex scientific challenges
• Secondly, the AI co-physician, AMIE, aims to give doctors superpowers and make medical expertise universally accessible

Uploaded 27-05-26

Follow along with the seminar schedule. Visit: https://web.stanford.edu/class/cs25/

Guest Speaker: Vivek Natarajan (DeepMind)

Instructors:
• Steven Feng, Stanford Computer Science PhD student and NSERC PGS-D scholar
• Karan P. Singh, Electrical Engineering PhD student and NSF Graduate Research Fellow in the Stanford Translational AI Lab
• Michael C. Frank, Benjamin Scott Crocker Professor of Human Biology Director, Symbolic Systems Program
• Christopher Manning, Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science, Co-Founder and Senior Fellow of the Stanford Institute for Human-Centered Artificial Intelligence (HAI)

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Stanford Seminar: The Journey of Other

Sat, 31 Oct 2026 01:00:00 -0000

Catherine Berman
CNote

Uploaded 26-10-17

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford EE259 I Radar principle of operation and architectures (pulsed, FMCW, PMCW) I 2023 I Lec. 10

Sat, 31 Oct 2026 00:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 25-01-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford Seminar - Generalized Reversible Computing and the Unconventional Computing Landscape

Fri, 30 Oct 2026 23:00:00 -0000

EE380: Computer Systems Colloquium Seminar
Generalized Reversible Computing and the Unconventional Computing Landscape
Speaker: Michael P. Frank, Sandia National Laboratories

Uploaded 19-10-17

With the end of transistor scaling now in sight, the raw energy efficiency (and thus, practical performance) of conventional digital computing is expected to soon plateau. Thus, there is presently a growing interest in exploring various unconventional types of computing that may have the potential to take us beyond the limits of conventional CMOS technology. In this talk, I survey a range of unconventional computing approaches, with an emphasis on reversible computing (defined in an appropriately generalized way), which fundamental physical arguments indicate is the only possible approach that can potentially increase energy efficiency and affordable performance of arbitrary computations by unboundedly large factors as the technology is further developed

About the Speaker:
Michael Frank attended the world's first class on nanotechnology during his Freshman year at Stanford in 1988, and received his B.Sci. in Symbolic Systems in 1991, after helping the Cardinal take home the world championship trophy in the ACM International Collegiate Programming Contest. During his graduate work at MIT, his research interests zeroed in on the limits of computing. In 1995 he designed one of the first universal molecular computers using DNA, and, after finding that fundamental thermochemistry forced it to be reversible, he began studying more efficient electronic implementations of reversible computing for his Ph.D. After helping to design the world's first fully reversible microprocessors, he graduated in 1999 and spent most of the next 16 years teaching and continuing his research at the University of Florida and the FAMU-FSU College of Engineering. In 2015 he joined the Non-conventional Computing Technologies department at Sandia National Laboratories, where he is once again trying to help the world understand that it really needs to pursue reversible computing, so that civilization can extract unboundedly manyfold times greater total economic value from computation than would otherwise be possible.

For more information about this seminar and its speaker, you can visit http://ee380.stanford.edu/Abstracts/171018.html

Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum.

Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Learn more: http://bit.ly/WinYX5

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Stanford CME295 Transformers and LLMs | Autumn 2026 | Lecture 4 - Reinforcement Learning with LLMs

Fri, 30 Oct 2026 22:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:06:35 RL setup
00:09:32 Reward design
00:12:23 Policy gradient
00:17:12 Derivation of the gradient
00:23:50 REINFORCE
00:25:51 Importance sampling ratio
00:33:22 Challenges of policy gradients
00:40:34 Proximal Policy Optimization (PPO)
00:42:49 Value function and advantage
00:51:16 Policy ratio
00:53:03 Ratio clipping
00:57:45 KL Penalty
00:59:48 Connection with DPO
01:07:29 Group Relative Policy Optimization (GRPO)
01:09:42 Comparison of GRPO and PPO
01:18:25 Limitations of GRPO
01:22:41 DAPO, Dr. GRPO
01:25:17 On-Policy Distillation (OPD)
01:39:32 Self (OPSD) and Multi-Teacher (OPD) variants

Uploaded 19-10-26

Afshine Amidi is an Adjunct Professor at Stanford University.

Shervine Amidi is an Adjunct Professor at Stanford University.

Follow the playlist: https://youtube.com/playlist?list=PLakGfbKCei80&si=HF2tNywj7nXIQq8X

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Stanford Seminar - Global Entrepreneurs in Korea

Fri, 30 Oct 2026 21:32:00 -0000

"Global Entrepreneurs in Korea" -Sherman Li, Enswers America and David Lee, Kstartup
This lecture series presented by the US-Japan Technology Management Center and the US-Asia Technology Management Center explores patterns and challenges of entrepreneurship in Asia. A wide range of expert executive guest speakers will discuss the latest exciting new trends in entrepreneurship, start-up companies and investment in major Asian economies. Explore the culture of entrepreneurship in Asian countries and the aggressive change happening in business across this area.

Uploaded 22-05-13

Learn more: http://stanford.io/16vBuLV

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Andreas "Andy" Bechtolsheim: The Process of Innovation" - Stanford Engineering Hero Lecture

Fri, 30 Oct 2026 21:00:00 -0000

More than 30 years ago as a Stanford graduate student, Andreas "Andy" Bechtolsheim designed a simple but powerful computer workstation that would help define the modern technology era and launch Sun Microsystems. He's since founded three more startups, including cloud-networking company Arista Networks, where he is now chairman. His investing foresight is legendary. Not only was he the first major backer for Google, but he's also been an early-stage investor in VMware, Brocade and others. Bechtolsheim discusses the process of innovation and describe its importance to Silicon Valley.

Uploaded 23-05-12

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Localization and Filters

Fri, 30 Oct 2026 20:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 14 — Introduction to Localization and Filtering Theory

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 19-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Stanford EE259 I Lidar principle of operation, laser physics I 2023 I Lecture 15

Fri, 30 Oct 2026 19:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 31-01-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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How to Get Users and Grow - Alex Schultz, VP of Growth at Facebook - Stanford CS183F: Startup School

Fri, 30 Oct 2026 18:00:00 -0000

Alex Schultz, VP of Growth at Facebook, shares with us how to get users, how to grow, and how to measure growth.

Uploaded 03-05-17

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Stanford CS153 Frontier Systems | The Discipline of Delivering Value per Gigawatt

Fri, 30 Oct 2026 17:32:00 -0000

For more information about Stanford's online Artificial Intelligence programs, visit: https://stanford.io/ai

Follow along with the course schedule and syllabus, visit: https://cs153.stanford.edu/

In a CS153 Frontier Systems lecture, the class returns to the upstream infrastructure stack with Amin Vahdat, who leads Google's internal compute infrastructure and the TPU program powering Gemini, framing his nearly 30-year career as the discipline of building reliable, balanced supercomputers at a planetary scale. Vahdat argues the industry is over-fixated on gigawatts and flops as headline metrics: at roughly $40 to $50 billion per gigawatt, the question that matters is value delivered per dollar, measured in happy daily active users and paying enterprise customers, not raw capacity.

Uploaded 27-05-26

He walks through the three constraints that govern utility. Reliability, where moving from 99 percent to 99.9 percent uptime closes a 3.65-day annual gap, and where Frontier Labs are newly willing to trade five-nines for double the capacity. System balance, invoking Amdahl's 1967 law that every million instructions per second needs a megabyte per second of I/O, now stretched across 100,000-node synchronous training jobs where a single failed node halts the entire computation. And procurement lead times of two to three years for net-new gigawatts, where land permitting, utility contracts, and 20-year take-or-pay power agreements have replaced the slack capacity that once let hyperscalers ask for ten megawatts on a handshake.

He details Google's optical circuit switch architecture, which uses 136 MEMS-controlled mirrors per chip to programmatically rewire the 3D torus topology connecting TPU racks, allowing failed racks to be virtually swapped in seconds and bandwidth redirected to distant storage clusters for the duration of a five-hour Borg job. Vahdat closes on responsibility: data centers should be a net uplift to local grids and communities, citing Google's choice to accept 10 percent worse power efficiency in water-scarce regions and its gigawatt-scale demand response program that returns capacity to utilities during peak residential load.

Amin Vahdat is a Fellow and Chief Technologist for AI Infrastructure at Google, where his team is responsible for delivering industry-leading infrastructure which spans custom silicon, data centers, network, and supply chain and operations. This infrastructure serves Alphabet, Google and the world, and Artificial Intelligence technologies that empower ML developers and solve customers’ most pressing business challenges. In the past, he was Vice President and General Manager for Google's compute, storage, and network hardware and software infrastructure. Until 2019, he was the Technical Lead and Vice President for the Networking organization at Google.

Before joining Google, Amin was the Science Applications International Corporation (SAIC) Professor of Computer Science and Engineering at UC San Diego (UCSD). He received his doctorate from the University of California Berkeley in computer science, and is a Fellow of the Association for Computing Machinery (ACM).

Amin has been recognized with a number of awards, including the National Science Foundation (NSF) CAREER award, the UC Berkeley Distinguished EECS Alumni Award, the Alfred P. Sloan Fellowship, the Association for Computing Machinery's SIGCOMM Networking Systems Award, and the Duke University David and Janet Vaughn Teaching Award. Amin was awarded the SIGCOMM lifetime achievement award for his contributions to data center and wide area networks. He was inducted into the National Academy of Engineering in 2023 for his contributions to the design and implementation of datacenter and planet-scale networks that power cloud computer systems.

Follow the playlist: https://youtube.com/playlist?list=PLoROMvodv4rN447WKQ5oz_YdYbS74M5IA&si=DOJ5

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Stanford Seminar - New Horizons for Electronic Systems

Fri, 30 Oct 2026 17:00:00 -0000

EE380: Computer Systems Colloquium Seminar
New horizons for electronic systems: Devices, design methods and application areas
Speaker: Giovanni De Micheli, EPFL (Laussane)

Uploaded 04-05-17

Three new game changers enable the design of emerging electronic systems: the use of new devices and materials, the combined integration of circuits and sensors and the application of new design methods and tools that support the correct composition of complex systems from regular components. These ingredients are crucial to design electronic systems in various key areas, such as precision medicine, the Internet of Things, and environmental monitoring/protection.

I will first present the landscape of emerging areas, such as cyber medical systems. Then I will describe enhanced-functionality devices exploiting new materials and geometries, their application into circuits that provide direct data acquisition from the environment by fusing sensors and circuits, as well as new design methods and tools for the creation of complex systems.

I will conclude by presenting some examples from the Swiss Nano-Tera.ch program for engineering complex systems for health care and the environment.

About the Speaker:
Giovanni De Micheli is Professor and Director of the Institute of Electrical Engineering and of the Integrated Systems Centre at EPF Lausanne, Switzerland. He is program leader of the Nano-Tera.ch program. Previously, he was Professor of Electrical Engineering at Stanford University. He holds a Nuclear Engineer degree (Politecnico di Milano, 1979), a M.S. and a Ph.D. degree in Electrical Engineering and Computer Science (University of California at Berkeley, 1980 and 1983).

More detail is available here: http://si2.epfl.ch/~demichel/.

For more information about this seminar and its speaker, you can visit http://ee380.stanford.edu/Abstracts/170503.html

Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum.

Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Learn more: http://bit.ly/WinYX5

0:00 Introduction
0:40 Outline
1:50 A connected world
3:52 Nano-Tera.ch: Key figures
6:31 Example: Tele-medicine
7:48 Implanted computing example
9:30 Example: Monitoring the Alps
13:05 Semiconductor technologies
14:01 Emerging nano-technologies
15:55 FinFET to Nanowire FET
16:43 Vertical silicon nanowire transistors
17:17 Carbon Nanotube Transistors
18:07 2D electronic technologies
19:02 Double gate SiNW FET
20:37 Fabricated device view
22:13 Controllable polarity in 2D
23:22 Modeling various emerging nanogates
24:37 Logic cell design for controllable polarity gates
25:36 Physical design
25:41 Dumbbell-stick diagrams
26:14 Layout abstraction and regularity
26:46 Biconditional Binary Decision Diagrams
28:49 BBDDs are Compact (Majority Function)
31:42 Why majority logic?
36:03 Synthesis Motivation for Majority
36:59 How to Exploit Majority Logic?
37:46 Majority-Inverter Graph
42:46 MIG Size Optimization
43:32 MIG Depth Optimization: Adders
44:44 Majority-based synthesis: MIGthy
45:10 Experimental Results: MCNC circuits
45:59 CMOS Design Results
49:00 Nanoarchitecture Design
49:52 Key points
53:12 Conclusions

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 5: Gaussian Discriminant Analysis

Fri, 30 Oct 2026 16:00:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

For information on the graduate course, CS229 Machine Learning, including the syllabus and course materials, visit the course's website: https://cs229.stanford.edu/index.html-spr26

Uploaded 30-07-26

Teaching Team:
Chris Ré, Professor of Computer Science
Tengyu Ma, Assistant Professor of Computer Science

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Stanford EE259 I MIMO imaging radar, direction of arrival est, target detection I 2023 I Lecture 13

Fri, 30 Oct 2026 15:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 29-01-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford Webinar: Cultivate Your Creativity

Fri, 30 Oct 2026 14:00:00 -0000

Stanford Webinar presented by the Stanford Innovation Master Series (http://scpd.stanford.edu/design/index.php)
Cultivate Your Creativity
Speaker: William Burnett, Stanford University

Uploaded 05-05-17

In this webinar, you’ll learn to debunk the notion that creativity is a special talent assigned to others. You’ll learn to train yourself to behave creatively, thus unlocking your own ability to solve problems.

Join Stanford’s William Burnett, Executive Director of the Design Program at Stanford, as he describes design thinking principles – and actionable methods - that you can put to work immediately, so that you think out of the box, frame problems differently, and generate innovative solutions at your company.

About the Speaker:
Bill Burnett is an Adjunct Professor and the Executive Director of the Design Program at Stanford. He directs the undergraduate and graduate program in design and teaches at the d.school. He received his Bachelors of Science and Masters of Science in Product Design at Stanford and has worked in start-ups and Fortune 100 companies, including seven years at Apple designing award-winning laptops and a number of years in the toy industry designing Star Wars action figures. He holds a number of mechanical and design patents and design awards, and, in addition to his duties at Stanford, he is on the board of VOZ, a socially responsible fashion start-up, and advises several other start-up companies.

About the Stanford Innovation Master Series:
Harness the power of design thinking tools to incorporate innovation into your everyday business practices and transform your organization. Get close and personal with some of the people who invented the tools of design thinking and learn to use the innovative techniques to develop you and your team's creativity, achievement habits, customer empathy and visual thinking.
Learn more at http://scpd.stanford.edu/design/index.php

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Stanford Seminar - Entrepreneurial Thought Leaders: Akshay Kothari and Ankit Gupta of Pulse

Fri, 30 Oct 2026 13:32:00 -0000

Akshay Kothari and Ankit Gupta
Pulse

In this seminar, entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. Speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks are followed by a half hour of class interaction.

Uploaded 22-05-13

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford EE259 I 2023 I Lecture 12 (Extra Session)

Fri, 30 Oct 2026 13:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 28-01-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Live Office Hours with Adora Cheung and Avichal Garg - Stanford CS183F: Startup School

Fri, 30 Oct 2026 12:00:00 -0000

In this session of Live Office Hours, Adora Cheung, YC Partner, and Avichal Garg, previously Director of Product Management at Facebook and now an Expert at YC, sit down with three startups: Edmission, Yembo.ai, and Almabase.

Uploaded 08-05-17

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Stanford Faculty - Meet Chris Somerville

Fri, 30 Oct 2026 11:00:00 -0000

Professor Somerville, University of California, Berkeley, researches synthesis of plant cell wall polysaccharides, the relationship of the structures to cell wall functions, and how the system is regulated, He envisions that knowledge of cell wall structure and function will facilitate the development of plants with improved utility as sources of renewable materials and as biofuel feedstocks.

Uploaded 18-06-12

Enroll in Development of Cellulosic Biofuels course today: http://scpd.stanford.edu/search/publicCourseSearchDetails.do?method=load&courseId=14230374

Learn more about the Energy Innovation and Emerging Technologies certificate program: http://energyinnovation.stanford.edu/

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Stanford EE259 I Radar range and velocity estimation, performance tradeoffs I 2023 I Lecture 12

Fri, 30 Oct 2026 10:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 27-01-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford MSandE435 Economics of the AI Supercycle | Spring 2026 | Enterprise Internal Knowledge

Fri, 30 Oct 2026 09:32:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

This seminar covers intelligence and unlocking enterprise internal knowledge.

Uploaded 22-05-26

Follow along with the course schedule: https://mse435.stanford.edu/

Guest speaker: Yash Patil, CEO of Applied Compute

Instructor: Apoorv Agrawal is a Stanford Adjunct Lecturer in Management Science and Engineering and a partner at Altimeter Capital.

Playlist: https://youtube.com/playlist?list=PLoROMvodv4rOAe_DtQYOCKy6_x3irEYV1&si=hI20BgdZqMFtAKS-

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Stanford Seminar: Changing the World through Innovative Advising and Investing

Fri, 30 Oct 2026 09:00:00 -0000

EE402T: Entrepreneurship in Asian High Tech Industries
Entrepreneurship in the Boardroom: Changing the World through Innovative Advising and Investing
Speaker: Ray Hatoyama, Sozo Ventures

Uploaded 09-05-17

Large Japanese firms have been increasing the number of outside board directors in recent years, but the expectation often appears to be that the outside directors would just come to the board meeting, sit quietly, and then go home. Our seminar this week discusses the rise of activist outside directors in Japan as a type of entrepreneurship dedicated to “changing the world.” We also explore the importance of the emergence of advisors and mentors for startup companies from the investment community, their impact on the quality of how entrepreneurs build their business in Japan or the U.S., and the roles of mentors in helping startups seek international business ties at earlier stages of development.

About the Speaker:
Mr. Ray Hatoyama is formerly a managing director at Sanrio, where he oversaw the development of the Hello Kitty brand in some 130 countries, thereby contributing to an increase in the market cap of the company from $500 million to $3.5 billion in the seven years after he joined the company in 2008. Ray currently serves as an independent director for publicly listed companies such as LINE Corp. (the largest SNS in Japan), Pigeon Corp. (a leading childcare and nursing products company), and Transcosmos Inc. (a leading provider for Business Process Outsourcing). He has served on the board of DeNA Co., Ltd. In addition, Ray is a Venture Partner with Sozo Ventures, a VC fund that focuses on assisting early-stage US-based technology startups in global expansion. Ray has also his own consulting firm, Hatoyama Studio Corp. which helps develop global strategies for small and large companies.

To learn more about the Entrepreneurship in Asian High-Tech Industries seminar course, visit http://asia.stanford.edu/?page_id=7818

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Stanford Seminar: Life Hacks for Breakthrough Thinking

Fri, 30 Oct 2026 08:00:00 -0000

Olivia Fox Cabane, Author and Speaker
Judah Pollack, Riverene Leadership

Olivia Fox Cabane and Judah Pollack, co-authors of the book “The Net and the Butterfly: The Art and Practice of Breakthrough Thinking,” share tips on how we can train ourselves to have more “eureka” moments with mental exercises that awaken more regions of our brains and build our comfort level with failure and uncertainty — two givens on the way to innovation.

Uploaded 09-05-17

Olivia Fox Cabane has lectured at Stanford, Yale, Harvard, MIT, the Marine Corps War College and the United Nations. As keynote speaker and executive coach to the leadership of Fortune 500 companies, she helps people become more persuasive, influential, and inspiring. From a base of thorough behavioral science, Olivia extracts the most practical tools for business, applying the latest in global behavioral science to everyday leadership needs to improve her clients' productivity, effectiveness and efficiency.

Judah Pollack is an author, professional speaker and strategic advisor on the art of leadership. In his latest book, The Net and the Butterfly, Judah takes a deep dive into the neuroscience of breakthrough thinking and has further developed tools and processes to help individuals, teams and organizations capitalize on their native ability to achieve breakthroughs. For the past eight years he has worked with U.S. Army generals and Special Forces, CEOs, start-up founders, and their executive teams. He is a regular contributor to Fast Company, guest lecturer at the University of California-Berkeley’s Haas School of Business, and a regular instructor for the U.S. Army's Red Team Leadership curriculum at the University of Foreign Military and Cultural Studies.

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Webinar - How to Stay Competitive Through Disruption

Fri, 30 Oct 2026 07:00:00 -0000

Learn more in Professor Mendelson's online course: https://online.stanford.edu/courses/xldr249-business-model-innovation?utm_source=webinar&utm_medium=youtube

A business model set in stone is a thing of the past. As new disruptors enter crowded markets, established companies are forced to continuously evolve and optimize how they create, deliver, and capture value.

Uploaded 25-01-24

In this webinar, Professor Haim Mendelson of the Stanford Graduate School of Business will share a framework that you can use as a team, department, or C-level leader to analyze and evaluate your company’s existing business model and uncover new opportunities for innovation and growth.

You Will Learn:
- What makes a successful business model work
- Where to look for opportunities for innovation in your company’s business model
- How and why prominent companies have pivoted their business models to stay competitive

#businessmodel #onlineclasses

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 4: Exponential Family, GLMs Classification

Fri, 30 Oct 2026 06:00:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

For information on the graduate course, CS229 Machine Learning, including the syllabus and course materials, visit the course's website: https://cs229.stanford.edu/index.html-spr26

Uploaded 29-07-26

Teaching Team:
Chris Ré, Professor of Computer Science
Tengyu Ma, Assistant Professor of Computer Science

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Stanford Seminar - BitTorrent Live: A Low Latency Live P2P Video Streaming Protocol

Fri, 30 Oct 2026 05:32:00 -0000

"BitTorrent Live: A Low Latency Live P2P Video Streaming Protocol" -Bram Cohen, BitTorrent
Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Uploaded 22-05-13

Learn more: http://bit.ly/WinYX5

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Stanford Seminar - The Machineries of Doubt and Disinformation

Fri, 30 Oct 2026 05:00:00 -0000

EE380: Computer Systems Colloquium Seminar
The Machineries of Doubt and Disinformation
Cigarettes, Climate and Other Electronic Confusions
Speaker: John R. Mashey, Semi-Retired Ex-CTO

Uploaded 11-05-17

The tobacco industry has long employed the best marketing techniques and adopted the latest technologies for disinformation. The fossil energy industries have employed similar tactics and technologies. For both, the Internet has proved a fertile ground and by now, similar tactics have gained force in politics.

For example, 2009 "Climategate" theft and use of emails against climate scientists seems a precursor of recent Russian efforts in American and French elections.

This talk uses insights from the well-documented history of tobacco and fossil disinformation machinery to anticipate further attacks on science and political processes, including thoughts about the challenges of informed skepticism in the world of Internet, Twitter and Facebook and electronic cigarettes that monitor and control usage, and may report back to the vendor.

About the Speaker:
John Mashey is a semi-retired computer scientist known for early work on UNIX, MIPS microprocessor design, and at Silicon Graphics, supercomputer design and the mid-1990s introduction of the phrase "Big Data".

In 2001, Dr. Mashey was a discussant for an EE380 lecture by famed Stanford climate scientist Stephen Schneider. That led to study of climate science and then of its antagonists, the disinformation groups later found to overlap strongly with those helping Big Tobacco.

He has written many investigative reports on these topics and has given talks on them at various universities.

He is a member of ACM, IEEE CS, AAAS, AGU and APS and the advisory committee for UCSF's Center for Tobacco Control Research and Education, which manages the 90Million-page database of tobacco company internal documents.

For further reading:
http://www.desmogblog.com/user/john-mashey
https://en.wikipedia.org/wiki/John_Mashey
http://www.huffingtonpost.com/author/mash-398

For more information about this seminar and its speaker, you can visit http://ee380.stanford.edu/Abstracts/170510.html

Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum.

Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages.

It is free and open to the public, with new lectures each week.

Learn more: http://bit.ly/WinYX5

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Stanford Webinar: Common Pitfalls of A/B Testing and How to Avoid Them

Fri, 30 Oct 2026 04:00:00 -0000

A Stanford Webinar presented by the Stanford Leadership and Management Science (http://stanford.io/2ppiCxy) and Decision Analysis (http://stanford.io/2pByYDC) graduate certificate programs
"Common Pitfalls of A/B Testing and How to Avoid Them"
Speaker: Ramesh Johari, Stanford University

Uploaded 16-05-17

A/B testing is the process of using randomized controlled experiments on a web page or technology platform to determine which of two versions has a higher conversion rate. While this method of testing has become ubiquitous, there remain some common traps to which even the most sophisticated data scientists fall prey.

Join Ramesh Johari, Associate Professor, Stanford Department of Management Science and Engineering, as he discusses some common practices and pitfalls in A/B testing.

You will learn:

-To analyze the output of A/B tests
-To balance quick results with statistical significance during continuous monitoring
-To maintain data integrity when running simultaneous experiments

About the Speaker:
Ramesh Johari is interested in the design and management of large-scale complex networks, such as the Internet. Using tools from operations research, engineering, and economics, he has developed models to analyze efficient market mechanisms for resource allocation in networks.

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Stanford Seminar: Recent Trends in the Entrepreneurship Ecosystem in India

Fri, 30 Oct 2026 03:00:00 -0000

EE402T: Entrepreneurship in Asian High-Tech Industries
Recent Trends in the Entrepreneurship Ecosystem in India
Speaker: Vish Mishra, Clearstone

Uploaded 16-05-17

US-ATMC Director Richard Dasher engages Vish in an interactive discussion about what’s going on in the ecosystem for entrepreneurship in India. This session is timed just after TiEcon 2017, so that Vish can comment on the Silicon Valley mega-conference sponsored by “The Indus Entrepreneurs” that is billed as the world’s largest entrepreneurship conference.

About the Speaker:
Vish Mishra joined Clearstone in March 2002 with over 30 years of leadership and management experience in the high tech industry including software, Internet, networking and telecommunications. He has been listed as one of 51 most influential global Indian men, and the San Jose Mercury News included him among the 10 most influential people in Silicon valley. Vish also served as Conference Chair of TiEcon 2017.

To learn more about the Entrepreneurship in Asian High-Tech Industries seminar course, visit http://asia.stanford.edu/?page_id=101

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How to Find Product Market Fit - Stanford CS183F: Startup School

Fri, 30 Oct 2026 02:00:00 -0000

Peter Reinhardt, co-founder and CEO of Segment, shares his experience on finding product market fit.

Uploaded 17-05-17

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Stanford Robotics Seminar ENGR319 | Spring 2026 | Integrated Learning and Planning

Fri, 30 Oct 2026 01:32:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

May 8, 2026
This seminar covers:
• Integrated learning and planning with neuro-symbolic concepts
• A general learning and reasoning framework based on neuro-symbolic concepts
• Systems that leverage neuro-symbolic concepts demonstrate superior data efficiency, enable agents to reason and plan more quickly, and achieve strong generalization in novel situations and for novel goals

Uploaded 20-05-26

Follow along with the seminar schedule: https://stanfordasl.github.io/robotics_seminar/

Jiayuan Mao is a Member of Technical Staff at Amazon Frontier AI and Robotics, and an incoming assistant professor at the University of Pennsylvania.

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One Day Without Energy

Fri, 30 Oct 2026 01:00:00 -0000

http://energyinnovation.stanford.edu/

Stanford professors Michael McGehee, Mark Zoback, Chris Somerville and Yi Cui comment on how the world would survive one day with no energy.

Uploaded 18-06-12

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Stanford Seminar - Robots that aren't afraid of contact, an embodied approach

Fri, 30 Oct 2026 00:00:00 -0000

January 12, 2024
Hannah Stuart, UC Berkeley

The world is rich with complex and varied mechanics, which often leads to robots that tend to avoid contact due to uncertainty. However, this richness also opens opportunities for new robotic mechanisms that creatively harness the local environment. In this talk, I'll focus on two recent case studies that use ambient fluids for resilient and compliant grippers with tactile sensing. These inventions apply to logistics pick-and-place automation, as well as more generalized applications. Time allowing, I will also provide another case study in granular media (i.e. sand) interaction for planetary robotics. Inspired by nature, the goal of this research is to access new and resilient robotic behaviors without an over-reliance on digital computing alone, but rather by harnessing morphological computation alongside active control.

Uploaded 23-01-24

About the speaker: https://me.berkeley.edu/people/hannah-stuart/

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Stanford EE259 I Ultrasonic sensor (Sonar) principle of operation and architecture I 2023 I Lecture 8

Thu, 29 Oct 2026 23:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 23-01-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford Lecture - Dancing Cells, Dr. Donald Knuth I 2023

Thu, 29 Oct 2026 22:00:00 -0000

Don Knuth's 27th Annual Christmas Tree Lecture
December 6, 2023

This talk is sort of a sequel to “Dancing Links”, the Christmas Lecture of 2018, because there have been surprising new developments since then—stimulated by the work of Christine Solnon at INSA de Lyon.

Uploaded 22-01-24

When a computer program explores a large space of possibilities, it needs good data structures that are able to undo every tentative decision that has been made, thereby allowing new decisions to take their place.

The dancing links idea is a simple modification of a 60-year-old method (doubly linked lists), which is particularly suited to undoing. As a result, algorithms based on dancing links have become the method of choice for exploring the set of solutions to a huge variety of combinatorial problems.

The dancing cells idea, similarly, is a simple modification of a 30-year-old method (sparse-set representation), and it provides efficient support for that same wide class of applications. Indeed, programs based on dancing cells often turn out to be significantly faster than the analogous programs based on dancing links. And again, there is exquisite choreography!

See more from Don Knuth: https://online.stanford.edu/donald-e-knuth-lectures

► Check out our entire catalog of courses and programs: https://online.stanford.edu/explore

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Stanford Seminar - Filling in the H in CHI, Terry Winograd

Thu, 29 Oct 2026 21:32:00 -0000

Terry Winograd
Stanford University

This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 24-05-13

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford Seminar - Harnessing Data for Social Impact

Thu, 29 Oct 2026 21:00:00 -0000

Harnessing Data for Social Impact: Empowering Communities through Visualization and Social Computing
January 12, 2024
Narges Mahyar of University of Massachusetts Amherst

Uploaded 22-01-24

Today's world faces several complex problems, such as climate change, transportation, infrastructure, education, and healthcare. Technology, if designed right, can play an essential role in informing people, raising awareness, sharing data, and connecting communities and decision-makers to take data-informed actions. In this talk, I present examples of my recent work on building and studying community-centered tools to empower the general public to engage in real-world socio-technical problems such as urban planning and climate change and bring their ideas and comments for shaping future policies. These examples demonstrate my multidisciplinary approach in combining information visualization, HCI, applied ML, and human-centered AI to design and build innovative tools and technologies to address complex socio-technical problems. I then describe a vision for expanding my research to further advance democracy, equity, well-being, and sustainability by fostering the inclusion and empowerment of marginalized populations. I also briefly present my work on inclusive data visualization to empower the public to understand the data that is increasingly part of their lives and make better data-informed decisions. I close with a discussion of how my work can be applied to other socio-technical problems, such as health informatics and learning sciences.

About the speaker:
Narges Mahyar is an Assistant Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. Currently she holds a position as a Radcliffe Fellow at Harvard University. Narges s research falls at the intersection of Human-Computer Interaction, Information Visualization, Social Computing, and Design. She designs, develops, and evaluates novel social computing and visualization techniques that help people explore, understand, and make data-informed decisions. In addition, over the past nine years, she has focused on an emerging interdisciplinary area of Digital Civics, which explores new strategies for scaling and diversifying public engagement in massive decision-making processes related to civic issues. She holds a Ph.D. in Computer Science from the University of Victoria, an MS in Information Technology from the University of Malaya, and a BS in Electrical Engineering from Tehran Azad University. She was a postdoctoral fellow in the Department of Computer Science at the University of British Columbia from 2014 to 2016 and in the Design Lab at the University of California San Diego from 2016 to 2018. Her recognition in the field has been repeatedly confirmed through many accolades for her research, including five Best Paper Awards from CHI 2023, Eurovis 2022, CSCW 2020, the Council of Educators in Landscape Architecture 2017, and VAST 2014; and three Best Paper Honorable Mention Awards from TiiS 2022, DIS 2021 and ISS 2016.

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 3: Weighted Least Squares

Thu, 29 Oct 2026 20:00:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

For information on the graduate course, CS229 Machine Learning, including the syllabus and course materials, visit the course's website: https://cs229.stanford.edu/index.html-spr26

Uploaded 29-07-26

Teaching Team:
Chris Ré, Professor of Computer Science
Tengyu Ma, Assistant Professor of Computer Science

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Stanford EE259 I Inertial navigation, dead reckoning I 2023 I Lecture 7

Thu, 29 Oct 2026 19:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 22-01-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford Webinar - Game-Changer for Solar Energy: Perovskite Semiconductors

Thu, 29 Oct 2026 18:00:00 -0000

In the last five years, advances in perovskite semiconductor technology have improved power conversion efficiency of solar cells at an impressive rate. Are these semiconductors poised to revolutionize the solar industry? Join Professor Michael McGehee as he explores the potential and the challenges of this exciting new technology.

Uploaded 18-10-17

You Will Learn

•What makes perovskite semiconductors so great for solar cells
•How challenges with stability and lead toxicity are being tackled
•How industry can scale up and bring this technology to market, and how it can compete with conventional solar technology

About the Speaker:

Michael McGehee is a professor in the Stanford School of Engineering's Material Science department. His research group studies the electrical and optical properties of organic semiconductors; self-assembly of inorganic nanostructures with organic structure directing agents; and fabrication of low-cost organic photovoltaic cells, light-emitting diodes and transistors. His primary interests and areas of expertise are organic electronics, patterning materials at the nanometer length scale, and developing materials for renewable energy and sustainability applications. Because McGehee has extensive experience with LEDs and lasers, future research projects may involve these areas as well.

Presented By Online Stanford Energy Technology Programs (http://stanford.io/2bG9hqO)

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Stanford Webinar - The Power of Storytelling: Making Brands Come to Life

Thu, 29 Oct 2026 17:32:00 -0000

In the webinar, Prof. Jennifer Aaker and David Hornik of August Capital discuss the importance of stories in fueling growth and innovation in your company as well as the role of stories in shaping how others view your brand. To learn more about Prof. Aaker's course and further your understanding of brand storytelling and innovation visit: http://create.stanford.edu/courses/power-of-stories.php

Uploaded 14-06-13

In an environment characterized by 24/7 news cycles and interconnected customers with the power to rapidly communicate their likes and dislikes to millions at the push of a button, organizations are challenged to keep their brands fresh, relevant, and differentiated. Companies must rethink their approach and consider the power of the narrative to build a strong and innovative brand.

Presented By
The Stanford Innovation and Entrepreneurship Certificate program.

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Classic Visual Recognition

Thu, 29 Oct 2026 17:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 12 — Information Extraction and Classic Visual Recognition

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 18-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Stanford EE292R AI and Data for Science | Autumn 2026 | Models of Computation in the Retina

Thu, 29 Oct 2026 16:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

This lecture is from Stanford EE292R AI and Data for Science, and it covers mechanistically interpretable models of computation in the early visual system.

Uploaded 16-10-26

Follow the course schedule: https://aida4sci.stanford.edu/schedule.html#fall-2026

Speaker:
Stephen Baccus, Professor of Neurobiology at Stanford University

#ComputationalNeuroscience #MechanisticInterpretability #Neuroscience

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Stanford Faculty - Meet Yi Cui

Thu, 29 Oct 2026 15:00:00 -0000

http://energyinnovation.stanford.edu/

Professor Yi Cui studies nanoscale phenomena and their applications broadly defined. Research Interests:

Uploaded 18-06-12

* Nanocrystal and nanowire synthesis and self-assembly
* Electron transfer and transport in nanomaterials and at the nanointerface
* Nanoscale electronic and photonic devices, batteries, solar cells, microbial fuel cells, water filters and chemical and biological sensors.

Enroll today in Professor Cui's Batteries course: http://scpd.stanford.edu/search/publicCourseSearchDetails.do?method=load&courseId=14427375

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Image Processing

Thu, 29 Oct 2026 14:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 11 — Image Processing, Feature Detection and Description

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 17-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Stanford Robotics Seminar ENGR319 | Spring 2026 | Interactive Autonomy

Thu, 29 Oct 2026 13:32:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

May 1, 2026
This seminar covers:
• Learning and control for multi-agent interactions
• Game-theoretic planning and control for robots
• Learning in interactive domains, including imitation learning and reinforcement learning

Uploaded 20-05-26

Follow along with the seminar schedule, visit: https://stanfordasl.github.io/robotics_seminar/

Negar Mehr is an assistant professor in the Mechanical Engineering Department at the University of California at Berkeley. She runs the ICON Lab.

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Data Mining: The Tool of The Information Age

Thu, 29 Oct 2026 13:00:00 -0000

Learn how to explore, analyze, and leverage data sets of any scale in this 60-minute webinar with Google's Search Scientist and Stanford Instructor Rajan Patel.

Uploaded 22-03-11

Learn more: http://scpd.stanford.edu/courses/data-mining-courses.jsp

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Stanford Webinar - A Conversation on the Future of Translational Medicine

Thu, 29 Oct 2026 12:00:00 -0000

View the details of our Drug Development: From Discovery to Commercialization Graduate Certificate:

🎓 https://online.stanford.edu/programs/drug-development-discovery-commercialization-graduate-certificate

What does it take to move a discovery from the lab into the clinic, and who's equipped to lead that work? Translational medicine is the discipline sitting at that critical crossroads — bridging groundbreaking research and real-world patient care.

Uploaded 18-09-26

Listen to and watch faculty leaders of Stanford's Master of Science in Translational Research and Applied Medicine (M-TRAM) program, Dean Felsher, MD, PhD, and Joanna Liliental, PhD, have a lively conversation and Q and A session.

In this webinar, they discuss the real challenges facing translational research today, the opportunities emerging at the intersection of science and industry, and what it takes to train the next generation of innovators ready to turn discovery into impact.

Speakers:

Dean Felsher, MD, PhD, is a Professor of Medicine-Oncology at Stanford University School of Medicine and Director of the Translational Research and Applied Medicine (TRAM) Program, which he has led since 2011. His laboratory investigates how oncogenes initiate and sustain cancer, and he has pioneered model systems showing that inactivating a single oncogene, such as MYC, can be sufficient to reverse tumor growth — a concept known as "oncogene addiction."

Joanna Liliental, PhD, is Executive Director of Stanford's Master's in Translational Research and Applied Medicine (M-TRAM) program, Director of the Translational Applications Service Center (TASC), and Associate Director of the Translational Research and Applied Medicine (TRAM) Program. Since 2011, she has worked alongside Dean Felsher, mentoring researchers as they move scientific findings from the lab into clinical application, including biomarker research for early cancer detection.

The graduate course TRAM223B Drug Development and Clinical Trials will be available for the winter quarter. Enrollment begins October 19. Learn course details:

🎓 https://online.stanford.edu/courses/tram223b-drug-development-and-clinical-trials

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Stanford CME295 Transformers and LLMs | Autumn 2026 | Lecture 1 - Transformers

Thu, 29 Oct 2026 11:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Welcome
00:03:43 Difference from last year’s edition
00:05:01 Class logistics
00:12:42 Timeline
00:16:56 Tokenization
00:37:27 Word2vec (CBOW, Skip-gram)
00:48:26 RNN, LSTM
00:55:18 Attention
00:57:16 Self-attention mechanism
00:59:54 Query, key, value
01:09:35 Transformer architecture
01:12:19 Input
01:16:27 Transformer Encoder
01:18:19 Transformer Decoder
01:22:16 Computational tricks
01:27:35 End-to-end example

Uploaded 28-09-26

Afshine Amidi is an Adjunct Professor at Stanford University.

Shervine Amidi is an Adjunct Professor at Stanford University.

Follow the playlist: https://youtube.com/playlist?list=PLakGfbKCei80&si=HF2tNywj7nXIQq8X

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 2: Supervised Learning Setup

Thu, 29 Oct 2026 10:00:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

For information on the graduate course, CS229 Machine Learning, including the syllabus and course materials, visit the course's website: https://cs229.stanford.edu/index.html-spr26

Uploaded 29-07-26

Teaching Team:
Chris Ré, Professor of Computer Science
Tengyu Ma, Assistant Professor of Computer Science

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Stanford Engineering Hero Lecture - Jim Clark in conversation with John Hennessy

Thu, 29 Oct 2026 09:32:00 -0000

Stanford Engineering Hero Lecture

In this lecture, Stanford University President John L. Hennessy leads a discussion with Stanford Engineering Hero Jim Clark, the entrepreneur and computer scientist best known as a founder of Silicon Graphics, Netscape and several other companies.
Jim Clark is a creator of the computer graphics technology revolutionized the design process for everything from bridges to airplanes to film and TV special effects.

Uploaded 26-06-13

#computergraphics

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Stanford Webinar: How to Lead Innovation Through Implementation

Thu, 29 Oct 2026 09:00:00 -0000

http://scpd.stanford.edu/innovation-and-entrepreneurship/
Innovation = creativity + implementation. In this webinar, Professor Bob Sutton provides practical approaches on turning insights into organizational action—the "implementation" part of the innovation equation. Learn how skilled leaders turn smart-talk into action, how to overcome dysfunctional internal competition, and the power of adopting a small wins strategy.

Uploaded 23-09-12

This webinar is presented by The Stanford Center for Professional Development in association with the Stanford Innovation and Entrepreneurship Certificate program.

Knowing how to incorporate innovation in your work and teams is crucial for creating thriving workplaces and careers. The Stanford Innovation and Entrepreneurship professional certificate program will teach participants essential skills and effective strategies for working in and managing innovative organizations -- and for starting new ones. In classes designed to engage and inspire, you will learn by doing from some of Stanford's best in the field.

Browse courses: http://scpd.stanford.edu/innovation-and-entrepreneurship/individual/courses/marketing-innovation.php

Apply now: https://scpd.stanford.edu/portal/applications/applicationProfile.do?method=loadApplicationIndex&applicationProfileId=16078672

0:00 Introduction
0:10 Meet the Speakers
2:19 A Simple Formula
9:06 McKinsey's 'War for Talent studyin 77 companies Corporate Officers' Reports
10:02 Overcoming knowing doing gaps
10:50 Avoid the smart-talk trap Talk as a substitute for action
12:43 Whytalk substitutes for action
14:18 Linkingtalkandaction: Put people in management positions who know the organization's work
16:54 Linkingtalkandaction: Stop the complaints step up the action
19:01 Linkingtalk and action: Simple language and repetition
19:55 Simple strategies are easier to execute
22:32 When competition tums friends into enemies
27:23 Averting destructive internal competition Meeding out the rotten apples
28:33 Usea"small wins" startegy
29:58 The CEO and the sales campaign Her people were freaked out andoverwhelmed by a crucial sales campaign
31:58 Parting thought #1
34:26 Parting thought #2
36:48 Innovators aren't born that way. They learn to do things DIFFERENTLY
38:20 Innovation Masters Series: Design Thinking and the Art of Innovation
49:28 Save the Date: Upcoming Innovation Offerings

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The Growing Threat and Impact of Web-Based Malware - Stanford Computer Security

Thu, 29 Oct 2026 08:00:00 -0000

http://scpd.stanford.edu/computerSecurity/ - The way malware is being distributed has undergone a fundamental shift, with attackers focusing on planting "drive-by downloads" on legitimate sites in an automated fashion, taking advantage of vulnerabilities in hosting platforms, web applications, and structural vulnerabilities in web sites. This webinar presents data analyzing the growth of infections, and show how webmasters can take a holistic approach to preventing, detecting, containing, and recovering from such attacks.

Uploaded 22-03-11

Speaker: Neil Daswani, Co-founder, Dasient, Inc.

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AI in Healthcare Series: Have We Already Bent the Healthcare Cost Curve?

Thu, 29 Oct 2026 07:00:00 -0000

Learn more about Stanford's online Healthcare AI programs: https://stanford.io/3NEt7uE
Check out the AI in Healthcare series playlist:https://stanford.io/3NEt7uE

Matt Lungren, Stanford University - https://profiles.stanford.edu/matthew-lungren
Justin Norden, Stanford University - https://profiles.stanford.edu/justin-norden

Guest speaker: Eric Larsen, President, TowerBrook Advisors; venture partner, Thrive Capital and SignalFire

In this episode of the Stanford Healthcare AI Podcast, Justin and Matt sit down with Eric Larsen, President at TowerBrook Advisors and venture partner at Thrive Capital and SignalFire, to explore whether AI has already begun to bend the U.S. healthcare cost curve, and why this may be healthcare’s Oppenheimer moment. They unpack a controversial paper on autonomous AI exceeding physicians, debate how far and how fast AI will displace “prestige professions,” and discuss GLP‑1s, site‑of‑care shifts, and a cost-curve paper that suggests nearly $1T in unexpected savings. They ask: What happens when medical expertise becomes abundant and can incumbent health systems adapt in time?

Uploaded 24-09-26

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Innovations in Electrical Engineering - Stanford EE Opportunities

Thu, 29 Oct 2026 06:00:00 -0000

Innovations in electrical engineering have led to major applications and products that fundamentally alter our lifestyles -- from personal computing to environmental sensing. How will we tackle the opportunities and challenges ahead in this dynamic and changing field? Simon Wong, Stanford University's Vice Chair of Electrical Engineering, and Stephen P. Boyd, Professor of Electrical Engineering, will discuss these challenges and several new EE courses that can help you capture these emerging opportunities.

Uploaded 22-03-11

Learn more: http://scpd.stanford.edu/courses/electrical-engineering-courses.jsp

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 3: Calculus of Variations

Thu, 29 Oct 2026 05:32:00 -0000

Lecture 3 — Calculus of Variations

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_3.pdf

Speaker: Dr. Daniele Gammelli

Bio: Dr. Daniele Gammelli is the Research Director of the Machine Intelligence for Robot Autonomy Laboratory at the Italian Institute of Artificial Intelligence (AI4I), and a Researcher in the Department of Aeronautics and Astronautics at Stanford University. Since 2022, he has been a Research Fellow at the Center for Aerospace Autonomy Research (CAESAR) at Stanford and, until 2025, at the Center for Automotive Research at Stanford (CARS). He received his Ph.D. in Machine Learning and Mathematical Optimization from the Technical University of Denmark (DTU) in 2022, where his doctoral thesis was nominated for the DTU Best Thesis of the Year Award and the DTU Young Researcher Award. Dr. Gammelli’s research focuses on developing the algorithmic foundations and system-level methodologies that enable AI-powered autonomous systems to operate safely, efficiently, and reliably in high-stakes environments, with an emphasis on aerospace autonomy and next-generation mobility systems.

Uploaded 11-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Stanford Instructor - Meet Lynda Kate Smith

Thu, 29 Oct 2026 05:00:00 -0000

Lynda Smith brings over 25 years of experience in marketing, sales and general management across a diverse set of industries and has spent the last 15 years working in the area of high technology products and services. In her capacity as SVP Marketing/Chief Marketing Officer for a number of companies including Genesys Telecommunications Laboratories, Nuance, Genpact, Jive, WeVideo and now Twilio, Lynda has been responsible for the full end-to-end marketing responsibilities of an organization. As General Manager of Nuance's call center division, Lynda had P and L responsibility for one of the core businesses of the company with oversight of engineering, sales, services, marketing and M and A activity. Her industry experience includes aerospace, telecommunications, enterprise software, global outsourcing, digital media and social.

Uploaded 24-07-12

Lynda's global background is extensive with hands on experience in driving business in the US, Europe, India, China and other major markets. Lynda has an undergraduate degree in liberal arts from Simpson College and an MBA from the Wharton School of Business at the University of Pennsylvania. She is on the faculty of Stanford University where she serves as a lecturer in Global Entrepreneurial Marketing and Professional Education programs.

Enroll today in Global Entrepreneurial Marketing: http://scpd.stanford.edu/search/publicCourseSearchDetails.do?method=load&courseId=12724

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Stanford CS153 Frontier Systems | Teaching AI to Touch Atoms

Thu, 29 Oct 2026 04:00:00 -0000

For more information about Stanford's online Artificial Intelligence programs, visit: https://stanford.io/ai

Follow along with the course schedule and syllabus; visit: https://cs153.stanford.edu/

In this CS 153 Frontier Systems lecture, Periodic Labs cofounders Liam Ferriss and Dorje Chubak — veterans of OpenAI's post-training team and DeepMind's Genome project, respectively — walked students through their eleven-month-old mission to apply AI to the physical world, specifically using autonomous labs to accelerate the discovery of new materials including high-temperature superconductors.

Uploaded 29-09-26

Operating out of a 40,000-square-foot Menlo Park facility where machine learning researchers work alongside physicists and chemists, their AI system (named Onnes, after the scientist who discovered superconductivity in 1908) runs a continuous loop of computational prediction, robotic synthesis, and experimental verification — closing the feedback cycle between digital intelligence and physical reality in a way no purely in-silico approach can. A key early lesson was abandoning their original plan of spending the first year purely computational: building smaller, semi-manual labs first allowed them to direct the research program and understand what to scale, faster.

Dorje emphasized that the results of applying LLMs to actual atoms have exceeded even their own expectations, while Liam stressed that sample efficiency in reinforcement learning — not benchmark climbing — is the core technical frontier when physical experiments can't be arbitrarily scaled up the way digital rollouts can. They closed by pushing back on student anxiety about AGI displacing their careers, arguing that most scientific domains remain largely untouched by LLMs, that the bar to make meaningful AI-physical world progress is still surprisingly low, and that the history of civilization is essentially a materials story — making their work, in their view, among the highest-leverage bets anyone can make right now.

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 18: RL Policy Optimization

Thu, 29 Oct 2026 03:00:00 -0000

Lecture 18 — Model-free Reinforcement Learning: Policy Optimization

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_4.pdf

Speaker: Dr. Daniele Gammelli

Bio: Dr. Daniele Gammelli is the Research Director of the Machine Intelligence for Robot Autonomy Laboratory at the Italian Institute of Artificial Intelligence (AI4I), and a Researcher in the Department of Aeronautics and Astronautics at Stanford University. Since 2022, he has been a Research Fellow at the Center for Aerospace Autonomy Research (CAESAR) at Stanford and, until 2025, at the Center for Automotive Research at Stanford (CARS). He received his Ph.D. in Machine Learning and Mathematical Optimization from the Technical University of Denmark (DTU) in 2022, where his doctoral thesis was nominated for the DTU Best Thesis of the Year Award and the DTU Young Researcher Award. Dr. Gammelli’s research focuses on developing the algorithmic foundations and system-level methodologies that enable AI-powered autonomous systems to operate safely, efficiently, and reliably in high-stakes environments, with an emphasis on aerospace autonomy and next-generation mobility systems.

Uploaded 13-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Professor Tom Byers on the Stanford Executive Institute

Thu, 29 Oct 2026 02:00:00 -0000

http://www.techamerica.org/stanford-executive-institute - The Stanford Executive Institute has a proven track record of teaching tools and approaches to prepare the technology industry's senior leadership to face the sector's unique set of challenges. In this video, Tom Byers, professor in Stanford's Management Science and Engineering Dept and Academic Director of The Stanford Executive Institute, discusses how this program gives you the tools to be a leader in your enterprise.

Uploaded 23-03-11

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Stanford Faculty - Meet Peter DeMarzo

Thu, 29 Oct 2026 01:32:00 -0000

Peter DeMarzo is the Mizuho Financial Group Professor of Finance, and currently Senior Associate Dean for Academic Affairs, at the Stanford Graduate School of Business. He earned his BA in cognitive science and applied mathematics from the University of California, San Diego, and his MS in operations research and PhD in economics from Stanford University.

Uploaded 17-07-13

In addition to his experience at the Stanford Graduate School of Business, he has taught at the Haas School of Business and the Kellogg Graduate School of Management and was a visiting scholar at the Hoover Institution. He is a member of the National Bureau of Economic Research and past president of the Western Finance Association. His research is in the area of corporate finance, asset securitization, financial contracting, and market regulation. Recent work has examined issues of the optimal design of securities, optimal compensation mechanisms, regulation of insider trading and broker-dealers, bank capital regulation, and the influence of information asymmetries on corporate disclosures and investment.

Peter's research has received awards including the Barclays Global Investors/Michael Brennan best paper award from the Review of Financial Studies, and the Western Finance Association Corporate Finance Award. He won the Sloan Teaching Excellence award in 2004 and 2006, and the Cheit Outstanding Teaching Award in 1998. He is co-author of a leading corporate finance textbook, and currently teaches MBA and PhD courses in Corporate Finance and Financial Modeling.

Peter teaches Financing Innovation: Valuing Projects and Firms, an online course in the Stanford Innovation and Entrepreneurship professional certificate - http://create.stanford.edu/courses/financing-innovation.php

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Webinar: AI Agent Simulation of Human Behavior with Michael Bernstein

Thu, 29 Oct 2026 01:00:00 -0000

- Explore these concepts and more in the UI/UX Design for AI Products course: https://bit.ly/stf-aid-webinar
- Learn how to build intelligent AI agents: https://online.stanford.edu/programs/agentic-ai
- Browse all Stanford Online AI courses: https://online.stanford.edu/artificial-intelligence/courses-and-programs

Uploaded 29-09-26

What happens when AI agents are designed to simulate human behavior?

In this Stanford Online webinar delivered in collaboration with Global Alumni, Professor Michael Bernstein explores the emerging possibilities of AI agents and their ability to model, interact with, and simulate aspects of human behavior. The session offers a closer look at how advances in artificial intelligence are changing the way we think about human-AI interaction, simulation, and the design of intelligent systems.

Drawing on Stanford research in human-AI interaction and social computing, Professor Bernstein examines the opportunities and challenges that arise as AI systems become increasingly capable of representing human behaviors and interactions.

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 1: Introduction

Thu, 29 Oct 2026 00:00:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

For information on the graduate course, CS229 Machine Learning, including the syllabus and course materials, visit the course's website: https://cs229.stanford.edu/index.html-spr26

Uploaded 29-07-26

Teaching Team:
Chris Ré, Professor of Computer Science
Tengyu Ma, Assistant Professor of Computer Science

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Program Overview: Welcome to Product Management

Wed, 28 Oct 2026 23:00:00 -0000

Uploaded 30-09-26

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Global Product Design - Stanford Center for Professional Development

Wed, 28 Oct 2026 22:00:00 -0000

The game is changing. Products from the West used to be inherently desirable and localization was adequate to capture market share, but these days are gone and organizations are increasingly designing for a new global landscape with different requirements and expectations. In this webinar, you'll learn about models for global design and some of the key challenges and opportunities in this new and exciting space.

Uploaded 08-04-11

Learn more about professional programs offered by the Stanford Center for Professional Development by visiting http://scpd.stanford.edu/programs/professional-certificates.

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 2: Optimization Theory

Wed, 28 Oct 2026 21:32:00 -0000

Lecture 2 — Optimization Theory

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_2.pdf

Speaker: Prof. Marco Pavone

Bio: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Uploaded 11-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Stanford Faculty - Meet Perry Klebahn

Wed, 28 Oct 2026 21:00:00 -0000

http://scpd.stanford.edu/innovation-and-entrepreneurship/

Perry Klebahn is an entrepreneur to the core. He received his Masters from Stanford in the product design program in 1991 and has taught there periodically since 1996. He left Stanford with his degree and his design project -- a snowshoe. He turned his project into a business, creating the Atlas Snowshoe Company. His experience taught him several things: you can't do anything significant on your own, you need a team; and engineering something is not nearly as much fun as marketing what you have engineered. In 2000, Perry sold his snowshoe business and moved to Southern California to run sales and marketing for Patagonia, an outdoor clothing brand. There, he got a chance to market on a global scale, learning to balance the need to drive sales while building a brand with important commitments and a sustainability message. In 2007 he took on a new challenge at Timbuk2, the original bike messenger bag company. As CEO of the San Francisco-based company, Perry focused on delivering custom products and an innovative customer experience. He implemented many techniques used at the d.school at Timbuk2. A founding faculty member of the d.school, Perry teaches classes including: Launchpad, Design Garage, d.Health, and d.leadership. He runs the executive programs for the d.school.

Uploaded 27-09-12

Professor Klebahn teaches in the Stanford Innovation and Entrepreneurship professional certificate program. This certificate delivers essential skills and effective strategies for working in and managing innovative organizations - and for starting new ones. Designed to engage and inspire, be prepared to get out of your chair and use your whole brain, because creativity and innovation take practice.

A joint offering of the Stanford Center for Professional Development at the School of Engineering and the Stanford Graduate School of Business.

Learn more: http://scpd.stanford.edu/innovation-and-entrepreneurship/
Apply now: http://bit.ly/SoKSVl

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Webinar: What AI Can and Cannot Do: Intelligence Augmentation in Practice with Michael Bernstein

Wed, 28 Oct 2026 20:00:00 -0000

- Explore these concepts in the AI-Powered Product Innovation course: https://bit.ly/pin-stf-webinar
- Learn how to build intelligent AI agents: https://online.stanford.edu/programs/agentic-ai
- Browse all Stanford Online AI courses: https://online.stanford.edu/artificial-intelligence/courses-and-programs

Uploaded 30-09-26

Artificial intelligence is transforming how organizations approach problem-solving, decision-making, and innovation—but knowing where AI can create reliable value is just as important as knowing where it cannot.

In this Stanford Online webinar delivered in collaboration with Global Alumni, Professor Michael Bernstein explores the practical realities of AI adoption and the role of human judgment in an increasingly AI-enabled workplace. The session examines how to distinguish between problems AI can solve reliably and those that continue to require human expertise, judgment, and oversight.

Drawing on research and practical frameworks for human-AI interaction, Professor Bernstein explores how leaders and professionals can evaluate AI opportunities more thoughtfully, assess potential risks, and determine where AI can genuinely augment human capabilities.

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Stanford Instructor - Meet Donna Novitsky

Wed, 28 Oct 2026 19:00:00 -0000

Donna co-teaches Global Entrepreneurial Marketing at Stanford in the School of Engineering and to entrepreneurial executives worldwide. She has recently taught groups of entrepreneurs from France, Poland, Denmark, Finland, Chile, Singapore and other parts of Asia. She also is running her third startup. Yiftee is about Microgifts - a simple concept integrating merchants, social media and mobile. Now you can give your friends a real latte, beer or cupcake to say Happy Birthday, thanks or just because. Friends stop by the merchants and pick them up, driving traffic and repeat business. Donna uses her 25 yrs of hands-on entrepreneurship to bring her lessons to life, often driving home concepts by coaching students in their own new businesses.

Uploaded 24-07-12

Prior to Yiftee, Donna was CEO of Big Tent Design, a fast-growth Internet company that provides a free web software platform for groups. BigTent is now a subsidiary of Federated Media, and continues to help community groups such as parenting clubs stay organized and vibrant online and off with over a million "chief household officers" using it on a regular basis. Donna joined Big Tent from Mohr, Davidow Ventures (MDV) where she was a Partner for almost nine years, serving as VP Marketing in sixteen MDV-funded start-ups. Prior to MDV, Donna was VP Marketing at Clarify Inc., a CRM pioneer that she helped establish, grow and take public. Her career began at Sun Microsystems in product management.

Enroll today in Global Entrepreneurial Marketing: http://scpd.stanford.edu/search/publicCourseSearchDetails.do?method=load&courseId=12724

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 17: RL Value-Based Methods

Wed, 28 Oct 2026 18:00:00 -0000

Model-free Reinforcement Learning: Value-based Methods

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_4.pdf

Speaker: Dr. Daniele Gammelli

Bio: Dr. Daniele Gammelli is the Research Director of the Machine Intelligence for Robot Autonomy Laboratory at the Italian Institute of Artificial Intelligence (AI4I), and a Researcher in the Department of Aeronautics and Astronautics at Stanford University. Since 2022, he has been a Research Fellow at the Center for Aerospace Autonomy Research (CAESAR) at Stanford and, until 2025, at the Center for Automotive Research at Stanford (CARS). He received his Ph.D. in Machine Learning and Mathematical Optimization from the Technical University of Denmark (DTU) in 2022, where his doctoral thesis was nominated for the DTU Best Thesis of the Year Award and the DTU Young Researcher Award. Dr. Gammelli’s research focuses on developing the algorithmic foundations and system-level methodologies that enable AI-powered autonomous systems to operate safely, efficiently, and reliably in high-stakes environments, with an emphasis on aerospace autonomy and next-generation mobility systems.

Uploaded 13-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Stanford Seminar - What Asia Entrepreneurship Means for Silicon Valley

Wed, 28 Oct 2026 17:32:00 -0000

"What Asia Entrepreneurship Means for Silicon Valley" -
David Brunner- moduleQ
Jim Nguyen- Kynded
Yukai Chou- YukaiChou.com

Uploaded 22-07-13

This lecture series presented by the US-Japan Technology Management Center and the US-Asia Technology Management Center explores patterns and challenges of entrepreneurship in Asia. A wide range of expert executive guest speakers will discuss the latest exciting new trends in entrepreneurship, start-up companies and investment in major Asian economies. Explore the culture of entrepreneurship in Asian countries and the aggressive change happening in business across this area.

Learn more: http://stanford.io/16vBuLV

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Stanford Webinar - Leadership Competencies for Air Transport Professionals

Wed, 28 Oct 2026 17:00:00 -0000

Stanford-IATA Aviation Management certificate program webinar covering five critical competencies for working in the aviation industry.
Info: http://aviation.stanford.edu/

Uploaded 08-04-11

In today's fast-paced, global environment, all leaders of teams in air transport ranging from frontlines, new and senior managers, working in an airline, travel agency, freight forwarding company, airport, or even in a Civil aviation authority face a number of challenges. In addition to operational excellence - strategy, execution, leadership, innovation, and decision-making are five critical competencies for leaders and business managers in the aviation industry to overcome today and tomorrow air transport industry challenges. Our speakers will share why the 5 competencies are important, how they are embedded in the Stanford-IATA Aviation Management Certificate, as well as some specific success stories within the aviation industry.

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Course Preview - Topics in International Technology Management

Wed, 28 Oct 2026 16:00:00 -0000

Watch new lectures every week in this seminar series at http://bit.ly/UnxxnS

The theme for Autumn 2012 is "Green Technologies in Transportation: Recent Developments from Asia." Students will learn about technology and business trends, innovations, opportunities in Asia and Japan, and implications for US firms and researchers. The course will eature distinguished speakers from industry and government.

Uploaded 04-10-12

Topics Include

New materials, fuels, and energy storage for vehicles
Automobile and aircraft design
Smart grids and intelligent transportation systems
Mobile mesh networks

Instructors

Richard Dasher, Consulting Professor, Electrical Engineering

Graduate seminars may be viewed online at no charge. Students seeking credit for a 1-unit seminar course need to enroll in another course in order to meet the 3-credit unit enrollment requirement.

Register for this seminar series today! http://bit.ly/QLrXot

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Course Preview: Next-Generation Energy Storage

Wed, 28 Oct 2026 15:00:00 -0000

Enroll in the full course: https://online.stanford.edu/courses/xeiet215-next-generation-energy-storage-batteries-grid-solutions?utm_medium=Video&utm_term&utm_campaign=XEIET215Previewf&utm_source=Youtube

Stanford professor Yi Cui breaks down one of the leading candidates for grid storage: redox-flow batteries. Holding energy in liquids rather than solid cells could change how storage scales.

Uploaded 07-10-26

What you'll learn:
• Why capacity is set by tank size rather than cell size
• How flow batteries let energy and power scale independently
• The three obstacles left: how much the liquids can carry, whether the molecules survive repeated cycling, and the cost of the membrane

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Course Overview: AA228 Decision Making Under Uncertainty

Wed, 28 Oct 2026 14:00:00 -0000

Enrollment in the graduate course AA228 Decision Making Under Uncertainty is open now: https://stanford.io/AA228

Hear about this course and learn why Associate Professor of Aeronautics and Astronautics, Mykel Kochenderfer, loves it. Discover what you'll learn and how it will impact your decision-making process.

Uploaded 28-07-26

The Decision Making Under Uncertainty graduate course is designed to increase awareness and appreciation for why uncertainty matters, particularly for aerospace applications.

Browse all graduate courses available this autumn quarter: https://stanford.io/Autumn-2026

View past AA228 Decision Making Under Uncertainty lectures here: https://www.youtube.com/playlist?list=PLoROMvodv4rNXnHUlDJ1jnvZgNN5Q60d3

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 1: Course Overview

Wed, 28 Oct 2026 13:32:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 1 — Course Overview; Introduction to Nonlinear Optimization

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_1.pdf

Speaker: Prof. Marco Pavone

Bio: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Uploaded 11-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Taking Healthcare ERM to the Next Level -- Strategic Decisions and Risk Management Program

Wed, 28 Oct 2026 13:00:00 -0000

http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&from=courseprofile&certificateId=8151942 Learn how value-driven Enterprise Risk Management meets the challenges of integrating management of all the downside risks a healthcare organization faces, plus how to capitalize on uncertainties in the value creation opportunities driving the short- and long-term prosperity of the enterprise.

Uploaded 19-04-11

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Sensors and Intro to CV

Wed, 28 Oct 2026 12:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 8 — Robotic Sensors and Introduction to Computer Vision

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 09-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Entrepreneurship in Asian High-Tech Industries

Wed, 28 Oct 2026 11:00:00 -0000

http://scpd.stanford.edu/coursesSeminars/seminarsAndWebinars.jsp Presented by the US-Japan Technology Management Center, the US-Asia Technology Management Center, this lecture series explores the patterns and challenges of entrepreneurship in Asia.

Uploaded 26-04-11

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 16: Fundamentals of RL

Wed, 28 Oct 2026 10:00:00 -0000

Lecture 16 — Fundamentals of Reinforcement Learning

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_4.pdf

Speaker: Dr. Daniele Gammelli

Bio: Dr. Daniele Gammelli is the Research Director of the Machine Intelligence for Robot Autonomy Laboratory at the Italian Institute of Artificial Intelligence (AI4I), and a Researcher in the Department of Aeronautics and Astronautics at Stanford University. Since 2022, he has been a Research Fellow at the Center for Aerospace Autonomy Research (CAESAR) at Stanford and, until 2025, at the Center for Automotive Research at Stanford (CARS). He received his Ph.D. in Machine Learning and Mathematical Optimization from the Technical University of Denmark (DTU) in 2022, where his doctoral thesis was nominated for the DTU Best Thesis of the Year Award and the DTU Young Researcher Award. Dr. Gammelli’s research focuses on developing the algorithmic foundations and system-level methodologies that enable AI-powered autonomous systems to operate safely, efficiently, and reliably in high-stakes environments, with an emphasis on aerospace autonomy and next-generation mobility systems.

Uploaded 13-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Stanford Innovation and Entrepreneurship Certificate Overview

Wed, 28 Oct 2026 09:32:00 -0000

http://create.stanford.edu/ Taught by world-renowned faculty from the Graduate School of Business and the School of Engineering, the Stanford Innovation and Entrepreneurship Certificate is a self-paced interactive, online program where participants will gain essential skills and effective strategies to generate and implement new ideas. Our team will collaborate with you to design a custom learning experience that best meets the needs of your team or organization.
Learn more at http://create.stanford.edu/

Uploaded 08-08-13

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Stanford Webinar - Project-Based Work -- Your Key to Innovation

Wed, 28 Oct 2026 09:00:00 -0000

http://apm.stanford.edu
Organizations strive for a culture of innovation -- which is often challenging to achieve. During this webinar, John Warren, VP of Strategic Business Solutions at IPS Learning, will share with you how innovation requires, and is fostered by, Project-Based Work (PBW). Innovation doesn't just happen -- it's the result of a culture within an organization that executes its strategy and exceeds client expectations.

Uploaded 01-08-12

Learn how leaders of PBW can drive innovation by leading people, building project infrastructure and creating the resilience required for successful strategy execution.

Speakers

John Warren
Vice President of Strategic Business Solutions
Executive Director, SAPM Center of Excellence for Strategy Execution
IPS Learning

Enroll in professional education courses today: http://scpd.stanford.edu/coursesSeminars/profCourses.jsp

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Motion Planning Sampling-Based

Wed, 28 Oct 2026 08:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 7 — Motion Planning II: Sampling-Based Methods

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 09-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Motion Planning Graph Search Methods

Wed, 28 Oct 2026 07:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 6 — Motion Planning I: Graph Search Methods

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 09-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Instructor:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Timothy Guertin on The Stanford Executive Institute

Wed, 28 Oct 2026 06:00:00 -0000

http://scpd.stanford.edu/search/publicCourseSearchDetails.do?method=load&courseId=9626281 President and CEO of Varian Medical Systems, Inc shares how Stanford Executive Institute can transform how you do business.

Uploaded 05-05-11

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Stanford CS329A Self-Improving AI Agents | Part 4 | Learning from Feedback with Tools/Code

Wed, 28 Oct 2026 05:32:00 -0000

Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education course: https://learn.stanford.edu/agentic-ai-2026.html
- XCS329 graduate course: https://online.stanford.edu/courses/cs329a-self-improving-ai-agents

Uploaded 03-08-26

A similar curriculum is covered in XCS329z https://online.stanford.edu/courses/cs329z-engineering-ai-agents

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University

View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA

Video Summary:
This lecture recording from Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery on October 3, 2025, covers three approaches to improving language models through feedback. ReAct interleaves chain-of-thought reasoning with tool-calling actions and is evaluated on HotpotQA, FEVER, and WebShop. RLEF, or Reinforcement Learning from Execution Feedback, trains coding agents using public and private unit test results within a PPO training loop, evaluated on CodeContests. Constitutional AI, developed by Anthropic, uses a written set of principles along with model self-critique and revision to train a preference model through reinforcement learning from AI feedback rather than human feedback. The lecture compares how each method sources its feedback signal, from environment interaction to execution results to AI-generated critique, and reviews related work including WebGPT, Code Monkeys, and SWE-bench.

Speaker Bio:
Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University

Dr. Aakanksha Chowdhery is pushing the frontier of agentic LLMs, focusing on recursive self-improvement and long-horizon agents that learn and deploy in the real world. She is one of the few researchers globally who has led frontier model training end-to-end, across both dense and mixture-of-experts (MoE) architectures. At Google, she led the 540B PaLM model, the largest densely trained language model in the world at the time. She subsequently drove pre-training and scaling of Gemini's MoE models across multiple generations, and contributed key components to PaLM-E, Med-PaLM, and the Pathways infrastructure underpinning Google's large-model efforts. She went on to build and lead pretraining teams for open intelligence efforts at Reflection and Meta. Earlier, she held research roles at Microsoft Research and Princeton. At Stanford, where she earned her PhD, she teaches CS329A (Self-Improving AI Agents) and serves as Program Chair for MLSys 2026.

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Advanced Trajectory Optimization

Wed, 28 Oct 2026 05:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 5 — Advanced Methods for Trajectory Optimization

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 09-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Instructor:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Stanford MSandE435 Economics of the AI Supercycle | Spring 2026 | The GPU Economy

Wed, 28 Oct 2026 04:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Follow along with the course schedule: https://mse435.stanford.edu/

Guest Speakers: Sunny Madra, Groq (now Nvidia) and Brad Gerstner, Altimeter

Instructor: Apoorv Agrawal is a Stanford Adjunct Lecturer in Management Science and Engineering and a partner at Altimeter Capital.

Uploaded 23-07-26

Playlist: https://youtube.com/playlist?list=PLoROMvodv4rOAe_DtQYOCKy6_x3irEYV1&si=hI20BgdZqMFtAKS-

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Stanford Online - Learn more about Courses, Certificates, and Degrees from Stanford Online

Wed, 28 Oct 2026 03:00:00 -0000

The Stanford Center for Professional Development connects professionals worldwide to the research and teaching of Stanford University faculty in the School of Engineering and related academic departments. Qualified individuals may study for master of science degrees on a part-time basis, pursue graduate certificates and professional certificates, take individual graduate courses and professional courses, participate in workshops, view free online seminars and more. Courses are delivered online, on the Stanford campus in the heart of Silicon Valley, and at the work site.

Uploaded 15-02-11

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Stanford Lecture: Donald Knuth - All Questions Answered (May 12, 2011)

Wed, 28 Oct 2026 02:00:00 -0000

May 12, 2011

Donald Knuth, in this Stanford Engineering Hero Lecture, answers questions from the audience--from his opinion of Wikipedia to the next great discovery in computer science to his most memorable mistake.

Uploaded 17-05-11

Learn more: http://scpd.stanford.edu/knuth/

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Stanford Webinar - Financing Innovation: Common Mistakes Even Great Investors Make

Wed, 28 Oct 2026 01:32:00 -0000

Innovation cannot happen without the funding to bring an idea to life. In this webinar Stanford Professor Peter DeMarzo, Senior Associate Dean of the Graduate School of Business and instructor within the Stanford Innovation and Entrepreneurship certificate, discusses how to value ideas and innovations so they receive the proper funds to launch.

Uploaded 13-08-13

0:00 Introduction
2:14 Risk and Return: Insights from 86 Years of Investor History
6:03 Return Volatility
7:45 Risk vs. Return: The Security Market Line
11:21 How Do Individual Investors Behave?
14:21 Individuals Trade A Lot
15:05 Individual Performance
15:39 Turnover and Returns
17:57 Swimming with Sharks: Picking Stocks, or Picked Off?
23:55 Mutual Fund Performance
26:00 Institutional Fund Managers
27:57 Active vs. Passive Investing
36:50 A Star has Fallen: Bill Miller's Legg Mason Value Trust
41:12 Some Final Thoughts...
46:20 New Course Launching in September: Financing Innovation: Valuing Projects and Firms

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Trajectory Tracking

Wed, 28 Oct 2026 01:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 4 — Trajectory Tracking and Closed-Loop Control

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 09-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Instructor:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Stanford Seminar - Emerging Trends and Applications of Light Field Displays

Wed, 28 Oct 2026 00:00:00 -0000

Gordon Wetzstein
Stanford University

This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 14-10-15

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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A Conversation About Energy, Ricardo Levy and Howie Rosen

Tue, 27 Oct 2026 23:00:00 -0000

Stanford's Ricardo Levy and Howie Rosen offer insights into entrepreneurial opportunities in the renewable energy industry. What does the future hold? Where will there be innovation? Levy and Rosen teach courses in the new Molecular Engineering of Energy Technologies online graduate certificate at Stanford University.

Uploaded 07-08-12

Learn more about the Molecular Engineering of Energy Technologies certificate: http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=15533128

Enroll in graduate courses today! https://online.stanford.edu/explore?type=All&credentials[156]=156

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Stanford CS234 Reinforcement Learning I Offline RL 3 I 2024 I Lecture 10

Tue, 27 Oct 2026 22:00:00 -0000

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai

Uploaded 30-10-24

To follow along with the course, visit the course website:
https://web.stanford.edu/class/cs234/

Emma Brunskill
Associate Professor of Computer Science at Stanford University
https://cs.stanford.edu/people/ebrun/

Learn more about the course and how to enroll: https://online.stanford.edu/courses/cs234-reinforcement-learning

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford CS329A Self-Improving AI Agents | Part 1 | Course Overview

Tue, 27 Oct 2026 21:32:00 -0000

Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education course: https://stanford.io/4zOyjPN
- XCS329 graduate course: https://online.stanford.edu/courses/cs329a-self-improving-ai-agents

Uploaded 03-08-26

A similar curriculum is covered in XCS329z https://online.stanford.edu/courses/cs329z-engineering-ai-agents

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA

Video Summary:
This first lecture videoof Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery and Azalia Mirhoseini on September 22, 2025, opens with an overview of scaling laws that link model parameters, training compute, and dataset size to lower test loss in large language models from GPT-2 through GPT-4. It covers few-shot and zero-shot learning, the emergence of chain-of-thought reasoning in larger models, and the role of instruction tuning and reinforcement learning from human feedback in the development of ChatGPT. The lecture introduces inference-time scaling through the Large Language Monkeys project, which repeatedly samples a model's outputs and selects correct answers with a verifier to improve performance without retraining. It then traces the shift from single-turn chatbots to agent workflows such as prompt chaining, routing, parallelization, and orchestrator-worker patterns, using Claude Code and deep research tools as examples. The session closes with logistics for the course.

Speaker Bios:
Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University

Dr. Aakanksha Chowdhery is pushing the frontier of agentic LLMs, focusing on recursive self-improvement and long-horizon agents that learn and deploy in the real world. She is one of the few researchers globally who has led frontier model training end-to-end, across both dense and mixture-of-experts (MoE) architectures. At Google, she led the 540B PaLM model, the largest densely trained language model in the world at the time. She subsequently drove pre-training and scaling of Gemini's MoE models across multiple generations, and contributed key components to PaLM-E, Med-PaLM, and the Pathways infrastructure underpinning Google's large-model efforts. She went on to build and lead pretraining teams for open intelligence efforts at Reflection and Meta. Earlier, she held research roles at Microsoft Research and Princeton. At Stanford, where she earned her PhD, she teaches CS329A (Self-Improving AI Agents) and serves as Program Chair for MLSys 2026.

Azalia Mirhoseini
Assistant Professor of Computer Science, Stanford University

Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Research Grant, the Google ML and Systems Junior Faculty Award, MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and co

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Stanford Seminar - Electric Vehicles and Startups, Marc Tarpenning Co-Founder of Tesla

Tue, 27 Oct 2026 21:00:00 -0000

"Electric Vehicles and Startups" -Marc Tarpenning, Tesla

The purpose of this seminar series is to help students and professionals develop the tools to apply the engineering mindset to problems that stem from climate change, in order to consider and evaluate possible stabilizing, remedial and adaptive approaches. This course is not a crash course on climate change or policy. Instead we will focus on learning about and discussing climate problems that seem most likely to benefit from the engineering mindset.

Uploaded 14-10-15

Learn more: http://stanford.io/19O05eN

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Stanford CS234 I Guest Lecture on DPO: Rafael Rafailov, Archit Sharma, Eric Mitchell I Lecture 9

Tue, 27 Oct 2026 20:00:00 -0000

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai

Stanford CS234 Reinforcement Learning I Offline RL 2 and Guest Lecture on Direct Preference Optimization (DPO) by authors:
Rafael Rafailov: https://arxiv.org/search/cs?searchtype=author&query=Rafailov,+R
Archit Sharma: https://arxiv.org/search/cs?searchtype=author&query=Sharma,+A
Eric Mitchell: https://arxiv.org/search/cs?searchtype=author&query=Mitchell,+E

Uploaded 30-10-24

To follow along with the course, visit the course website:
https://web.stanford.edu/class/cs234/

Emma Brunskill
Associate Professor of Computer Science at Stanford University
https://cs.stanford.edu/people/ebrun/

Learn more about the course and how to enroll: https://online.stanford.edu/courses/cs234-reinforcement-learning

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford Seminar - Christina Smolke of Stanford, Steve Jurvetson of DFJ and Astro Teller of Google X

Tue, 27 Oct 2026 19:00:00 -0000

Christina Smolke, Stanford University
Steve Jurvetson, DFJ
Astro Teller, Google X

In this seminar, entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. Speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks are followed by a half hour of class interaction.

Uploaded 14-10-15

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Robotics Seminar ENGR319 | Winter 2025 | Embodied Intelligence

Tue, 27 Oct 2026 18:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

March 7, 2025
This seminar covers:
• Embodied intelligence with morphing materials and mechanisms
• Facing open design challenges, such as complete degradability and biocompatibility, arbitrary shape morphing, and freely reconfigurable degrees of freedom
• How addressing these challenges sometimes requires looking beyond conventional mechatronic systems
• The critical role of computational design and optimization in assisting robotic and machine design and control

Uploaded 22-07-26

Follow along with the seminar schedule: https://stanfordasl.github.io/robotics_seminar/archive/

Lining Yao is an Assistant Professor at the Mechanical Engineering department, the University of California, Berkeley, where she directs the Morphing Matter Lab (morphingmatter.org).

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Stanford Webinar - Build an Entrepreneurial Culture within your Organization

Tue, 27 Oct 2026 17:32:00 -0000

Entrepreneurship is synonymous with innovation, especially in Silicon Valley. In this webinar Stanford Professors Howie Rosen and Ricardo Levy discuss how your organization can continue to be disruptive in industry and maintain an entrepreneurial edge

Uploaded 14-08-13

Learn more about the Molecular Engineering of Energy Technologies graduate certificate:
http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=15533128

0:00 Introduction
0:56 Caveat...
1:37 Start-up or established company: evaluating a new business before you "leap" has advantages.
2:23 Successful new businesses have two parts: 1. A "good" idea; and, 2. A "business model"
3:40 Engineering and science-based businesses take time and money to show technical feasibility.
5:59 Learning objectives for today's webinar
6:34 Entrepreneurial leadership
7:23 Key issues
7:40 the difference between leadership and management
9:29 LEADERSHIP vs MANAGEMENT
10:37 Management and leadership with limited resources
13:01 the nature of the entrepreneurial commitment
14:17 Total emotional engagement -- The Positive
15:41 Total emotional engagement -- the negative
17:13 Consequences of control
18:15 The leader's role in the face of uncertainty
19:55 Absorbing Uncertainty...
21:38 The entrepreneurial "edge"
23:45 The established company "edge"
25:00 some of the corporate challenges
26:55 some of the personal challenges to be aware of
29:20 Some final thoughts...
31:01 and remember...

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Stanford CS234 Reinforcement Learning I Offline RL 1 I 2024 I Lecture 8

Tue, 27 Oct 2026 17:00:00 -0000

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Stanford Seminar - Wafer-Scale Thermionic Energy Converters?

Tue, 27 Oct 2026 16:00:00 -0000

"Wafer-Scale Thermionic Energy Converters?"- Roger T. Howe of Stanford University

Uploaded 15-10-15

About the talk:
Thermionic energy converters (TECs) were conceived in 1915, demonstrated in 1939, and were the focus of huge investments during the Cold War by the U.S. and the Soviet Union. A 6 kW thermionic converter, fabricated using precision machining technology, was flown in 1987 by the Soviet Union. TEC research ramped down in the 1990s and the field withered to a few embers by 2005. I will review the simple physics of TECs and what the barriers have been to applying them to direct heat-to-electricity conversion for terrestrial applications.

Over the past decade, new materials, processes, and device architectures are being explored at Stanford, the Max Planck Institute, and other research groups around the world. I will provide an update on recent progress at Stanford on thermally isolated, suspended micro cathodes inspired by microbolometer infrared imagers. In order to reach useful efficiencies, both very high thermal isolation between the cathode and the anode and a much lower anode work function are needed. Over the past year, two approaches to fabricating anodes with work functions of 1 eV or less have been demonstrated at Stanford: the electrostatic doping of graphene and the surface photovoltage in a semiconductor anode.

Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum.

Speaker Abstract and Bio can be found here: http://ee380.stanford.edu/Abstracts/151014.html

For more info about the EE380 series: http://ee380.stanford.edu

Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Learn more: http://bit.ly/WinYX5

0:00 Introduction
4:15 Thermionic Energy Converter
6:29 Thermionics Timeline: The Early Days
10:30 A Cold War Application: Space Power
14:28 Thermionic Converter Design Cathode work function sets hot-side temperature
15:46 Barrier Index: a critical parameter
19:00 Microfabricated Thermionic Converters?
20:57 Standard MEMS Thermal Isolation Design
22:34 A Pixelated Suspended Cathode
25:33 Nano-Gap Heat Transfer
28:43 Testing Thermionic Components
29:21 Mixed Alkali Earth Oxide Coatings Lower the Work Function of Tungsten
30:07 Density Functional Theory (DFT) Simulations of Work Function and Formation Energy
32:06 Computation of Thermionic Emission Current from First Principles using DFT
33:14 Work Functions for Cathodes and Anodes
43:08 Topping Cycles for Fossil-Fuel Power Plants: Themoelectronic Triodes
47:03 A 21st Century Application for TEC: Concentrated Solar Thermal Power
48:28 Photon-Enhanced Thermionic Emission (PETE) Energy Diagram
51:04 Tandem Solar Thermal Cycle
55:48 Spark Thermionics @ Cyclotron Road

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Stanford CS234 Reinforcement Learning I Policy Search 3 I 2024 I Lecture 7

Tue, 27 Oct 2026 15:00:00 -0000

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Stanford Student Profile | Angela Marsiaj of Staeye, Innovation Masters Series

Tue, 27 Oct 2026 14:00:00 -0000

Angela Marsaiaj of Staeye highlights the importance of learning by doing for a small business owner at the Innovation Masters Series and how the experience made "everything come to life."

Uploaded 17-10-13

Learn the problem-solving tools and problem-finding frameworks that lead to innovation and strategic leadership in the Innovation Masters Series: Design Thinking and the Art of Innovation course, pioneered by the Design Group and the d.school at Stanford.

Learn more: http://scpd.stanford.edu/design/

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Stanford CS329A Self-Improving AI Agents | Part 2 | Test-Time Compute Scaling

Tue, 27 Oct 2026 13:32:00 -0000

Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education course: https://stanford.io/4zOyjPN
- XCS329 graduate course: https://online.stanford.edu/courses/cs329a-self-improving-ai-agents

Uploaded 03-08-26

A similar curriculum is covered in XCS329z https://online.stanford.edu/courses/cs329z-engineering-ai-agents

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA

Video Summary:
This lecture recording from Stanford's CS329A, Self-Improving AI Agents, delivered by Azalia Mirhoseini on September 26, 2025, examines test-time compute scaling as a way to improve model performance without additional training. It covers the Large Language Monkeys paper's finding that solve rate follows a power law as the number of parallel samples increases, driven by a long tail of hard problems each model solves only rarely, and it distinguishes majority voting from oracle verification to define the generation-verification gap. The lecture also covers a paper on optimally scaling test-time compute, which compares parallel sampling against sequential revision and introduces outcome and process reward models to guide search over candidate solutions. It closes with the Arkon paper on inference-time architecture search, which combines techniques including fusion, critic, ranker, and unit test generation across multiple models, reporting an average 14.1 percent improvement in pass-at-one accuracy over GPT-4 and Claude 3.5 Sonnet on reasoning, math, and coding tasks. Discussion throughout addresses when pre-training still outperforms added test-time compute for the hardest problems.

Speaker Bio:
Azalia Mirhoseini
Assistant Professor of Computer Science, Stanford University

Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Research Grant, the Google ML and Systems Junior Faculty Award, MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including WSJ, NYT, Forbes, MIT Technology Review, IEEE Spectrum, WIRED, and TechCrunch.

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Neil Daswani - A Conversation on Stanford, Twitter, and Mobile Security

Tue, 27 Oct 2026 13:00:00 -0000

http://scpd.stanford.edu/computerSecurity/
Neil Daswani, co-founder of Dasient, Inc., a stealth-mode Internet security company, is an instructor in the Stanford Advanced Computer Security certificate program. In the past, Neil has served in a variety of research , development, teaching, and managerial roles at Google, Stanford University, DoCoMo USA Labs, Yodlee, and Bellcore (now Telcordia Technologies). While at Stanford, Neil cofounded the Stanford Center for Professional Development (SCPD) Security Certification Program.

Uploaded 14-08-12

Enroll in professional education courses today! https://online.stanford.edu/programs/advanced-cybersecurity-program

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Stanford CS234 Reinforcement Learning I Policy Search 2 I 2024 I Lecture 6

Tue, 27 Oct 2026 12:00:00 -0000

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Stanford Seminar - Mostly Missless Memory in the Mill CPU

Tue, 27 Oct 2026 11:00:00 -0000

"Mostly missless memory in the Mill CPU: Avoiding the pain of cache misses in a statically-scheduled architecture" -Ivan Godard, Out-of-the-Box Computing

Uploaded 24-10-13

Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Learn more: http://bit.ly/WinYX5

0:00 Introduction
1:02 The Mill Architecture
3:37 Wide issue
5:02 Exposed pipeline
9:16 The memory hierarchy
11:10 The load problem
12:08 Every CPU's goal - hide load latency
13:20 Hardware approach - dynamic scheduling
22:18 Several different load problems...
24:36 Mill "deferred loads"
28:56 Deferred loads vs. alternatives
32:57 Reordering constraints
34:50 So eliminate aliasing
35:53 Alias immunity
37:09 Loads across control flow
38:14 Pickup loads
39:41 Compiler strategy
41:32 The trade-off
42:22 Implementation - the retire station
43:33 Implementation - stream monitoring
46:06 Retire station allocation
48:00 The fine print #1
50:33 The fine print #2
51:29 When stores miss...
52:45 Valid bits
58:50 Hierarchy from 40,000 ft.

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Stanford CS234 Reinforcement Learning I Policy Search 1 I 2024 I Lecture 5

Tue, 27 Oct 2026 10:00:00 -0000

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Stanford Faculty Profile | Ron Howard, Professor Management Science and Engineering

Tue, 27 Oct 2026 09:32:00 -0000

Professor Howard of the Stanford School of Engineering explains the catalyst for coining the term decision analysis, why he started the Strategic Decision and Risk Management (SDRM) program and how decision analysis can assist anyone with the unanswered, difficult questions in their life.

Uploaded 03-09-13

Learn more about the Strategic Decision and Risk Management program and Howard's online Decision Analysis course at: http://strategicdecisions.stanford.edu/

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Stanford Seminar - Entrepreneurial Though Leaders: Matt Rabinowitz of Natera

Tue, 27 Oct 2026 09:00:00 -0000

Matt Rabinowitz
Natera

In this seminar, entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. Speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks are followed by a half hour of class interaction.

Uploaded 25-10-13

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford CS547 HCI Seminar | Spring 2026 | Promoting Agency in Human-AI Interaction

Tue, 27 Oct 2026 08:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

May 29, 2026
This lecture covers:
• LLM-based physical activity coaching
• How LLMs can elicit and reason about qualitative context to provide non-prescriptive and agency-promoting support
• A reinforcement learning algorithm to enable LLM agents to explicitly reason about uncertainty in a user's goals

Uploaded 22-07-26

To follow along with the seminar schedule, visit: https://hci.stanford.edu/

Matthew Jörke is a PhD candidate in Computer Science at Stanford, co-advised by James Landay and Emma Brunskill.

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Stanford CS234 Reinforcement Learning I Q learning and Function Approximation I 2024 I Lecture 4

Tue, 27 Oct 2026 07:00:00 -0000

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Stanford Seminar - Richard Dasher, Stanford University

Tue, 27 Oct 2026 06:00:00 -0000

"Disruptive Ideas, Open Innovation and New Value Chains: Trends in Asia" -Richard Dasher, Stanford University

Topics in International Technology Management: "Green Technologies in Transportation: Recent Developments from Asia." In this seminar series, learn about technology and business trends, innovations, opportunities in Asia and Japan, and implications for US firms and researchers.Topics Include: new materials, fuels, and energy storage for vehicles; automobile and aircraft design; smart grids and intelligent transportation systems; mobile mesh networks

Uploaded 25-10-13

Learn more: http://stanford.io/TYpXeK

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Stanford CS329A Self-Improving AI Agents | Part 3 | Robust Verification

Tue, 27 Oct 2026 05:32:00 -0000

Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education course: https://stanford.io/4zOyjPN
- XCS329 graduate course: https://online.stanford.edu/courses/cs329a-self-improving-ai-agents

Uploaded 03-08-26

A similar curriculum is covered in XCS329z https://online.stanford.edu/courses/cs329z-engineering-ai-agents

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA

Video Summary:
This lecture recording from Stanford's CS329A, Self-Improving AI Agents, taught by Azalia Mirhoseini on September 29, 2025, traces the evolution of verification methods for large language model outputs across four research papers. It covers OpenAI's "Training Verifiers to Solve Math Word Problems," which introduced the GSM8K dataset and outcome-based reward models, and "Let's Verify Step by Step," which compares outcome-supervised and process-supervised reward models using the PRM800K dataset of human-labeled reasoning steps. The lecture also covers Math-Shepherd, which automates step-level annotation without human labels, and Weaver, a Stanford paper that combines ensembles of weak verifiers, including reward models and LLM judges, to close the generation-verification gap. Topics include majority voting and self-consistency baselines, credit assignment in process versus outcome supervision, reward hacking, and using trained verifiers as reward signals for reinforcement learning fine-tuning.

Speaker Bio:
Azalia Mirhoseini
Assistant Professor of Computer Science, Stanford University

Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Research Grant, the Google ML and Systems Junior Faculty Award, MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including WSJ, NYT, Forbes, MIT Technology Review, IEEE Spectrum, WIRED, and TechCrunch.

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Stanford CS234 Reinforcement Learning I Policy Evaluation I 2024 I Lecture 3

Tue, 27 Oct 2026 05:00:00 -0000

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Stanford Seminar - New Platforms for Development Solutions

Tue, 27 Oct 2026 04:00:00 -0000

"New Platforms for Development Solutions," Topics in International Technology Management -Andy Bartley, Algorithms.io and Divyabh Mishra, CrowdANALYTIX

Uploaded 28-10-13

In this seminar series, examine the impact of new technologies on industry value chains. Focus is placed on Silicon Valley-Asia connections and on the processes of open innovation that are co-evolving with the new value chains. Lectures feature distinguished speakers from industry and government.

Learn more: http://stanford.io/TYpXeK

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Student Testimonial - Innovation Masters Series - Angelica

Tue, 27 Oct 2026 03:00:00 -0000

Meet Angelica. She and a team from her company participated in the Innovation Masters Series course, Design Thinking and the Art of Innovation, at Stanford University. In this short interview, she shares her experience with the design thinking program.

Uploaded 14-08-12

The Innovation Masters Series - http://scpd.stanford.edu/design/
For three days, Stanford opens up its design labs and classrooms to executives, business leaders, and decision makers faced with the daunting task of retooling and revitalizing their enterprises.

Senior faculty will lead you through hands-on workshops where you will learn the problem-solving tools and problem-finding frameworks that lead to innovation and strategic leadership, pioneered by the Design Group and the d.school at Stanford.

Next Session - December 12-14, 2012
Register today! http://scpd.stanford.edu/search/publicCourseSearchDetails.do?method=load&courseId=6085755

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Stanford CS234 Reinforcement Learning I Tabular MDP Planning I 2024 I Lecture 2

Tue, 27 Oct 2026 02:00:00 -0000

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Program Overview - Stanford Innovation and Entrepreneurship Certificate

Tue, 27 Oct 2026 01:32:00 -0000

http://create.stanford.edu/ Learn how this new online certificate program can help you think differently and develo innovative products and services. Taught by world-renowned faculty from the Graduate School of Business and the School of Engineering, the Stanford Innovation and Entrepreneurship Certificate is a self-paced interactive, online program where participants will gain essential skills and effective strategies to generate and implement new ideas. Whether you work at a start-up or a large organization, you'll get valuable hands on experience with everything from marketing innovation to prototyping to customer behavior.
Learn more at http://create.stanford.edu/

Uploaded 09-09-13

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Stanford Seminar - Big Data as Both A Window and A Mirror

Tue, 27 Oct 2026 01:00:00 -0000

Dan Cosley
Cornell University

This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 28-10-13

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford CS234 Reinforcement Learning I Introduction to Reinforcement Learning I 2024 I Lecture 1

Tue, 27 Oct 2026 00:00:00 -0000

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Stanford Seminar - Enviornmental Engineering and Water Quality

Mon, 26 Oct 2026 23:00:00 -0000

"Environmental Engineering and Water Quality" -Dick Luthy, Stanford University

This seminar series equips students and professionals with tools to apply the engineering mindset to problems that stem from climate change, so that they may consider and evaluate possible interventional, remedial and adaptive approaches. This is not a crash course on climate change (established climate experts are better equipped for that), nor is it a crash course in policy; instead the series focuses on discovering and exploring climate problems that seem most likely to benefit from adding the engineering mindset as solutions are considered.

Uploaded 28-10-13

Learn more: http://stanford.io/19O05eN

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Stanford MSandE435 Economics of the AI Supercycle | Spring 2026 | Economics of Generative AI

Mon, 26 Oct 2026 22:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Follow along with the course schedule: https://mse435.stanford.edu/

Instructor: Apoorv Agrawal is a Stanford Adjunct Lecturer in Management Science and Engineering and a partner at Altimeter Capital.

Uploaded 17-07-26

Playlist: https://youtube.com/playlist?list=PLoROMvodv4rOAe_DtQYOCKy6_x3irEYV1&si=hI20BgdZqMFtAKS-

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Stanford CS329A Self-Improving AI Agents | Part 6 | Train Time Scaling/Scaling RL

Mon, 26 Oct 2026 21:32:00 -0000

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Stanford Seminar - Open-world Segmentation and Tracking in 3D

Mon, 26 Oct 2026 21:00:00 -0000

October 11, 2024
Laura Leal-Taixé, NVIDIA

In this talk, I will discuss how to train models for detection and segmentation of 3D data, e.g., Lidar, without having to actually annotate data in the 3D domain. I will discuss different uses of pseudo-labels and present a demo for our Segment Anything in Lidar.

Uploaded 28-10-24

About the speaker: https://dvl.in.tum.de/team/lealtaixe/

More about the course can be found here: https://stanfordasl.github.io/robotics_seminar/

View the entire AA289 Stanford Robotics and Autonomous Systems Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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Stanford Webinar: How to Bring Empathy and Experimentation into Your Work Today

Mon, 26 Oct 2026 20:00:00 -0000

In this webinar, you will learn how to bring some foundational design thinking techniques into a current work project. Stanford professors Perry Klebahn and Jeremy Utley combine case study, instruction, and live-fire, real-time working activities in this session to advance an active project in your work and/or life.

Uploaded 31-10-13

Learn more about the Stanford Innovation and Entrepreneurship certificate: http://create.stanford.edu/

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Celebrating 70 years: Stanford Honors Cooperative Program

Mon, 26 Oct 2026 19:00:00 -0000

For 70 years, the School of Engineering has been seeding innovation through an education exchange between academic thought leaders and industry professionals. Stanford’s Honors Cooperative Program (HCP) provides working professionals worldwide the opportunity to pursue a master’s degree on a part-time basis. HCP students can attend many classes online through live streams from their on-campus classrooms or watching the recordings on-demand.

Uploaded 28-10-24

Learn more: https://online.stanford.edu/hcpalumni/hcp-history

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Stanford Seminar - Entrepreneurial Though Leaders: Cyriac Roeding of Shopkick

Mon, 26 Oct 2026 18:00:00 -0000

Cyriac Roeding
Shopkick

In this seminar, entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. Speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks are followed by a half hour of class interaction.

Uploaded 05-11-13

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Webinar: How a Stanford Engineering Professor Engineered a Startup

Mon, 26 Oct 2026 17:32:00 -0000

In this webinar, Professor Bob Sutton and special guest Amin Saberi, Founder and CEO of NovoEd and Associate Professor of Engineering at Stanford discuss the creation of a new learning platform and the delivery of education's digital future.

Uploaded 27-09-13

Learn about Professor Sutton and Professor Saberi's experience with startup launches and online education.

Learn more at: http://create.stanford.edu/

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Stanford Course: Marketing Innovation

Mon, 26 Oct 2026 17:00:00 -0000

Preview the online course: Marketing Innovation (XINE254)
More info: http://create.stanford.edu/courses/marketing-innovation.php

Stanford instructors presents a brief overview of Marketing Innovation, a new online professional course in the Stanford Innovation and Entrepreneurship certifiate.

Uploaded 14-08-12

Taking a product to market without a thorough understanding of your target market can dramatically affect consumer adoption. This course will help you maximize the success of your product, service, or cause with hands-on exercises and real-world applications. Learn the framework and marketing tools that thousands of innovators have used to successfully take a wide variety of products and services to market. This course focuses on early stage innovations and developing a cohesive strategy for implementing marketing throughout product development. In the process you will analyze real products from around the world and create new market plans for your own product or services.

Learn more about the Stanford Innovation and Entrepreneurship certificate: http://create.stanford.edu/

You will learn:

* How to identify and understand your target market
* How market research can work in partnership with R and D
* How to create clear differentiated positioning
* The challenges and opportunities of bringing radical innovation to market
* How to measure marketing success and turn data into action

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Stanford Seminar - Accounting for Human Engagement Behavior to Enhance AI-Assisted Decision Making

Mon, 26 Oct 2026 16:00:00 -0000

October 4, 2024
Ming Yin, Purdue University

Artificial intelligence (AI) technologies have been increasingly integrated into human workflows. For example, the usage of AI-based decision aids in human decision-making processes has resulted in a new paradigm of AI-assisted decision making---that is, the AI-based decision aid provides a decision recommendation to the human decision makers, while humans make the final decision. The increasing prevalence of human-AI collaborative decision making highlights the need to understand how humans engage with the AI-based decision aids in these decision-making processes, and how to promote the effectiveness of the human-AI team in decision making. In this talk, I'll discuss a few examples illustrating that when AI is used to assist humans---both an individual decision maker or a group of decision makers---in decision making, people's engagement with the AI assistance is largely subject to their heuristics and biases, rather than careful deliberation of the respective strengths and limitations of AI and themselves. I'll then describe how to enhance AI-assisted decision making by accounting for human engagement behavior in the designs of AI-based decision aids. For example, AI recommendations can be presented to decision makers in a way that promotes their appropriate reliance on AI by leveraging or mitigating human biases, informed by the analysis of human competence in decision making. Alternatively, AI-assisted decision making can be improved by developing AI models that can anticipate and adapt to the engagement behavior of human decision makers.

Uploaded 21-10-24

About the speaker:
Ming Yin is an Assistant Professor in the Department of Computer Science, Purdue University. Her current research interests include human-AI interaction, crowdsourcing and human computation, and computational social sciences. Before joining Purdue, Ming was a postdoctoral researcher at Microsoft Research, New York City. Ming completed her Ph.D. in Computer Science at Harvard University and received her bachelor's degree from Tsinghua University. Ming is a recipient of the NSF CAREER award, and she was the Conference Co-Chair of the AAAI Conference on Human Computation and Crowdsourcing (HCOMP) in 2022. Her work was recognized with multiple best paper and honorable mention awards at CHI, CSCW, and HCOMP, and was covered by ACM Ubiquity, New Scientist, World Economic Forum, and more.

More about the course can be found here: https://hci.stanford.edu/seminar/

View the entire CS547 Stanford Human-Computer Interaction Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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Stanford Seminar - Nanoelectronics and Its Impact on Open Innovation in Japan

Mon, 26 Oct 2026 15:00:00 -0000

"Nanoelectronics and Its Impact on Open Innovation in Japan" -Shigeo Okaya, Tsukuba Innovation Arena

Topics in International Technology Management: "New Value Chains and the Rise of Open Innovation in Asia." This series examines the impact of new technologies on industry value chains. Focus is placed on Silicon Valley-Asia connections and on the processes of open innovation that are co-evolving with the new value chains. Lectures feature distinguished speakers from industry and government.

Uploaded 05-11-13

Learn more: http://stanford.io/TYpXeK

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Stanford Webinar - How AI is Changing Coding and Education, Andrew Ng and Mehran Sahami

Mon, 26 Oct 2026 14:00:00 -0000

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai

This engaging fireside chat brings together two leading Stanford experts, Andrew Ng and Mehran Sahami, for an illuminating conversation on how AI is reshaping both coding and education. Their thought-provoking insights reveal the transformative impact of AI on the future of technology and learning.

Uploaded 16-10-24

From the rich history of these disciplines to the latest breakthroughs fueled by generative AI, they offer sharp insights into what’s coming next in technology and learning, and how it will reshape the world around us.

Topics include...
- How generative AI is empowering faster development for experienced developers while simplifying coding for novices, making advanced software creation more accessible.
- Why learning to code and use generative AI will become vital for professionals across various fields, not just software engineers
- How educational institutions are integrating generative AI tools into computer science curricula to ensure students are proficient with these emerging technologies from the outset.
-The importance of equipping students to recognize the broader societal implications of the technologies they develop by embedding considerations of fairness, privacy, and decision-making into technical courses

View the Machine Learning Specialization: https://online.stanford.edu/courses/soe-ymls-machine-learning-specialization

Browse all Stanford Online AI Programs: https://online.stanford.edu/artificial-intelligence/courses-and-programs

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Stanford CS329A Self-Improving AI Agents | Part 7 | Self-Improvement and Deep Research Agents

Mon, 26 Oct 2026 13:32:00 -0000

Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education course: https://stanford.io/4zOyjPN
- XCS329 graduate course: https://online.stanford.edu/courses/cs329a-self-improving-ai-agents

Uploaded 03-08-26

A similar curriculum is covered in XCS329z https://online.stanford.edu/courses/cs329z-engineering-ai-agents

A similar curriculum is covered in XCS329z https://online.stanford.edu/courses/cs329z-engineering-ai-agents

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA

Video summary:
This lecture video from Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery on October 17, 2025, examines self-improvement through search. AlphaCode pretrains a masked language model on GitHub and CodeContests data and generates large numbers of samples before clustering and selecting a final answer, while AlphaCode2 fine-tunes Gemini Pro with a learned scoring model, reaching an 85th percentile ranking on competitive programming contests. The lecture explains how solve rate scales with sample budget and where selection and clustering become bottlenecks even after large-scale sample generation. It then introduces Search-O1, a method that triggers search queries when a reasoning model expresses uncertainty and reasons over retrieved documents, which outperforms standard and agentic retrieval-augmented generation on GPQA and multi-hop question-answering benchmarks including HotpotQA and Bamboogle. The session closes by comparing Search-O1's prompting-based approach to Search-R1's reinforcement-learning-based approach for teaching models when to search.

Speaker Bio:
Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University

Dr. Aakanksha Chowdhery is pushing the frontier of agentic LLMs, focusing on recursive self-improvement and long-horizon agents that learn and deploy in the real world. She is one of the few researchers globally who has led frontier model training end-to-end, across both dense and mixture-of-experts (MoE) architectures. At Google, she led the 540B PaLM model, the largest densely trained language model in the world at the time. She subsequently drove pre-training and scaling of Gemini's MoE models across multiple generations, and contributed key components to PaLM-E, Med-PaLM, and the Pathways infrastructure underpinning Google's large-model efforts. She went on to build and lead pretraining teams for open intelligence efforts at Reflection and Meta. Earlier, she held research roles at Microsoft Research and Princeton. At Stanford, where she earned her PhD, she teaches CS329A (Self-Improving AI Agents) and serves as Program Chair for MLSys 2026.

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Stanford Seminar - Entrepreneurial Though Leaders: Steve Teig of Tabula

Mon, 26 Oct 2026 13:00:00 -0000

Steve Teig
Tabula

In this seminar, entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. Speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks are followed by a half hour of class interaction.

Uploaded 05-11-13

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford MSandE435 Economics of the AI Supercycle | Spring 2026 | Applications, AI in Life Sciences

Mon, 26 Oct 2026 12:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Follow along with Stanford's MS and E435 course schedule: https://mse435.stanford.edu/

Guest Speaker: Eric Kauderer-Abrams, Head of Life Sciences, Anthropic

Instructor: Apoorv Agrawal is a Stanford Adjunct Lecturer in Management Science and Engineering and a partner at Altimeter Capital.

Uploaded 17-07-26

Playlist: https://youtube.com/playlist?list=PLoROMvodv4rOAe_DtQYOCKy6_x3irEYV1&si=hI20BgdZqMFtAKS-

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Stanford CME295 Transformers and LLMs | Autumn 2025 | Lecture 6 - LLM Reasoning

Mon, 26 Oct 2026 11:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

November 7, 2025
This lecture covers:
• Reasoning models
• RL for reasoning
• GRPO
• Scaling

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:12:43 Reasoning models
00:27:49 Benchmarks
00:32:04 Pass@k metric
00:48:07 Scaling with RL
00:57:44 GRPO
01:06:03 Comparison between GRPO and PPO
01:16:14 Length bias
01:25:00 DAPO, Dr. GRPO
01:29:38 DeepSeek R1 recipe

Uploaded 14-11-25

Afshine Amidi is an Adjunct Lecturer at Stanford University.

Shervine Amidi is an Adjunct Lecturer at Stanford University.

View the course playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rOCXd21gf0CF4xr35yINeOy

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Stanford Webinar - Forecasting Innovation: What Drives the Future

Mon, 26 Oct 2026 10:00:00 -0000

In this Innovation Masters Series webinar, look at one element of forecasting - determining the "forces" that drive innovation. Join Bill Burnett, consulting assistant professor of Mechanical Engineering and executive director of the Stanford Design Program, and Cynthia Benjamin, Mechanical Engineering and Design lecturer, as they discuss concepts and exercises that will be featured part of the Design Masters program in December 2013.

Uploaded 06-11-13

Learn more: http://scpd.stanford.edu/design/

0:00 Introduction
0:32 Design Thinking started at Stanford
1:44 Design Thinking is a process
2:44 Design Thinking stands for...
4:46 Forecasting and Design Thinking
6:43 Why should innovators think about the future?
8:43 Innovation Forecasting Overview
11:42 Central Question
20:54 Using Design Tools for Insight: Idea Maps
22:43 Using Design Tools for Insight: Brainstorming
25:18 Great, we've got a long list of drivers. Now what?
31:26 Robotics Example: Impact/Uncertainty Priorities
35:49 Next Step: Structure possible outcomes given your key drivers

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Stanford Webinar - How to be an Innovative Leader: A Jam Session with Stanford Faculty

Mon, 26 Oct 2026 09:32:00 -0000

In today's business world, a good leader is innovative and a great leader is a good leader with an innovative team. So, how do you become a great leader? How do you find the balance between ownership and mentoring, build a culture of creative collaboration, and continue to develop your own skills while leading others? In this on-demand webinar, sit in on Stanford's Bob Sutton, Jeremy Utley, and Perry Klebahn for a thought-provoking conversation about the powerful relationship between leadership and innovation. Learn how to apply the principles of d.Thinking (design thinking) to leadership. D.Thinking is a methodology for innovation that combines creative and analytical approaches, and requires collaboration and adaptability. D.Leadership will empower you and your team to adapt to culture challenges, better serve the needs of your stakeholders, and effect change in your organization.

Uploaded 30-09-13

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Stanford CME295 Transformers and LLMs | Autumn 2025 | Lecture 5 - LLM tuning

Mon, 26 Oct 2026 09:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

October 31, 2025
This lecture covers:
• Preference tuning
• RLHF overview
• Reward modeling
• RL approaches (PPO and variants)
• DPO

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:04:50 Preference tuning
00:11:31 Data collection
00:17:43 RLHF overview
00:28:24 Reward model
00:29:46 Bradley-Terry formulation
00:46:02 Reinforcement learning
00:53:54 PPO
00:57:56 Advantage, value function
01:03:39 PPO variants: Clip, KL-penalty
01:16:20 Challenges
01:19:58 On-policy vs off-policy
01:22:43 Best-of-N
01:29:47 DPO

Uploaded 14-11-25

Afshine Amidi is an Adjunct Lecturer at Stanford University.

Shervine Amidi is an Adjunct Lecturer at Stanford University.

View the course playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rOCXd21gf0CF4xr35yINeOy

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Stanford Seminar - Fieldwork: Digital Art as Inquiry in the Open Studio

Mon, 26 Oct 2026 08:00:00 -0000

Marc Downie
OpenEndedGroup

This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 11-11-13

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford At Your Fingertips

Mon, 26 Oct 2026 07:00:00 -0000

http://scpd.stanford.edu
The Stanford Center for Professional Development connects professionals worldwide to the research and teaching of Stanford University faculty in the School of Engineering and related academic departments. Qualified individuals may study for master of science degrees on a part-time basis, pursue graduate certificates and professional certificates, take individual graduate courses and professional courses, participate in workshops, view free online seminars and more. Courses are delivered online, on the Stanford campus in the heart of Silicon Valley, and at the work site.

Uploaded 14-08-12

Graduate Courses - http://scpd.stanford.edu/coursesSeminars/gradCourses.jsp
For a 10-week quarter, become a student in a Stanford University graduate class. You can earn academic credit and apply it toward a certificate or degree. If you do not have the time to complete a course for credit, you can audit the course lectures to access valuable content at an affordable rate. Most courses are offered online, according to the university's academic calendar.

Professional Courses - http://scpd.stanford.edu/coursesSeminars/profCourses.jsp
Are you ready to excel in your career? Our short, intensive courses help you develop skills in innovation, decision-making, strategy, execution, and technology leadership. Courses are offered online, at Stanford, and can be delivered to a group at your work place.

Seminars and Webinars - http://scpd.stanford.edu/coursesSeminars/seminarsAndWebinars.jsp
Tap into groundbreaking research and discovery at Stanford with seminars and webinars given by Stanford faculty, senior researchers, and industry experts. You can view all seminars and webinars online for free. You can also enroll in a seminar for 1 unit of credit if you need it to complete a degree or certificate.

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Stanford CS193p: iOS Development with SwiftUI | 2025 | L3: Model and UI and Swift Type System

Mon, 26 Oct 2026 06:00:00 -0000

View course details: https://online.stanford.edu/courses/cs193p-ios-application-development

All course materials are available at https://cs193p.stanford.edu.

The 3rd lecture of the 2025 version of Stanford's CS193p course (developing applications for iOS using SwiftUI). This lecture focuses on Swift's type system fundamentals and explains the Model-UI separation principle in SwiftUI, emphasizing that the Model contains the app's core logic and data (UI-independent) while the UI is a declarative, reactive visual manifestation of the Model. Covers the Swift type system, including: structs vs classes (value types vs reference types). Shows how enums can have associated data; generics for type-agnostic programming (like Array); how Optionals work; failable initializers that return Optionals; and extensions for adding functionality to existing types. Emphasizes Swift's strong typing, immutability by default, and the importance of clearly marking sources of truth with @State. All concepts are presented as building blocks for the CodeBreaker app development in the next lecture.

Uploaded 13-11-25

Paul Hegarty is a Lecturer who has been teaching CS193p at Stanford since 2010.

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Stanford CS329A Self-Improving AI Agents | Part 5 | Planning and Multi-Step Reasoning

Mon, 26 Oct 2026 05:32:00 -0000

Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education course: https://stanford.io/4zOyjPN
- XCS329 graduate course: https://online.stanford.edu/courses/cs329a-self-improving-ai-agents

Uploaded 03-08-26

A similar curriculum is covered in XCS329z https://online.stanford.edu/courses/cs329z-engineering-ai-agents

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA

Video Summary:
This lecture video from Stanford's CS329A, Self-Improving AI Agents, taught by Azalia Mirhoseini on October 6, 2025, covers three papers on planning and multi-step reasoning in language model agents. LATS, or Language Agent Tree Search, combines reasoning, acting, and search using Monte Carlo Tree Search with LLM-judge and self-consistency scoring, tested on HotpotQA and WebShop. SPRINT fine-tunes reasoning models such as DeepSeek-R1 to generate independent plans for parallel execution, reducing sequential token count while improving accuracy on math, Countdown, and GPQA Diamond benchmarks. SWiRL generates offline synthetic multi-step tool-use trajectories scored by an LLM judge and trains models through multi-step reinforcement learning without executing tools during training, showing generalization across HotpotQA and GSM8K. The lecture addresses trade-offs including inference cost, irreversible actions, and the comparative effect of process-filtered versus outcome-filtered training data.

Speaker Bio:
Azalia Mirhoseini
Assistant Professor of Computer Science, Stanford University

Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Research Grant, the Google ML and Systems Junior Faculty Award, MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including WSJ, NYT, Forbes, MIT Technology Review, IEEE Spectrum, WIRED, and TechCrunch.

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Stanford Seminar - Ki Hyun Jung of SK Planet

Mon, 26 Oct 2026 05:00:00 -0000

"Open Innovation in Korea: Perspectives from SK Planet" -Ki Hyun Jung, SK Planet

Topics in International Technology Management: "New Value Chains and the Rise of Open Innovation in Asia." This series examines the impact of new technologies on industry value chains. Focus is placed on Silicon Valley-Asia connections and on the processes of open innovation that are co-evolving with the new value chains. Lectures feature distinguished speakers from industry and government.

Uploaded 11-11-13

Learn more: http://stanford.io/TYpXeK

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Stanford CS193p: iOS Development with SwiftUI | 2025 | L2: Code Breaker App

Mon, 26 Oct 2026 04:00:00 -0000

View course details: https://online.stanford.edu/courses/cs193p-ios-application-development

All course materials are available at https://cs193p.stanford.edu.

The 2nd lecture of the 2025 version of Stanford's CS193p course (developing applications for iOS using SwiftUI). Continues explanation of SwiftUI development by introducing View modifiers, the second major pillar of UI building, alongside Views. Demonstrates how View modifiers work as chainable functions that return modified Views, showing examples like .font(), .foregroundStyle(), and .padding(). Explains how modifiers are applied in order and can be passed down from container Views to their children. The lecture then dives into building the CodeBreaker app, creating and layout out the game's pegs, then building the markers using Shapes with modifiers like .fill(), .stroke(), and .opacity(). Covers creating custom structs that behave like Views, moving Views to separate files, using enums (for match states like exact, inexact, nomatch), and introduces functional programming concepts like count(where:) for Array processing. The lecture emphasizes decomposition as crucial for code maintainability and compiler performance.

Uploaded 13-11-25

Paul Hegarty is a Lecturer who has been teaching CS193p at Stanford since 2010.

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Stanford Seminar - The Solar Industry from the Inside

Mon, 26 Oct 2026 03:00:00 -0000

"The Solar Industry from the Inside" -Nasreen Chopra, Alta Devices

This seminar series equips students and professionals with tools to apply the engineering mindset to problems that stem from climate change, so that they may consider and evaluate possible interventional, remedial and adaptive approaches. The series focuses on discovering and exploring climate problems that seem most likely to benefit from adding the engineering mindset as solutions are considered.

Uploaded 14-11-13

Learn More - http://stanford.io/TuS1c8

0:00 Introduction
0:25 Calibrating to Solar Growth
1:50 Module Price Trend
8:27 Solar Cell Operation
8:47 Conventional Solar Cell Technology
9:18 Conventional Solar Cell Loss Mechanisms
15:07 Major Solar Technologies
18:07 Solar Power Applications
21:27 Mobile Power Applications
21:34 Mobile Power for UAV Applications
33:51 What is the impact of manufacturing?
36:33 Pillars of Manufacturing
39:50 US Energy Mix

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Stanford CS547 HCI Seminar | Spring 2026 | Just-in-Time Objectives for Specialized AI Interactions

Mon, 26 Oct 2026 02:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

May 22, 2026
This lecture covers:
• How just-in-time (JIT) architectures can produce specialized AI interactions that enable users to redirect generic AI systems to realize their specific, distinctive goals
• An introduction to JIT objectives, which specialize outputs to a particular user by inducing user objectives from observed interaction traces
• How JIT objectives enable on-the-fly generation of software tools that visualize a research statement's logical argument, test alternate color palettes for a figure, or provide feedback based on relevant academic experts

Uploaded 13-07-26

To follow along with the seminar schedule, visit: https://hci.stanford.edu/

Michelle Lam is a Computer Science PhD candidate at Stanford University in the Human-Computer Interaction Group.

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Stanford Seminar - Magical Thinking: Fear, Wonder and Technology

Mon, 26 Oct 2026 01:32:00 -0000

Genevieve Bell
Intel

This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 11-10-13

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

0:00 Introduction
0:58 Introductions
3:33 Charting the socio-technical imagination
8:32 Capturing time
12:49 Je pense donc je suis
16:19 Winding up the world
22:10 Breaking the Frames
29:07 Electricity gives Life
32:53 Getting to abstraction
40:20 Ingenious Mechanical Devices
42:34 Machinery for grace
44:54 Robots are our friends
48:51 The Electric Fairies
52:20 Eternal Sunshine

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Stanford CS193p: iOS Development with SwiftUI | 2025 | L1: Intro to Xcode and SwiftUI

Mon, 26 Oct 2026 01:00:00 -0000

View course details: https://online.stanford.edu/courses/cs193p-ios-application-development

All course materials are available at https://cs193p.stanford.edu.

The first lecture of the 2025 version of Stanford's CS193p course (developing applications for iOS using SwiftUI). Instructor Paul Hegarty provides a course overview, prerequisites (comfort with writing lots of code, structured programming experience), and explains the course structure, which uses a "narrative approach" where students build one large app (CodeBreaker) over 6-7 weeks rather than learning through small code snippets. He gives a comprehensive tour of Xcode's interface and introduces the fundamentals of SwiftUI Views using a Lego analogy, comparing basic Views (Text, Image, Circle) to Lego bricks, composed Views (ContentView) to constructed Legos, container Views (VStack) to instruction manuals, and collections of Views to "bags of Lego." The lecture also covers Swift syntax basics, including structs, variables, and the concept of "some View" as a computed property.

Uploaded 13-11-25

Paul Hegarty is a Lecturer who has been teaching CS193p at Stanford since 2010.

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Stanford Webinar - AI Safety

Mon, 26 Oct 2026 00:00:00 -0000

View course details:
https://online.stanford.edu/courses/aa228v-validation-safety-critical-systems

When autonomous systems make high-stakes decisions, how do we know they're safe?

Uploaded 13-11-25

Join Sydney Katz, a postdoctoral researcher in the Stanford Intelligent Systems Laboratory, for an overview of the methods used to build safety cases for AI-powered decision-making systems operating in high-stakes environments. She will cover validation approaches applicable across multiple sectors, including aviation, autonomous driving, and finance.

You'll discover how safety engineers and AI developers establish trust in complex systems that rely on machine learning—systems where traditional testing alone may not be sufficient. Whether you're interested in developing autonomous technologies, implementing AI in regulated industries, or making decisions about deploying these systems, this webinar will help you understand the importance of safety validation methods.

Sydney Katz, PhD
Sydney Katz is a postdoctoral researcher at Stanford's Intelligent Systems Lab. Her research focuses on the design and validation of safety-critical decision-making systems and builds upon ideas from optimization, Bayesian inference, uncertainty quantification, neural network verification, and reachability analysis.

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EngX: Big Data, Big Impact mini-conference, Russ Altman, Jure Leskovec and Christopher Ré

Sun, 25 Oct 2026 23:00:00 -0000

In this first EngX School of Engineering mini-conference, Stanford engineering professors Russ Altman, Jure Leskovec and Christopher Ré explain how they are using data to prevent unwanted drug interactions, better understand human behavior and shed light on climate change.

Uploaded 14-11-13

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Camera Models and Calibration

Sun, 25 Oct 2026 22:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 9 — Camera Models and Camera Calibration

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 15-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Stanford CS329A Self-Improving AI Agents | Part 9 | Future Research Areas

Sun, 25 Oct 2026 21:32:00 -0000

Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education program: https://stanford.io/4zOyjPN
- XCS329 graduate course: https://online.stanford.edu/courses/cs329a-self-improving-ai-agents

Uploaded 03-08-26

A similar curriculum is covered in XCS329z https://online.stanford.edu/courses/cs329z-engineering-ai-agents

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA

Video Summary:
This final lecture video of Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery and Azalia Mirhoseini on December 5, 2025, covers open research directions in self-improving AI agents. Chowdhery presents three papers on bottlenecks in self-improvement loops: Multi-Agent Fine-Tuning, which uses specialized generator and critic agents to produce diverse reasoning chains; Deep Math V2's meta-verification approach for automated proof checking without reference solutions; and Absolute Zero, a method for models to propose and solve their own coding tasks through self-generated reasoning challenges without external data. Mirhoseini then presents research on the intelligence-per-watt metric, showing that local models with 20 billion parameters or fewer can now handle 88.7 percent of real-world chatbot queries, a 5.3 times efficiency gain over two years from combined model and hardware improvements. The lecture closes with open questions on continual learning, test-time scaling infrastructure, and hybrid local-cloud inference, along with a discussion of non-verifiable domains such as chip design and scientific simulation where reward models substitute for slow ground-truth verification.

Speaker Bios:
Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University

Dr. Aakanksha Chowdhery is pushing the frontier of agentic LLMs, focusing on recursive self-improvement and long-horizon agents that learn and deploy in the real world. She is one of the few researchers globally who has led frontier model training end-to-end, across both dense and mixture-of-experts (MoE) architectures. At Google, she led the 540B PaLM model, the largest densely trained language model in the world at the time. She subsequently drove pre-training and scaling of Gemini's MoE models across multiple generations, and contributed key components to PaLM-E, Med-PaLM, and the Pathways infrastructure underpinning Google's large-model efforts. She went on to build and lead pretraining teams for open intelligence efforts at Reflection and Meta. Earlier, she held research roles at Microsoft Research and Princeton. At Stanford, where she earned her PhD, she teaches CS329A (Self-Improving AI Agents) and serves as Program Chair for MLSys 2026.

Azalia Mirhoseini
Assistant Professor of Computer Science, Stanford University

Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Rese

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Entrepreneurship in Engineering and Science-Based Industries

Sun, 25 Oct 2026 21:00:00 -0000

Stanford's Ricardo Levy and Howie Rosen talk about new their energy course, now offered online in the Molecular Engineering of Energy Technologies graduate certificate through the Stanford Center for Professional Development.

Uploaded 23-08-12

This course is for students interested in learning about entrepreneurship in engineering and science intensive industries such as chemical, energy, materials, bioengineering, environmental, clean-tech, pharmaceuticals, medical and biotechnology. Students will gain a good sense of the excitement, complexity, and challenges that define entrepreneurship, especially for companies that require long development times, large investments, integration across a wide range of technical and non-technical disciplines, and the creation and protection of intellectual property. Teaching methods will include lectures, case studies, guest speakers and individual and team projects.

Topics Include:

* Business basics
* Opportunity viability
* Creating a start-up
* Entrepreneurial leadership
* Entrepreneurship as a career

Instructors

Ricardo Levy, Lecturer, Chemical Engineering

Howard Rosen, Lecturer, Chemical Engineering

Learn more about this course: http://scpd.stanford.edu/search/publicCourseSearchDetails.do?method=load&courseId=15519067

Learn more about the new Molecular Engineering of Energy Technologies certificate: http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=15533128

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Stanford CME 295 Transformers and LLMs | Fall 2026 | Lecture 3 - LLM training

Sun, 25 Oct 2026 20:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:03:36 Pretraining
00:10:33 Cross-entropy loss
00:17:15 Data mixtures
00:22:18 Midtraining
00:23:41 Long context training
00:31:47 Supervised finetuning (SFT)
00:40:15 Low-Rank Adaptation (LoRA)
00:46:37 Preference tuning
00:54:44 Bradley-Terry formulation
01:01:49 Reinforcement Learning from Human Feedback (RLHF)
01:05:38 Direct Preference Preference Optimization (DPO)
01:12:08 Reasoning
01:20:56 Group Relative Policy Optimization (GRPO)
01:23:31 On-Policy Distillation (OPD)
01:27:47 End-to-end example

Uploaded 15-10-26

Afshine Amidi is an Adjunct Professor at Stanford University.

Shervine Amidi is an Adjunct Professor at Stanford University.

Follow the playlist: https://youtube.com/playlist?list=PLakGfbKCei80&si=HF2tNywj7nXIQq8X

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Jen-Hsun Huang: Stanford student and Entrepreneur, co-founder and CEO of NVIDIA

Sun, 25 Oct 2026 19:00:00 -0000

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai

Every successful thing needs to be torn down and rebuilt. In more of an intimate conversation than a lecture, Huang relates his experience as an engineer, entrepreneur, and innovator. Find out how he meets the ever-constant challenge of re-invention as co-founder and CEO of NVIDIA.

Uploaded 23-06-11

► Check out our entire catalog of courses and programs: https://online.stanford.edu/explore

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Stanford Seminar - Entrepreneurial Thought Leaders: Justin Rosenstein of Asana

Sun, 25 Oct 2026 18:00:00 -0000

Justin Rosenstein
Asana

In this seminar, entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. Speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks are followed by a half hour of class interaction.

Uploaded 06-06-13

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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William J. Perry on Energy, National Security and Technology

Sun, 25 Oct 2026 17:32:00 -0000

This Stanford engineering hero lecture features former U.S. secretary of defense and Stanford Engineering Hero, William J. Perry, as he shares his thoughts around Energy, National Security and Technology.

Uploaded 11-10-13

William J. Perry was secretary of defense of the United States from February 1994 to January 1997, deputy secretary of defense from 1993 to 1994 and under secretary of defense for research and engineering from 1977 to 1991

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Trajectory Optimization

Sun, 25 Oct 2026 17:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 3 — Trajectory Optimization: Open-Loop Motion Control and Differential Flatness

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 09-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Stanford Seminar - Dynamic Procedure Aids Support - Rapid Assimilation of Information

Sun, 25 Oct 2026 16:00:00 -0000

Jesse Cirimele
Stanford University

This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 06-06-13

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

0:00 Introduction
0:36 Medicine is High Impact
1:03 Emergency Medicine: Designing for
2:02 Medical Errors
2:32 Training for Perfection
9:52 Practical Skills Matter
11:38 Observation and Participatory Design
11:56 Simulation and Checklists
13:09 Checklist Challenges
19:35 Professional Acceptance
23:32 Evaluation
24:13 Methods
25:21 Study Setup
25:30 Scenario, Aid, Distractor
27:20 Measures
28:29 Dynamic Aid Wins
29:53 Narrative Simulation
31:46 Latency Changes Behavior
33:00 Glance times in OR
33:44 5 aids 15 tasks
36:13 Gaze Pattern
36:36 Dynamic Wins
38:13 Appropriate Ventilation Rate During CPR?
39:19 How Many Joules When Shocking (Defibrillating)
42:13 Creating Aids Generatively
43:42 Eye-tracking for design
44:59 Dynamic++
45:34 Outline
45:49 Personal Health Aids
46:24 MILES Project
47:57 Recap
48:29 Main Take-Away
49:25 Thank Yous
49:46 Committee
49:59 Family and Friends

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 15: Imitation Learning

Sun, 25 Oct 2026 15:00:00 -0000

Lecture 15 — Imitation Learning

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_4.pdf

Speaker: Dr. Daniele Gammelli

Bio: Dr. Daniele Gammelli is the Research Director of the Machine Intelligence for Robot Autonomy Laboratory at the Italian Institute of Artificial Intelligence (AI4I), and a Researcher in the Department of Aeronautics and Astronautics at Stanford University. Since 2022, he has been a Research Fellow at the Center for Aerospace Autonomy Research (CAESAR) at Stanford and, until 2025, at the Center for Automotive Research at Stanford (CARS). He received his Ph.D. in Machine Learning and Mathematical Optimization from the Technical University of Denmark (DTU) in 2022, where his doctoral thesis was nominated for the DTU Best Thesis of the Year Award and the DTU Young Researcher Award. Dr. Gammelli’s research focuses on developing the algorithmic foundations and system-level methodologies that enable AI-powered autonomous systems to operate safely, efficiently, and reliably in high-stakes environments, with an emphasis on aerospace autonomy and next-generation mobility systems.

Uploaded 13-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Stanford Seminar - Dan Mihai Dumitriu and Allen Miner

Sun, 25 Oct 2026 14:00:00 -0000

"Midokura and the Changing Environment for Entrepreneurship in Japan" -Dan Mihai Dumitriu, Midokura and Allen Miner, SunBridge Group
This lecture series presented by the US-Japan Technology Management Center and the US-Asia Technology Management Center explores patterns and challenges of entrepreneurship in Asia. A wide range of expert executive guest speakers will discuss the latest exciting new trends in entrepreneurship, start-up companies and investment in major Asian economies. Explore the culture of entrepreneurship in Asian countries and the aggressive change happening in business across this area.

Uploaded 06-06-13

Learn more: http://stanford.io/16vBuLV

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Stanford CS329A Self-Improving AI Agents | Part 8 | Agentic Evaluations and Long Horizon Tasks

Sun, 25 Oct 2026 13:32:00 -0000

Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education course: https://stanford.io/4zOyjPN
- XCS329 graduate course: https://online.stanford.edu/courses/cs329a-self-improving-ai-agents

Uploaded 02-08-26

A similar curriculum is covered in XCS329z https://online.stanford.edu/courses/cs329z-engineering-ai-agents

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA

Video Summary:
This lecture video from Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery on November 17, 2025, covers methods for evaluating AI agents on long-horizon and economically valuable tasks. It reviews METR's time-horizon methodology, which measures the task duration models complete at 50 and 80 percent reliability across the HCAST, SWE-bench, and RE-Bench suites, showing that model capability has roughly doubled every seven months, from seconds for GPT-2 in 2019 to nearly an hour for Claude 3.7 Sonnet in 2025. It also covers GDPval, OpenAI's benchmark that scores model output against work from industry professionals across 44 occupations and nine sectors, where win rates rose from 12.4 percent for GPT-4o to 47.6 percent for Claude Opus 4.1. Stanford's Deep Scholar Bench, also discussed, tests whether models can generate literature review sections for academic papers by scoring knowledge synthesis, retrieval quality, and citation verifiability. The lecture closes by detailing recurring agent failure modes, including poor planning, incorrect tool selection, premature task abandonment, and repetitive action loops.

Speaker Bio:
Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University

Dr. Aakanksha Chowdhery is pushing the frontier of agentic LLMs, focusing on recursive self-improvement and long-horizon agents that learn and deploy in the real world. She is one of the few researchers globally who has led frontier model training end-to-end, across both dense and mixture-of-experts (MoE) architectures. At Google, she led the 540B PaLM model, the largest densely trained language model in the world at the time. She subsequently drove pre-training and scaling of Gemini's MoE models across multiple generations, and contributed key components to PaLM-E, Med-PaLM, and the Pathways infrastructure underpinning Google's large-model efforts. She went on to build and lead pretraining teams for open intelligence efforts at Reflection and Meta. Earlier, she held research roles at Microsoft Research and Princeton. At Stanford, where she earned her PhD, she teaches CS329A (Self-Improving AI Agents) and serves as Program Chair for MLSys 2026.

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Stanford CS547 HCI Seminar | Spring 2026 | Toward Ontological Multiplicity in AI and Computing

Sun, 25 Oct 2026 13:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

May 15, 2026
This lecture covers:
• The recursive relationship between imagination and design
• Ontological boundaries in system design
• The stakes of embedded assumptions in AI
• Moving critique from reaction to prevention
• Methods for expanding design possibilities

Uploaded 13-07-26

To follow along with the seminar schedule, visit: https://hci.stanford.edu/

Nava Haghighi is a doctoral candidate in Computer Science at Stanford University with a focus on human-centered AI.

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Mining Online Data Across Social Networks

Sun, 25 Oct 2026 12:00:00 -0000

Capturing Data, Modeling Patterns, Predicting Behavior. Capturing Data, Modeling Patterns, Predicting Behavior - Based on collecting more than 20 million blog posts and news media articles per day, Professor Jure Leskovec discusses how to mine such data to capture and model temporal patterns in the news over a daily time-scale --in particular, the succession of story lines that evolve and compete for attention. He discusses models to quantify the influence of individual media sites on the popularity of news stories and algorithms for inferring hidden networks of information flow.

Uploaded 18-08-11

Learn more: http://scpd.stanford.edu/

0:00 Introduction
0:08 Meet The Speaker
4:01 Many data is a Network!
5:27 Networks: Rich Social Data
6:27 Networks: Size *matters
7:38 Networks: Structure and Process
9:02 Why study Web and networks?
10:28 Projects: Link Prediction
17:17 Projects: Friends vs. Foes
18:13 Projects: Predicting Enemies
20:26 Theory of Structural Balance
22:25 Theory of Status
24:18 Friends vs. Foes, Trust vs. Distrust
27:55 Online (social) media
29:50 Tracking Information on the Web
32:29 How is news being made?
34:35 Modeling influence
36:41 Influence curves of media types
38:35 Inferring the Diffusion Network
39:26 Inferring networks
42:21 Maximizing the Influence
43:35 Influential blogs and Information outbreaks
44:29 Reflections
46:03 Directions
47:23 References
47:34 Autumn Quarter 2011-12
49:09 Graduate Portfolio
50:04 Course Enrollment
51:06 Please indicate your level of interest in the Mining Massive Datasets graduate certificate
54:22 Stanford Center for Professional Development

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Stanford Seminar - Entrepreneurial Thought Leaders: Jocelyn Goldfein of Facebook

Sun, 25 Oct 2026 11:00:00 -0000

Jocelyn Goldfein
Facebook

In this seminar, entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. Speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks are followed by a half hour of class interaction.

Uploaded 07-06-13

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Overview, Mobile Robot Kinematics

Sun, 25 Oct 2026 10:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 1 — Course Overview, Mobile Robot Kinematics

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 09-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Inspiration, Design, and the Human Element

Sun, 25 Oct 2026 09:32:00 -0000

A Jam Session with Stanford Engineering Faculty. Stanford University rock stars Kurt Beiter and David Beach explore product quality and design! What secrets lie in the mind of the product user? How do we factor non-measurable elements into a successful design? From inspiration to conception to design, how does one develop a truly awesome product?

Uploaded 15-11-11

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How Radical Collaboration Redefined the Automobile -- Stanford Innovation Masters Series

Sun, 25 Oct 2026 09:00:00 -0000

Learn more about design thinking and how Stanford uses it to create new and innovative approaches to solving strategic problems.

Uploaded 05-04-11

Learn more about the Innovation Masters Series: http://scpd.stanford.edu/design/

0:00 Introduction
0:47 Meet the Speakers
2:52 Design Thinking started at Stanford
11:33 Design Thinking explained
14:14 Design Thinking applied
16:23 Dynamic Design Lab
18:26 P1 Steer-by-wire Car
20:24 Our proposal to Nissan
21:27 Insight from P1 design process
22:22 Steer-by-Wire system
25:34 Driver Assistance Systems
28:46 What are we learning?
38:33 Sponsored by SCPD STANFORD CENTER FOR PROFESSIONAL DEVELOPMENT
39:57 Who should attend?
40:41 Key Program Takeaways
42:45 What is your interest level in the Innovation Masters Series Course?
43:42 Innovation Masters Series: Design Thinking and the Art of Innovation

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Stanford Lecture: Donald Knuth—"Why Pi?"(2010)

Sun, 25 Oct 2026 08:00:00 -0000

Don Knuth's 16th Annual Christmas Tree Lecture
December 6th, 2010

Professor Donald Knuth discusses recent discoveries that have uncovered a fascinating relationship between circles and the theory of trees.

Uploaded 06-09-11

Learn more: http://scpd.stanford.edu/knuth/index.jsp

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Live from Stanford Health AI Week

Sun, 25 Oct 2026 07:00:00 -0000

Learn more about Stanford's online Healthcare AI programs: https://online.stanford.edu/artificial-intelligence/ai-professionals-healthcare

Check out the AI in Healthcare series playlist: https://stanford.io/3NEt7uE

Hosts:
Matt Lungren, Stanford University - https://profiles.stanford.edu/matthew-lungren
Justin Norden, Stanford University - https://med.stanford.edu/profiles/justin-norden

Guests:
- Sue Sheridan, President and CEO, Patients for Patient Safety US
- Chris Boerner, Board Chair and CEO, Bristol Myers Squibb
- Kimberly Powell, Vice President of Healthcare, NVIDIA
- Ed Kim (Edward Sanghyun Kim), Physician-in-Chief and Senior Vice President, City of Hope Orange County
- Arati Prabhakar, Former Science and Technology Advisor to President Biden and Director of the White House Office of Science and Technology Policy

Uploaded 09-07-26

Recorded on location at Stanford Medicine’s Health AI Week, Matt Lungren and Justin Norden sit down with leaders from healthcare, life sciences, and industry to separate hype from progress in medical AI. Together, they explore where AI is already improving day-to-day work, from surfacing the right information at the right time to making tools that feel practical for clinicians and staff. The guests also dig into what it will take to prove impact at scale, including how organizations should measure outcomes, manage change, and fund the most promising use cases. The episode closes with a look at how AI could reshape research and clinical trials and what responsible adoption should prioritize.

5 key points:

1. AI’s biggest impact is already showing up at the patient level through improved access to knowledge and information.

2. Measuring impact is the central challenge: how to quantify quality and outcomes improvements at a national scale.

3. Evidence still matters: the “survival curve” moment is powerful, but it must be followed by proof, validation, and deployment planning.

4. Research and clinical trials are poised for change: identifying and enrolling participants and running studies will look different.

5. Workforce and culture are diverging sharply: leaders debate automation vs. job-loss narratives, and organizations are taking very different approaches to adopting AI tools.

Stanford Online, in collaboration with the Stanford Center for Health Education, is brought to you by the Stanford Engineering Center for Global and Online Education.

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Stanford CME295 Transformers and LLMs | Autumn 2026 | Lecture 2 - Large Language Models

Sun, 25 Oct 2026 06:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:04:47 BERT and encoder-only models
00:13:47 GPT and decoder-only models
00:16:10 Encoder-decoder versus decoder-only
00:19:23 Scaling laws
00:26:49 Large Language Model definition
00:29:05 Mixture of experts (MoE)
00:33:11 MoE-based LLMs
00:36:03 Load balancing loss
00:45:36 Position embeddings
01:01:58 Rotary position embeddings (RoPE)
01:09:28 Sliding Window Attention (SWA)
01:13:15 Group Query Attention (GQA)
01:18:13 Pre-Norm versus Post-Norm
01:19:41 LayerNorm versus RMSNorm
01:21:09 Next token prediction
01:35:22 Prompting
01:38:38 Context rot
01:41:42 Chain of Thought (CoT)

Uploaded 09-10-26

Afshine Amidi is an Adjunct Professor at Stanford University.

Shervine Amidi is an Adjunct Professor at Stanford University.

Follow the playlist: https://youtube.com/playlist?list=PLakGfbKCei80&si=HF2tNywj7nXIQq8X

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Stanford CS547 HCI Seminar | Spring 2026 | Show It or Tell It? Text, Visualization, and Combination

Sun, 25 Oct 2026 05:32:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

March 13, 2026
This lecture covers:
• Text, visualization, and their combination
• The role of language in information visualization
• How to decide what to express via language vs via visualization
• How to choose what kind of text to use when creating visualizations, and does that choice matter?
• Does anyone prefer text over visuals, under what circumstances, and why?
• How multimodal large language models figure into this

Uploaded 31-07-26

To follow along with the seminar schedule, visit: https://hci.stanford.edu/

Marti Hearst is a Professor in the UC Berkeley School of Information and the Computer Science Division.

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Stanford Seminar - Building Computers from Bacteriophage, Drew Endy

Sun, 25 Oct 2026 05:00:00 -0000

"Building Computers from Bacteriophage - Data, Communication, Logic with Biological Cells" -Drew Endy, Stanford University
Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Uploaded 07-06-13

Learn more: http://bit.ly/WinYX5

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Young Entrepreneurs for Society Student Testimonial - Innovation Masters Series

Sun, 25 Oct 2026 04:00:00 -0000

Meet Tau Shen, Carmen, and Maggie, from the YES Network (Young Entrepreneurs for Society). Learn how they plan to bring the concepts of design thinking back to their country and apply it to finding and solving social problems in innovative ways.

Uploaded 29-08-12

The Innovation Masters Series - http://scpd.stanford.edu/design/
For three days, Stanford opens up its design labs and classrooms to executives, business leaders, and decision makers faced with the daunting task of retooling and revitalizing their enterprises.

Senior faculty will lead you through hands-on workshops where you will learn the problem-solving tools and problem-finding frameworks that lead to innovation and strategic leadership, pioneered by the Design Group and the d.school at Stanford.

Next Session - December 12-14, 2012
Register today! http://scpd.stanford.edu/search/publicCourseSearchDetails.do?method=load&courseId=6085755

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Stanford Robotics Seminar ENGR319 | Spring 2026 | Towards Trustworthy Autonomy

Sun, 25 Oct 2026 03:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

May 15, 2026
This seminar covers:
• Guardrails to detect, avoid, and learn from failures
• How to detect when a system is unreliable
• How to enact safety interventions when it degrades
• How to improve it through principled use of deployment experience

Uploaded 07-07-26

Follow along with the seminar schedule: https://stanfordasl.github.io/robotics_seminar/

Rohan Sinha is a PhD candidate in the Department of Aeronautics and Astronautics at Stanford University, where he is a member of the Autonomous Systems Lab.

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Stanford CS25: Transformers United V6 I Distinct Modes of Generalization from Parameters and Context

Sun, 25 Oct 2026 02:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

May 7, 2026
This seminar covers:
• Two methods for teaching information to language models: training (updating parameters) or in-context learning (providing information in prompts)
• Striking differences in the types of generalization that models make when they learn information via these two routes
• Three different strategies that can help bridge the gap, based on data augmentation, retrieval, and RL

Uploaded 20-05-26

Follow along with the seminar schedule. Visit: https://web.stanford.edu/class/cs25/

Guest Speaker: Andrew Lampinen (Anthropic)

Instructors:
• Steven Feng, Stanford Computer Science PhD student and NSERC PGS-D scholar
• Karan P. Singh, Electrical Engineering PhD student and NSF Graduate Research Fellow in the Stanford Translational AI Lab
• Michael C. Frank, Benjamin Scott Crocker Professor of Human Biology Director, Symbolic Systems Program
• Christopher Manning, Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science, Co-Founder and Senior Fellow of the Stanford Institute for Human-Centered Artificial Intelligence (HAI)

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Stanford Lecture: Donald Knuth - "Bayesian trees and BDDs" (2011)

Sun, 25 Oct 2026 01:32:00 -0000

December 8th, 2011

Professor Donald Knuth's 17th annual Christmas Tree Lecture. Knuth explains how to apply elementary BDD technology so that the probability of such events (and many others) can be computed in polynomial time.

Uploaded 09-12-11

Learn more: http://scpd.stanford.edu/knuth/index.jsp

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Stanford Seminar - Design Mining the Web, Ranjitha Kumar

Sun, 25 Oct 2026 01:00:00 -0000

Ranjitha Kumar
Stanford University

This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 07-06-13

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 10: Reachibility Analysis

Sun, 25 Oct 2026 00:00:00 -0000

Lecture 10 — Hamilton-Jacobi-Bellman, Hamilton-Jacobi-Isaacs, and Reachibility Analysis

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_10.pdf

Speaker: Prof. Marco Pavone

Bio: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Uploaded 12-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Stanford Seminar - Entrepreneurial Thought Leaders: Kate Mitchell of Scale Venture Partners

Sun, 25 Oct 2026 00:00:00 -0000

Kate Mitchell
Scale Venture Partners

In this seminar, entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. Speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks are followed by a half hour of class interaction.

Uploaded 24-05-13

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Seminar - Chris Redlitz and Beverly Parenti of The Last Mile

Sat, 24 Oct 2026 23:00:00 -0000

Chris Redlitz and Beverly Parenti
The Last Mile

In this seminar, entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. Speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks are followed by a half hour of class interaction.

Uploaded 04-06-13

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford CS153 Frontier Systems | Building the Frontier Ecosystem

Sat, 24 Oct 2026 23:00:00 -0000

For more information about Stanford's online Artificial Intelligence programs, visit: https://stanford.io/ai

Follow along with the course schedule and syllabus, visit: https://cs153.stanford.edu/

In this CS153 session, Microsoft Chairman and CEO Satya Nadella joins Michael Abbott for a wide-ranging conversation on AI, Microsoft's strategy, and the future of computing. Nadella traces Microsoft's investment in OpenAI back to a longstanding obsession with natural language processing, and describes the company's vision for a "frontier ecosystem" in which every company can build and protect its own AI-powered IP through what he calls "hill climbing machines" or reinforcement learning environments trained on proprietary data.

Uploaded 29-06-26

He walks through several announcements from Microsoft's Build conference, including seven new MAI models, the Scout enterprise autopilot agent, a concept of "unmetered intelligence" running on edge devices, and Project Solara's new ambient computing form factors. He also addresses Microsoft's quantum computing program, the Majorana QPU, and the long-term potential of quantum-classical hybrid systems.

In the student Q and A, Nadella reflects on cultivating a growth mindset culture at Microsoft, the importance of broad intellectual curiosity for public communication, and his advice to students to pursue cognitive coverage rather than anxiety-driven productivity.

Satya Nadella is Chairman and Chief Executive Officer of Microsoft. Before being named CEO in February 2014, Nadella held leadership roles in both enterprise and consumer businesses across the company.

Joining Microsoft in 1992, he quickly became known as a leader who could span a breadth of technologies and businesses to transform some of Microsoft’s biggest product offerings.

Most recently, Nadella was executive vice president of Microsoft’s Cloud and Enterprise group. In this role, he led the transformation to the cloud infrastructure and services business, which outperformed the market and took share from competition. Previously, Nadella led R and D for the Online Services Division and was vice president of the Microsoft Business Division. Before joining Microsoft, Nadella was a member of the technology staff at Sun Microsystems.

Originally from Hyderabad, India, Nadella lives in Bellevue, Washington, with his family. He earned a bachelor’s degree in electrical engineering from Mangalore University, a master’s degree in computer science from the University of Wisconsin – Milwaukee and a master’s degree in business administration from the University of Chicago. Nadella serves on the board of trustees to his alma mater the University of Chicago.

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Stanford CS153 Frontier Systems | The AI Native Company: How One Founder Becomes a 1000x Engineer

Sat, 24 Oct 2026 22:00:00 -0000

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 6: Direct Methods

Sat, 24 Oct 2026 22:00:00 -0000

Lecture 6 — Direct methods for Optimal Control

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_6.pdf

Speaker: Dr. Daniele Gammelli

Bio: Dr. Daniele Gammelli is the Research Director of the Machine Intelligence for Robot Autonomy Laboratory at the Italian Institute of Artificial Intelligence (AI4I), and a Researcher in the Department of Aeronautics and Astronautics at Stanford University. Since 2022, he has been a Research Fellow at the Center for Aerospace Autonomy Research (CAESAR) at Stanford and, until 2025, at the Center for Automotive Research at Stanford (CARS). He received his Ph.D. in Machine Learning and Mathematical Optimization from the Technical University of Denmark (DTU) in 2022, where his doctoral thesis was nominated for the DTU Best Thesis of the Year Award and the DTU Young Researcher Award. Dr. Gammelli’s research focuses on developing the algorithmic foundations and system-level methodologies that enable AI-powered autonomous systems to operate safely, efficiently, and reliably in high-stakes environments, with an emphasis on aerospace autonomy and next-generation mobility systems.

Uploaded 12-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 20: GMM (EM), PCA

Sat, 24 Oct 2026 21:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

For information on the graduate course, CS229 Machine Learning, including the syllabus and course materials, visit the course's website: https://cs229.stanford.edu/index.html-spr26

Uploaded 31-07-26

Teaching Team:
Chris Ré, Professor of Computer Science
Tengyu Ma, Assistant Professor of Computer Science

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Stanford Course - The Power of Stories to Fuel Innovation

Sat, 24 Oct 2026 21:00:00 -0000

Preview the online course The Power of Stories to Fuel Innovation (XINE217)
More info: http://create.stanford.edu/courses/power-of-stories.php

A big idea is not enough. You need people to create it and people to buy into it. Your big idea needs a story. Stories fuel innovation. They hold the power to transform listeners; to take listeners on a journey that changes how they think, feel or act. This interactive online course covers the variety of roles a narrative can play, and its potential to transform an organization or new venture. Explore why story is at the heart of effective innovation and how story can be used to transform culture.

Uploaded 10-06-13

Learn How To:

-Understand the role story plays in business, and specifically innovation
-Determine what makes a good (and bad) story
-Use and craft stories for internal and external stakeholders, to further your strategic advantage and spur innovation

Faculty
Jennifer L. Aaker

0:00 Introduction
0:06 Please tell us about your course
0:56 What are some of the topics covered in the course?
1:49 How can students apply this course to their work?
2:26 What do you hope students will take away from this course?

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Stanford CME296 Diffusion and Large Vision Models | Spring 2026 | Lecture 6 - Model Training

Sat, 24 Oct 2026 21:00:00 -0000

Learn more details about this course: https://online.stanford.edu/courses/cme296-diffusion-and-large-vision-models

To follow along with the course schedule and syllabus, visit: https://cme296.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:07:45 Training lifecycle overview
00:10:39 Loss parameterization
00:16:08 Timestep sampling
00:22:27 Logit normal distribution
00:25:44 Sampling shift for different resolutions
00:43:56 Representation alignment (REPA)
00:49:21 Pre-training
00:52:46 Continued training (CT)
00:54:02 Supervised finetuning (SFT)
00:54:39 Preference tuning
00:56:48 Reward feedback learning (ReFL)
01:00:16 Flow-GRPO
01:03:23 Diffusion-DPO
01:05:06 Prompt enhancement (PE)
01:10:00 DreamBooth, low-rank adaptation (LoRA)
01:16:35 Distillation
01:21:42 Progressive distillation (PD)
01:25:14 InstaFlow
01:30:18 Consistency models (CM)
01:34:23 Distribution matching distillation (DMD)
01:39:16 Adversarial diffusion distillation (ADD)

Uploaded 19-05-26

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Afshine Amidi is an Adjunct Lecturer at Stanford University.

Shervine Amidi is an Adjunct Lecturer at Stanford University.

View the course playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rNdy8rt2rZ4T2xM0OjADnfu

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 5: Computational Methods

Sat, 24 Oct 2026 20:00:00 -0000

Lecture 5 — Pontryagin’s Minimum Principle and Computational Methods

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_5.pdf

Speaker: Dr. Daniele Gammelli

Bio: Dr. Daniele Gammelli is the Research Director of the Machine Intelligence for Robot Autonomy Laboratory at the Italian Institute of Artificial Intelligence (AI4I), and a Researcher in the Department of Aeronautics and Astronautics at Stanford University. Since 2022, he has been a Research Fellow at the Center for Aerospace Autonomy Research (CAESAR) at Stanford and, until 2025, at the Center for Automotive Research at Stanford (CARS). He received his Ph.D. in Machine Learning and Mathematical Optimization from the Technical University of Denmark (DTU) in 2022, where his doctoral thesis was nominated for the DTU Best Thesis of the Year Award and the DTU Young Researcher Award. Dr. Gammelli’s research focuses on developing the algorithmic foundations and system-level methodologies that enable AI-powered autonomous systems to operate safely, efficiently, and reliably in high-stakes environments, with an emphasis on aerospace autonomy and next-generation mobility systems.

Uploaded 11-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Stanford Seminar - Beam Yourself Anywhere: The Research and Development of Remote Presence Technology

Sat, 24 Oct 2026 20:00:00 -0000

Josh Tyler
Suitable Technologies

This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 31-05-13

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford MSandE435 Economics of the AI Supercycle | Spring 2026 | Infrasctructure, Enterprise AI, SaaS

Sat, 24 Oct 2026 19:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

This seminar covers applications, coding AI, and the future of software.

Uploaded 13-07-26

Follow along with the schedule: https://mse435.stanford.edu/

Guest Speaker: Ali Ghodsi, Databricks

Instructor: Apoorv Agrawal is a Stanford Adjunct Lecturer in Management Science and Engineering and a partner at Altimeter Capital.

Playlist: https://youtube.com/playlist?list=PLoROMvodv4rOAe_DtQYOCKy6_x3irEYV1&si=hI20BgdZqMFtAKS-

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 9: Stochastic Dyn. Program

Sat, 24 Oct 2026 19:00:00 -0000

Lecture 9 — Stochastic Dynamic Programming, Value Iteration, and Policy Iteration

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_9.pdf

Speaker: Prof. Marco Pavone

Bio: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Uploaded 12-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI"

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Stanford Seminar - Who Owns the Future, Jaron Lanier, Microsoft Research

Sat, 24 Oct 2026 18:00:00 -0000

"Who Owns the Future" -Jaron Lanier, Microsoft Research

Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Uploaded 14-10-13

Learn more: http://bit.ly/WinYX5

Explore all Stanford Online Courses and Programs: https://online.stanford.edu/explore

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Stanford MSandE435 Economics of the AI Supercycle | Spring 2026 | Applications, Coding AI

Sat, 24 Oct 2026 18:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

This seminar covers applications, coding AI, and the future of software.

Uploaded 23-06-26

Follow along with the schedule: https://mse435.stanford.edu/

Guest Speaker: Guillermo Rauch, Vercel

Instructor: Apoorv Agrawal is a Stanford Adjunct Lecturer in Management Science and Engineering and a partner at Altimeter Capital.

Playlist: https://youtube.com/playlist?list=PLoROMvodv4rOAe_DtQYOCKy6_x3irEYV1&si=hI20BgdZqMFtAKS-

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Stanford Seminar - Entrepreneurship in China: Through the Lens of a Venture Capitalist

Sat, 24 Oct 2026 17:32:00 -0000

"Entrepreneurship in China: Through the Lens of a Venture Capitalist" -Kevin Fong, GSR Ventures
This lecture series presented by the US-Japan Technology Management Center and the US-Asia Technology Management Center explores patterns and challenges of entrepreneurship in Asia. A wide range of expert executive guest speakers will discuss the latest exciting new trends in entrepreneurship, start-up companies and investment in major Asian economies. Explore the culture of entrepreneurship in Asian countries and the aggressive change happening in business across this area.

Uploaded 24-05-13

Learn more: http://stanford.io/16vBuLV

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Stanford CS547 HCI Seminar | Spring 2026 | HCI and Human-Centered AI for Digital Health

Sat, 24 Oct 2026 17:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

May 8, 2026
This lecture covers:
• HCI issues related to the design, evaluation, and deployment of AI-enabled systems in digital health
• How "easy to implement" AI engineering decisions that are usually default parameters in Python machine learning code can have dramatic effects on the user experience of patients and clinicians
• How the choice of evaluation metrics to optimize for can affect downstream user experience

Uploaded 20-05-26

To follow along with the seminar schedule, visit: https://hci.stanford.edu/

Dr. Peter Washington, PI of the UCSF TECH Lab (techlab.ucsf.edu), is an Assistant Professor in the Division of Clinical Informatics and Digital Transformation (DoC-IT) in the Department of Medicine at the University of California, San Francisco (UCSF).

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AStanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 8: LQR-Style Algorithms

Sat, 24 Oct 2026 17:00:00 -0000

Lecture 8 — Nonlinearity: Tracking LQR, Iterative LQR, Differential Dynamic Programming

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_8.pdf

Speaker: Prof. Marco Pavone

Bio: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Uploaded 12-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Stanford Seminar - Action for India: Scaling Social Impact through Technology

Sat, 24 Oct 2026 16:00:00 -0000

"Action for India: Scaling Social Impact through Technology" -Sanjay Kadaveru, Action for India and Radhika Shah, Action for India - Silicon Valley Circle
This lecture series presented by the US-Japan Technology Management Center and the US-Asia Technology Management Center explores patterns and challenges of entrepreneurship in Asia. A wide range of expert executive guest speakers will discuss the latest exciting new trends in entrepreneurship, start-up companies and investment in major Asian economies. Explore the culture of entrepreneurship in Asian countries and the aggressive change happening in business across this area.

Uploaded 10-06-13

Learn more: http://stanford.io/16vBuLV

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Stanford Webinar - Design Thinking and the Art of Innovation

Sat, 24 Oct 2026 16:00:00 -0000

In today's business environment, companies are challenged to implement more projects with fewer resources. New products are being rushed to market to stave off collapsing margins, and many markets are bombarded by copy-cat foreign companies and nimble competitors with radically lower cost structures. Success will be measured by your ability to successfully compete - to compete in a world of radical innovation - and win.

Uploaded 13-09-12

A key tool in your toolbox is Design Thinking. This webinar describes Design Thinking as it is practiced at Stanford's d.school and show how it can transform both the way you think about innovation and go to market with new ideas. The d.school is at the forefront of an international trend to integrate design, technology and business practices to train tomorrow's leaders. We will also review the curriculum for the upcoming Innovation Masters Series to help you decide if you should attend.

It is clear that innovation is critical to customers and businesses alike, more so now than ever. This webinar series will give you a taste of what will be taught during the Innovation Masters Series - Design Thinking and the Art of Innovation (XDES400) course being held this December 12- 14, 2012 at Stanford University.

Learn more about the Innovation Masters Series: http://scpd.stanford.edu/design

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Stanford Seminar - Entrepreneurs and Emerging Technology: Intensive Innovation in Asia

Sat, 24 Oct 2026 15:00:00 -0000

"Entrepreneurs and Emerging Technology: Intensive Innovation in Asia" -Lerwen Liu, NanoGlobe Pte Ltd.
This lecture series presented by the US-Japan Technology Management Center and the US-Asia Technology Management Center explores patterns and challenges of entrepreneurship in Asia. A wide range of expert executive guest speakers will discuss the latest exciting new trends in entrepreneurship, start-up companies and investment in major Asian economies. Explore the culture of entrepreneurship in Asian countries and the aggressive change happening in business across this area.

Uploaded 31-05-13

Learn more: http://stanford.io/16vBuLV

0:00 Introduction
2:02 Introduction of Nano Scale
7:11 Benefits of Nanotechnology
8:14 Nanotechnology: Areas of Impact
11:19 Nanotech Enabled Eco Boat
17:37 Moving up the Value Chain
19:56 The Case of Suzhou
23:32 Shift to R and D and Innovation
29:02 The Dynamics Driving It
32:29 Public-Private Partnerships
40:38 Creating an Ecosystem is key
41:17 Talent
42:27 Venture Environment
44:04 Pragmatic, Business Driven
45:13 Entrepreneurs and Ventures
46:04 Progressed to a High-Level
46:42 Nanotech Finance and Investment in China
47:27 Intellectual Property
48:35 Case Study of Nanotech Venture-Early Stage
51:38 Case Study of Nanotech Venture-Growth Phase

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 4: Indirect Methods

Sat, 24 Oct 2026 15:00:00 -0000

Lecture 4 — Indirect Methods for Optimal Control

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_4.pdf

Speaker: Dr. Daniele Gammelli

Bio: Dr. Daniele Gammelli is the Research Director of the Machine Intelligence for Robot Autonomy Laboratory at the Italian Institute of Artificial Intelligence (AI4I), and a Researcher in the Department of Aeronautics and Astronautics at Stanford University. Since 2022, he has been a Research Fellow at the Center for Aerospace Autonomy Research (CAESAR) at Stanford and, until 2025, at the Center for Automotive Research at Stanford (CARS). He received his Ph.D. in Machine Learning and Mathematical Optimization from the Technical University of Denmark (DTU) in 2022, where his doctoral thesis was nominated for the DTU Best Thesis of the Year Award and the DTU Young Researcher Award. Dr. Gammelli’s research focuses on developing the algorithmic foundations and system-level methodologies that enable AI-powered autonomous systems to operate safely, efficiently, and reliably in high-stakes environments, with an emphasis on aerospace autonomy and next-generation mobility systems.

Uploaded 11-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Stanford MSandE435 Economics of the AI Supercycle | Spring 2026 | Building AI Factories

Sat, 24 Oct 2026 14:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

This seminar covers infrastructure and building AI factories at gigawatt scale.

Uploaded 17-06-26

Follow along with the course schedule: https://mse435.stanford.edu/

Guest Speaker: Chase Lochmiller, Crusoe

Instructor: Apoorv Agrawal is a Stanford Adjunct Lecturer in Management Science and Engineering and a partner at Altimeter Capital.

Playlist: https://youtube.com/playlist?list=PLoROMvodv4rOAe_DtQYOCKy6_x3irEYV1&si=hI20BgdZqMFtAKS-

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 7: Dynamic Programming

Sat, 24 Oct 2026 14:00:00 -0000

Lecture 7 — Dynamic Programming and Discrete-Time LQR

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_7.pdf

Speaker: Prof. Marco Pavone

Bio: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Uploaded 12-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 18: GMM (EM), PCA

Sat, 24 Oct 2026 13:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

For information on the graduate course, CS229 Machine Learning, including the syllabus and course materials, visit the course's website: https://cs229.stanford.edu/index.html-spr26

Uploaded 31-07-26

Teaching Team:
Chris Ré, Professor of Computer Science
Tengyu Ma, Assistant Professor of Computer Science

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Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 13: Data (Sources, Datasets)

Sat, 24 Oct 2026 13:00:00 -0000

For more information about Stanford's online Artificial Intelligence programs, visit: https://stanford.io/ai

Uploaded 19-05-26

To learn more about enrolling in this course, visit: https://online.stanford.edu/courses/cs336-language-modeling-scratch

Follow along with the course schedule and syllabus, visit: https://cs336.stanford.edu/

Percy Liang
Professor of Computer Science (and courtesy in Statistics)

Tatsunori Hashimoto
Assistant Professor of Computer Science

View the course playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMqXOcazWaTUHhq-yembLCV

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AI in Healthcare Series: When AI Stops Being a Project

Sat, 24 Oct 2026 13:00:00 -0000

Learn more about Stanford's online Healthcare AI programs: https://stanford.io/3NEt7uE
Check out the AI in Healthcare series playlist:https://stanford.io/3NEt7uE

Matt Lungren, Stanford University - https://profiles.stanford.edu/matthew-lungren
Justin Norden, Stanford University - https://profiles.stanford.edu/justin-norden

Guest speaker: Sandeep Dadlani, Optum Insight CEO

A fast-paced Stanford Healthcare AI podcast episode with Optum Insight CEO Sandeep Dadlani exploring how large healthcare enterprises are actually using AI today. The conversation covers the shift from simple chatbots to long-running agentic workflows, the exploding costs and risks of LLM tokens, model choice and “lock-in” (including GLM 5.2), and why AI is rapidly becoming the business, not just a project. With candid stories from UnitedHealth Group’s internal AI “harness,” governance, and leadership rituals, this episode is a playbook for health systems and payers trying to move from pilots to real, scaled impact.

Uploaded 28-08-26

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Good Boss, Bad Boss: A Peek Inside the Minds of the Best (and Worst)

Sat, 24 Oct 2026 12:00:00 -0000

http://apm.stanford.edu/ - Professor Robert Sutton draws on his new book, Good Boss, Bad Boss, to describe the mindset and moves of the best (and worst) bosses. He will weave psychological and management research together with instructive stories and cases to help you be a better boss (or deal with a difficult one).

Uploaded 02-05-11

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Innovation after the Crisis - Stanford Innovation Masters Series

Sat, 24 Oct 2026 12:00:00 -0000

Watch this video to learn about design thinking and how Stanford uses it to create new and innovative approaches to solving strategic problems such as the power of prototyping, asking good questions, and focusing on a user-centered, co creation model of value.

Uploaded 22-03-11

Speakers:
Bill Burnett - Executive Director, Stanford Design Program
Sven Beiker - Executive Director, Center for Automotive Research at Stanford (CARS)

Learn more about the Innovation Masters Series: http://scpd.stanford.edu/design/

0:00 Introduction
0:56 Meet the Speakers
4:00 After the Crisis: The Challenge
5:03 Think about the following questions
6:45 The answer to these questions is critical!
7:30 People are suggesting Design as an answer
9:26 What's the case for design?
10:45 The strategic thinking equation
12:54 Design thinking's version...
13:51 Design Thinking explained
16:37 Design Thinking started at Stanford
17:42 Design Thinking is also...
19:37 The Design Thinking process
21:38 The Design Thinking context
25:17 An innovation framework
32:34 CASE STUDY: Center for Automotive Research at Stanford - CARS
37:13 CARS Challenge the Existing and Exploring New Paths
42:29 Radical collaboration yields radical solutions
48:11 Who should attend?
49:01 Key Program Takeaways
49:25 Selected Faculty Presenters
50:48 Innovation Masters Series: Gaining a Competitive Advantage in an Uncertain Economy

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Stanford Seminar -Drinking from the Firehose: How the Mill CPU Decodes 30+ Instructions per Cycle

Sat, 24 Oct 2026 11:00:00 -0000

"Drinking from the Firehose: How the Mill CPU Decodes 30+ Instructions per Cycle" -Ivan Godard, Out-of-the-Box Computing
Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Uploaded 04-06-13

Learn more: http://bit.ly/WinYX5

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Graduate Student Orientation

Sat, 24 Oct 2026 11:00:00 -0000

The orientation is a deep-dive into the logistics of distance learning through the Stanford Center for Professional Development. All students - and especially new students - are encouraged to watch. We also welcome anyone who is not enrolled, but wishes to learn more about the Stanford Center for Professional Development.

Uploaded 23-09-11

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Stanford Course - Creating Demand: Driving Growth Using Traditional, Social and Viral Marketing

Sat, 24 Oct 2026 10:00:00 -0000

Preview the online course: Creating Demand: Driving Growth Using Traditional, Social and Viral Marketing (XINE 235)
More info: http://create.stanford.edu/courses/creating-demand.php

Uploaded 11-09-12

The company that has the most paying customers wins. But how do you get the word out, drive demand for your products and services, and generate sales? Today good marketing involves a clear strategy to reach the target audience, execute appropriate tactics, and measure results. In this course, you will master the fundamentals of outbound and inbound marketing and explore the myriad of options available in today's world of traditional and social media. Learn how to apply your skills to create a robust and innovative marketing strategy for a new product or a new company.

Learn how to:

* Use core demand generation principles and guidelines
* Leverage outbound demand generation
* Tap inbound demand
* Combine traditional, social, and mobile media to drive viral demand
* Create and use a messaging platform for optimal public relations and buzz marketing

Apply today: http://scpd.stanford.edu/innovation-and-entrepreneurship/individual/courses/creating-demand.php

This course is a part of the Stanford Innovation and Entrepreneurship online professional certificate program. The certificate provides the tools and techniques to hone your ability to generate and implement new ideas and lead innovative teams and organizations. Taught by world-renowned Stanford faculty, these courses are engaging, interactive, and full of useful practices and strategies that you can apply immediately.

Learn more: http://scpd.stanford.edu/innovation-and-entrepreneurship/

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 14: Intro to IL and RL

Sat, 24 Oct 2026 10:00:00 -0000

Lecture 14 — Intro to Imitation Learning and Reinforcement Learning

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_4.pdf

Speaker: Dr. Daniele Gammelli

Bio: Dr. Daniele Gammelli is the Research Director of the Machine Intelligence for Robot Autonomy Laboratory at the Italian Institute of Artificial Intelligence (AI4I), and a Researcher in the Department of Aeronautics and Astronautics at Stanford University. Since 2022, he has been a Research Fellow at the Center for Aerospace Autonomy Research (CAESAR) at Stanford and, until 2025, at the Center for Automotive Research at Stanford (CARS). He received his Ph.D. in Machine Learning and Mathematical Optimization from the Technical University of Denmark (DTU) in 2022, where his doctoral thesis was nominated for the DTU Best Thesis of the Year Award and the DTU Young Researcher Award. Dr. Gammelli’s research focuses on developing the algorithmic foundations and system-level methodologies that enable AI-powered autonomous systems to operate safely, efficiently, and reliably in high-stakes environments, with an emphasis on aerospace autonomy and next-generation mobility systems.

Uploaded 13-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Design Thinking: Transforming Teams

Sat, 24 Oct 2026 09:32:00 -0000

Professor Bill Burnett of Stanford University's d. school explores how to transform teams through design thinking.

Uploaded 09-02-12

Learn more about the Innovation Masters Series: http://scpd.stanford.edu/design

0:00 Introduction
0:28 Transforming teams with d.thinking
4:02 Design Thinking started at Stanford
4:31 Design Thinking stands for...
6:58 Design Thinking process
9:39 Brainstorming doesn't work!
12:41 Let's dig deeper...
16:46 Jazz ensemble = brainstorming group
18:20 It's partly a 10,000 hours problem
24:50 idea selection is a critical step
25:25 Vote using "potential" criteria
26:43 Brainstorming will transform teams
27:27 Space creates behavior
35:35 Reference for more
36:18 Take-aways
39:18 Key Program Takeaways
39:58 Program Faculty
40:59 Innovation Masters Series: Design Thinking and the Art of Innovation

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Stanford Seminar - Entrepreneurial Thought Leaders: Ferdinando Buscema of Magic Experience Designer

Sat, 24 Oct 2026 09:00:00 -0000

Ferdinando Buscema
Magic Experience Designer

In this seminar, entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. Speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks are followed by a half hour of class interaction.

Uploaded 10-06-13

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Design Thinking and Peak Performance - Stanford Innovation Masters Series

Sat, 24 Oct 2026 09:00:00 -0000

It is clear that innovation is critical to customers and businesses alike, more so now than ever. But in a company, generational gaps can often limit or stifle innovation as older managers, used to hierarchy, often fail to elicit ideas from those younger contributors. Watch this webinar to learn what makes millennials tick, and how to leverage their insights to improve products and services across a wide spectrum of offerings.

Uploaded 22-03-11

Speakers:
Bill Burnett - Executive Director, Stanford Design Program
Sheri Sheppard - Professor, Stanford University Mechanical Engineering Department

Learn more about the Innovation Masters Series: http://scpd.stanford.edu/design/

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Graduate Student Orientation - September 2012

Sat, 24 Oct 2026 08:00:00 -0000

http://scpd.stanford.edu/coursesSeminars/gradCourses.jsp

The orientation is a deep-dive into the logistics of distance learning through the Stanford Center for Professional Development.

Uploaded 22-09-12

Topics include:

* Stanford University overview
* lecture and video access
* homework and exam logistics
* class participation at a distance
* technical assistance for video
* course materials and websites
* deadlines
* student resources

Learn more about online courses, certificates, and degrees:

Graduate courses: http://scpd.stanford.edu/coursesSeminars/gradCourses.jsp
Graduate certificates: http://scpd.stanford.edu/certificates/graduateCertificates.jsp
Master's Degree in Engineering: http://scpd.stanford.edu/certificates/mastersDegrees.jsp

0:00 Introduction
1:09 Orientation Agenda
4:02 Stanford School of Engineering
5:48 Stanford Center for Professional Development Stanford University Curriculum and Research
6:37 Our Students
8:39 SCPD Member Companies
9:06 Graduate Portfolio
13:21 SCPD Education Path
19:42 Video Access
21:50 Course Website
23:08 Deadlines
26:31 Assignments
32:18 Exams
44:35 Stanford University's Honor Code
46:06 Course Evaluations
52:55 SCPD Calendar
55:07 FERPA Family Education Rights and Privacy Act
56:11 What if...

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Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 12: Evaluation

Sat, 24 Oct 2026 08:00:00 -0000

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 13: Intro to Learning

Sat, 24 Oct 2026 07:00:00 -0000

Lecture 13 — Intro to Learning, System Identification, and Adaptive Control

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_3.pdf

Speaker: Dr. Daniele Gammelli

Bio: Dr. Daniele Gammelli is the Research Director of the Machine Intelligence for Robot Autonomy Laboratory at the Italian Institute of Artificial Intelligence (AI4I), and a Researcher in the Department of Aeronautics and Astronautics at Stanford University. Since 2022, he has been a Research Fellow at the Center for Aerospace Autonomy Research (CAESAR) at Stanford and, until 2025, at the Center for Automotive Research at Stanford (CARS). He received his Ph.D. in Machine Learning and Mathematical Optimization from the Technical University of Denmark (DTU) in 2022, where his doctoral thesis was nominated for the DTU Best Thesis of the Year Award and the DTU Young Researcher Award. Dr. Gammelli’s research focuses on developing the algorithmic foundations and system-level methodologies that enable AI-powered autonomous systems to operate safely, efficiently, and reliably in high-stakes environments, with an emphasis on aerospace autonomy and next-generation mobility systems.

Uploaded 13-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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AI in Healthcare Series: Inside the Rise of AI in Healthcare, Open Evidence and Cyber Risks

Sat, 24 Oct 2026 07:00:00 -0000

Learn more about Stanford's online Healthcare AI programs: https://stanford.io/3NEt7uE
Check out the AI in Healthcare series playlist:https://stanford.io/3NEt7uE

Matt Lungren, Stanford University - https://profiles.stanford.edu/matthew-lungren
Justin Norden, Stanford University - https://profiles.stanford.edu/justin-norden

Guest speaker: DJ Patil, former US Chief Data Scientist and Health Tech Entrepreneur

This episode of the Stanford Healthcare AI podcast explores how AI is transforming healthcare security, policy, and patient empowerment. The guest, DJ Patil, former U.S. Chief Data Scientist and health tech entrepreneur, warns that hospitals are “sitting targets” for cyberattacks from nation-states using even “dumb” AI models. They discuss gaps in U.S. cybersecurity ownership, the need to treat healthcare as critical infrastructure, and the risks of ransomware-style “terrorism.” Balancing this, they highlight the explosive growth of tools like Open Evidence and GPT for clinicians, rising patient engagement, and the moral question of whether powerful AI should be gated or widely accessible.

Uploaded 15-06-26

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Frameworks for Design Thinking - Stanford Innovation Masters Series

Sat, 24 Oct 2026 06:00:00 -0000

Watch this video to learn problem-solving tools and frameworks pioneered by the Design Group at Stanford and innovative ways for you to overcome the challenges you face and gain a competitive advantage you need.

Learn more about the Innovation Masters Series: http://scpd.stanford.edu/design/

0:00 Meet the Speakers
2:55 The Challenge
4:18 Ask yourself the following questions
6:01 The answer to these questions is critical!
7:39 Some suggest Design Thinking is an answer
9:41 Design Thinking started at Stanford
13:35 Design Thinking stands for...
15:26 The Design Thinking Nexus
17:03 The Design Thinking processes
20:22 Leadership Redefined
21:40 Different Classes of Challenges
26:13 Innovation/Ethnography framework
29:52 Design Thinking based Strategy
31:49 STANFORD CENTER FOR PROFESSIONAL DEVELOPMENT
33:13 Key Program Takeaways
33:45 Selected Faculty Presenters
48:19 Innovation Masters Series: Design Thinking and the Art of fonowtion

Uploaded 22-03-11

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Stanford Seminar - Software-Defined Networking at the Crossroads

Sat, 24 Oct 2026 06:00:00 -0000

"Software-Defined Networking at the Crossroads" -Scott Shenker, University of California, Berkeley
Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Uploaded 04-06-13

Learn more: http://bit.ly/WinYX5

Explore all Stanford Online courses: https://online.stanford.edu/explore

0:00 Introduction
0:28 Co-Conspirators
2:32 Caveats and Clarifications
4:04 Architecture and Academia Don't Mix
4:33 Outline
7:49 What is the Root Cause?
8:48 The Two Networking "Planes"
9:54 Data Plane Abstractions: Layers
10:27 Layers Key to Internet's Success
11:26 Today's Control Plane: No Abstractions
12:39 Abstractions For Control Plane?
14:28 SDN: Two Control Plane Abstractions
15:05 SDN is "Layers" for Control Plane
15:52 Clean Separation of Concerns
17:05 Major Change in Paradigm
18:54 Looking Back After Five Years
20:44 The Role of Control Programs
22:06 Simple Example: Access Control
22:57 Network Virtualization
23:44 Virtualization Simplifies Control Program
24:27 Software Defined Network
24:58 Virtualization is Killer App for SDN
25:59 Long-Term Implications
27:52 Homogeneous Network
28:16 Three Important Logical Interfaces
29:48 How Are These Interfaces Implemented?
31:02 Incorporate MPLS Approach into SDN
32:00 Claim #1: Core Can Focus on Delivery
36:01 Software Switches Quite Common
37:04 Software Switches Getting Faster
38:09 Claim #2: Edge Can Use Software
38:26 Examples
39:15 Software vs Hardware
40:01 Long-Term Opportunity
42:53 The Myth of the Internet's Dataplane
43:03 The Reality of the Internet's Dataplane
43:35 Middleboxes As Common As Switches
44:02 Four Important Facts
45:25 One Inescapable Conclusion
48:58 How Did We Get Here?
50:25 Why Is Software Forwarding Better?
54:04 SDN and Networking at the Crossroads

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 16: Basic Concept in RL, Policy Gradient

Sat, 24 Oct 2026 05:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

For information on the graduate course, CS229 Machine Learning, including the syllabus and course materials, visit the course's website: https://cs229.stanford.edu/index.html-spr26

Uploaded 31-07-26

Teaching Team:
Chris Ré, Professor of Computer Science
Tengyu Ma, Assistant Professor of Computer Science

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Desktop Virtualization: Challenges and Opportunities in IT

Sat, 24 Oct 2026 05:00:00 -0000

Daniel Barreto discusses the challenges of desktop computing and how desktop virtualization has become a viable alternative. Server Virtualization is a technology that has matured and experienced explosive growth during the last decade, forever transforming the IT industry and becoming a $30 billion ecosystem in 2009, according to IDC. Desktop Virtualization -- also known as VDI (Virtual Desktop Infrastructure)-- is predicted to be the next wave in virtualization and is considered a leading trend for the IT industry in the coming years.

Uploaded 22-03-11

Learn more: http://scpd.stanford.edu/

0:00 Introduction
0:02 Meet the Speaker
1:21 Webinar Overview
2:40 Virtualization 101 Physical Machines
4:59 Server Virtualization Benefits
6:14 Market Data
13:19 Desktop Virtualization 101 (Cont.)
14:28 Basic Deployment Architecture Virtual Desktops Clients Thin Clients Laptops
18:02 Advanced Deployment Architecture Application
20:11 Deployment Components (Cont.) • Security Gateway
21:08 Desktop Virtualization Ecosystem
21:53 Understanding User Types
25:24 Important Deployment Factors (Cont.)
29:20 Challenges of Virtual Desktops
31:19 Benefits of Virtual Desktop Computing
34:41 Other Important Trends in Desktop Virtualization
36:24 Alternative Desktop Computing Models
38:11 Summary
39:12 Summer Quarter 2009-10
43:13 Stanford Center for Professional Development
43:45 Graduate Portfolio
45:50 Course Enrollment
47:17 Upcoming Webinar

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 12: Feasibility of MPC

Sat, 24 Oct 2026 05:00:00 -0000

Lecture 12 — Persistent Feasibility of MPC, Stability, and Explicit MPC

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_1.pdf

Speaker: Prof. Marco Pavone

Bio: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Uploaded 13-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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How to Earn the Trust of a Stakeholder - Stanford Center for Professional Development

Sat, 24 Oct 2026 04:00:00 -0000

In 1963, the Stanford Research Institute defined stakeholders as ""those groups without whose support the organization will cease to exist."" As project managers, the strength of your relationship with your stakeholders is key to your project's, and ultimately your organization's, survival.

Uploaded 22-03-11

In this webinar, Tom Kosnik explores why building trust and commitment among stakeholders is so critical to success.

Learn more about professional programs offered by the Stanford Center for Professional Development by visiting http://scpd.stanford.edu/programs/professional-certificates.

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Stanford Seminar - Managing Personal Information with Private, Accountable Crowdsourcing

Sat, 24 Oct 2026 04:00:00 -0000

Nicolas Kokkalis
Stanford University

This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI.

Uploaded 12-06-13

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 19: Model-Based RL

Sat, 24 Oct 2026 03:00:00 -0000

Lecture 19 — Model-based Reinforcement Learning and Conclusions

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_4.pdf

Speaker: Dr. Daniele Gammelli

Bio: Dr. Daniele Gammelli is the Research Director of the Machine Intelligence for Robot Autonomy Laboratory at the Italian Institute of Artificial Intelligence (AI4I), and a Researcher in the Department of Aeronautics and Astronautics at Stanford University. Since 2022, he has been a Research Fellow at the Center for Aerospace Autonomy Research (CAESAR) at Stanford and, until 2025, at the Center for Automotive Research at Stanford (CARS). He received his Ph.D. in Machine Learning and Mathematical Optimization from the Technical University of Denmark (DTU) in 2022, where his doctoral thesis was nominated for the DTU Best Thesis of the Year Award and the DTU Young Researcher Award. Dr. Gammelli’s research focuses on developing the algorithmic foundations and system-level methodologies that enable AI-powered autonomous systems to operate safely, efficiently, and reliably in high-stakes environments, with an emphasis on aerospace autonomy and next-generation mobility systems.

Uploaded 13-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI

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Stanford Seminar - Wireless Power Transfer and RF Energy Harvesting: New Options for System Designers

Sat, 24 Oct 2026 03:00:00 -0000

"Wireless Power Transfer and RF Energy Harvesting: New Options for System Designers" -Joshua R. Smith, University of Washington
Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Uploaded 13-06-13

Learn more: http://bit.ly/WinYX5

0:00 Introduction
0:52 Sensor Systems Lab Graduate Students
2:21 Why Wireless Power?
5:16 The space of wirelessly powered systems
9:44 WISP and UHF RFID
12:42 WISP Block Diagram
14:13 Rectifying Charge Pump (1 stage)
16:13 Rectifier Efficiency
16:25 Power Management Block
23:33 RFID Cryptography
24:50 Commercialization: Intel STAG
27:53 Wirelessly powered bistable display
30:25 Analog Backscatter
30:32 Hybrid analog-digital backscatter audio sensing
35:06 WARP: Wireless Ambient Radio Power
36:16 WARP: Cell Tower Power
37:47 Old and new harvester designs
40:48 ABC Ambient Backscatter Communication
48:38 Coupled resonators
52:11 Range and orientation adaptation
54:17 RF Health and Science
57:01 Wireless USB Charging LED Message Fan

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Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 11: Introduction to MPC

Sat, 24 Oct 2026 02:00:00 -0000

Lecture 11 — Introduction to Model Predictive Control

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control

Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/

Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_11.pdf

Speaker: Prof. Marco Pavone

Bio: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Uploaded 13-08-26

Instructors:
Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA

Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University

Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg

#StanfordOnline #OptimalControl #Robotics #PhysicalAI"

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Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 11: Scaling Laws

Sat, 24 Oct 2026 02:00:00 -0000

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Stanford Webinar - Exporting Innovation: Translating Silicon Valley's Culture in China

Sat, 24 Oct 2026 01:32:00 -0000

Silicon Valley is the number 1 innovation hotspot, known for motivating and rewarding teams with equity compensation plans, employee stock options, and other forms of ownership. Which parts of the region's ownership culture can really be exported? This webinar shares valuable insights into best practices for building and transplanting an effective ownership culture, as well as the challenges and benefits specific to China.

Uploaded 05-03-12

0:00 Introduction
0:19 Meet the Speakers
3:36 What Is Employee Ownership?
6:36 National Center for Employee Ownership
6:56 Research Highlights
9:09 Main Forms
11:42 Key Parameters
19:11 Sample Nonqualified Options
20:56 Sample Stock Appreciation Rights
21:37 Sample Employee Stock Ownership Plan
22:54 Major Factors
24:16 Silicon Valley's Ownership Culture
27:05 Some Differences in China
30:51 One CEO's Story of China
34:53 Different possible Options
37:26 One Expert's View
46:01 Teaching Case Available
50:57 Innovation Masters Series: Design Thinking and the Art of Innovation

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Agentic AI Program Overview

Sat, 24 Oct 2026 01:00:00 -0000

Learn more: https://stanford.io/4iHlGQp

Build modern AI agents with Stanford's graduate-level curriculum, adapted for working professionals.

Uploaded 02-09-26

Taught by Stanford Adjunct Professor Chowdhery and Assistant Professor Mirhoseini, this Agentic AI program takes you beyond basic LLM prompting to agents that reason, plan, use tools, and improve themselves through interaction with their environment.

What you'll learn:
- Design agentic systems for complex, multi-step real-world tasks
- Apply test-time scaling to boost LLM performance
- Use self-improvement methods: verifiers, feedback loops, RL, and search
- Build agents that use tools and take actions effectively
- Add retrieval and long-term memory to LLMs
- Develop planning and multi-step reasoning
- Evaluate agent performance with robust frameworks

Drawing on Stanford research and the latest advances in the field.

About the Instructors
Stanford Engineering Adjunct Professor Dr. Aakanksha Chowdhery led end-to-end training of the 540B PaLM model (the largest densely trained language model in the world at the time) and drove pre-training and scaling of Gemini's mixture-of-experts models. Stanford Assistant Professor Azalia Mirhoseini co-developed the MoE architectures now used in nearly every frontier model, created AlphaChip (the RL method behind Google's TPU designs), and pioneered LLM test-time scaling; she worked on both Claude and Gemini.

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Stanford Seminar: Creating, Sharing, and Privacy - Designing Social Technologies for Older Adults

Sat, 24 Oct 2026 01:00:00 -0000

Anne Marie Piper
Northwestern University

Staying socially connected in older adulthood has important implications for health and wellbeing. Online engagement throughout the lifespan has also become a critical aspect of full participation in society. While increasing numbers of older adults are now active online, late-life disability can make staying socially connected through technology difficult. In this talk, I will describe the complexities of designing social technologies for older adults with cognitive impairments and introduce prototypes that help understand how to bridge gaps in online participation. These systems leverage familiar communication interfaces that allow older adults to express themselves in new ways while also attending to the role of caregivers, therapists, and family members in social interaction. Drawing on my work with older adults, I will argue that the field of human-computer interaction faces pressing challenges around how to help people sustain their online lives throughout the lifespan.

Uploaded 20-03-17

Anne Marie Piper is an Assistant Professor in Communication Studies at Northwestern University. She holds courtesy appointments in Electrical Engineering and Computer Science and in Communication Sciences and Disorders. Her research in human-computer interaction investigates natural user interfaces to support communication, social interaction, and learning across the lifespan. Much of her work focuses on new technologies for individuals with disabilities and older adults. Anne Marie is the recipient of an NSF CAREER Award, 2014 Alumnae of Northwestern Award for Curriculum Development, Best Paper Awards and Nominations at ACM CHI, DIS, and ASSETS, and a 2011 UC-San Diego Interdisciplinary Scholar Award. Anne Marie received her PhD in Cognitive Science from the University of California, San Diego, her MA in Education from Stanford University, and her BS in Computer Science from the Georgia Institute of Technology. Anne Marie also worked as a user experience researcher at Microsoft and LeapFrog.

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

0:00 Introduction
3:02 Risk for Social Isolation Shrinking social network
5:42 Theoretical Framing
8:06 Research Approach
12:09 Authoring System Development
16:20 Analysis of Audio Messages 104 messages total
18:28 Why was this case successful? Demonstrated improved social engagement at age 105 Case of older adult family group as content creators: Right level of abstraction in authoring interface Leveraged existing photo sharing practices
22:35 The Third Hand helps the creative process along without being intrusive, without distorting meaning or imposing pictorial ideas or preferences alien to the client... Artistic competence and imagination are employed in the empathic service of others. (Kramer, 2000).
28:42 Cultural and embodied sharing...
33:42 The Work of Informal Caregivers Physical, emotional, social, and financial stress associated with caregiving
34:55 Forms of Work: Connecting
35:53 Forms of Work: Protecting
38:18 Overview

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Stanford EE259 I Lenses, image sensors, image signal processing I 2023 I Lecture 20

Sat, 24 Oct 2026 00:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 05-02-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford Seminar: The Increasing Globalization of Asia Startups Outside China

Fri, 23 Oct 2026 23:00:00 -0000

EE402T: Entrepreneurship in Asian High-Tech Industries
The Increasing Globalization of Asia Startups Outside China
Speaker: Chris Burry, US Market Access Center

Uploaded 19-04-17

In this talk, Chris Burry, the Co-CEO of the US Market Access Center will discuss trends among startup companies in Asia outside of China that are beginning to globalize their business at earlier stages of development. He introduces their participation in channels for globalization, such as incubator and accelerator programs in Silicon Valley and also references Asia activities by Silicon Valley investors and mentors. What benefits can Silicon Valley achieve from the increased presence of Asia startups here?

About the Speaker:
Chris Burry is a member of US MAC’s management team. Prior to that he was a founding member of Avanade, a joint venture between Microsoft and Andersen Consulting. Founded in 2000, Avanade has grown to 9,500 global employees and annual revenues of over $850 million, with offices in 22 countries and projects in 30.

Prior to Avanade, Chris was a senior executive at Andersen Consulting and a senior technology consultant at EDS. In these roles, he oversaw numerous large-scale projects for clients in the energy, financial services, healthcare and government sectors. He has experience working in the United Kingdom, France, Germany and Australia.

Chris was also a columnist on operating systems and infrastructure for ComputerWorld magazine and an adviser to the research division of IDC (publisher of a number of technology magazines) on cloud computing and the future of technology. He was also a regular speaker at industry conferences for Microsoft and IDC. Chris has an undergraduate degree from Swarthmore College in Political Science (with honors). He lives in Emerald Hills, California.

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Stanford EE259 I Camera principle of operation and architectures, image formation I 2023 I Lec. 19

Fri, 23 Oct 2026 22:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 04-02-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 14: Transformers, In-Context Learning

Fri, 23 Oct 2026 21:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

For information on the graduate course, CS229 Machine Learning, including the syllabus and course materials, visit the course's website: https://cs229.stanford.edu/index.html-spr26

Uploaded 31-07-26

Teaching Team:
Chris Ré, Professor of Computer Science
Tengyu Ma, Assistant Professor of Computer Science

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Stanford Seminar: PyWren - Pushing Microservices to Teraflops

Fri, 23 Oct 2026 21:00:00 -0000

EE380: Computer Systems Colloquium Seminar
PyWren - pushing microservices to teraflops
Speaker: Eric Jonas, UC Berkeley

Much of cloud computing infrastructure remains hard to use, in spite of decades of both academic research and commercialization. Fortunately, recent technologies developed for web services and internet startups can be repurposed to enable a much lower-friction scalable cloud experience. Our goal is making the power, elasticity, and dynamism of commercial cloud services like Amazon's EC2 accessible to busy applied physicists, electrical engineers, and data scientists, as well as a compelling new capability over Matlab, hopefully encouraging migration. We built PyWren, a transparent distributed execution engine on top of AWS Lambda, which hopefully simplifies many scale-out use cases for data science and computational imaging. We will demo applications built on our framework and seek user input into next directions.

Uploaded 20-04-17

Joint work with Shivaram Venkataraman, Qifan Pu, Ion Stoica, and Ben Recht.

About the Speaker:
Eric Jonas is currently a postdoc in computer science at UC Berkeley working with Ben Recht on computational imaging, inverse problems, and machine learning for scientific data acquisition. He earned his PhD in Computational Neuroscience, M. Eng in Electrical Engineering, BS in Electrical Engineering and Computer Science, and BS in Neurobiology, all from MIT. Prior to his return to academia, he was founder and CEO of Prior Knowledge, a predictive database company which was acquired in 2012 by Salesforce.com, where he was Chief Predictive Scientist until 2014. In 2015 he was named one of the top rising stars in bioengineering by the Defense Department's Advanced Research Projects Agency (DARPA).

For more information about this seminar and its speaker, you can visit http://ee380.stanford.edu/Abstracts/170419.html

Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum.

Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Learn more: http://bit.ly/WinYX5

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Stanford Seminar - Single-Life Robot Deployment: Adapting On-the-Fly to Novel Scenarios

Fri, 23 Oct 2026 20:00:00 -0000

January 19, 2024
Annie Chen of Stanford University

A major obstacle to the broad application of robots is their inability to adapt to unexpected circumstances, which limits their uses largely to tightly controlled environments. Even equipped with prior experience and pre-training, robots will inevitably encounter out-of-distribution (OOD) situations at deployment time that may require a large amount of on-the-fly adaptation. In this talk, I will first motivate and introduce the problem setting of single-life deployment, which provides a natural setting to study the challenge of autonomously adapting to unfamiliar situations. I will then present our recent work on this problem, Robust Autonomous Modulation (ROAM). By effectively identifying relevant behaviors on-the-fly, ROAM adapts over 2x as efficiently compared to existing methods when facing a variety of OOD situations during deployment. Crucially, this adaptation process all happens within a single episode at test time, without any human supervision.

Uploaded 03-02-24

About the speaker: https://anniesch.github.io/

More about the course can be found here: https://stanfordasl.github.io/robotics_seminar/

View the entire AA289 Stanford Robotics and Autonomous Systems Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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How to Build a Product II - Aaron Levie, co-founder of Box - Stanford CS183F: Startup School

Fri, 23 Oct 2026 19:00:00 -0000

For our second talk on How to Build a Product, we are joined by Aaron Levie, CEO and co-founder of Box.

Uploaded 24-04-17

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Stanford EE259 I Lidar range and direction of arrival estimation I 2023 I Lecture 18

Fri, 23 Oct 2026 18:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 03-02-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Pivot Thinking: The Neuroscience of Design

Fri, 23 Oct 2026 17:32:00 -0000

htts://scpd.stanford.edu/design/ Mark Scharr explores the latest findings in cognitive science and neuroscience that helps explain the thinking behind design thinking. This includes a concept called "pivot thinking" which is the cognitive ability to reframe a problem or move in a new direction.

Uploaded 09-03-12

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Stanford Seminar: Disrupting the Pink Aisle

Fri, 23 Oct 2026 17:00:00 -0000

Debbie Sterling
GoldieBlox

Debbie Sterling, founder and CEO of GoldieBlox, shares her evolution from lonely inventor to inspiring entrepreneur with a vision to give young girls the confidence to become engineers through hands-on play. Sterling talks about overcoming gender stereotypes and her own fears, as well as the entrepreneurial challenges of embracing failure and succeeding despite scant resources.

Uploaded 26-04-17

Debbie Sterling is an engineer, entrepreneur and a leader in the movement toward getting girls interested in STEM. She was named Time’s "Person of the Moment,” honored by the National Retail Foundation as one of 25 “People Shaping Retail’s Future,” and was recently added to Fortune’s “40 under 40” list. In 2015, Sterling was inducted as a Presidential Ambassador for Global Entrepreneurship and honored by the National Women’s History Museum with a “Living Legacy Award” for her work to empower girls around the world.

Sterling holds a degree in mechanical engineering and product design from Stanford University.

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Seminar - Improving Robotic Dexterity with Optical Tactile Sensor DenseTact

Fri, 23 Oct 2026 16:00:00 -0000

January 19, 2024
Won Kyung Do of Stanford University

Dexterous manipulation, particularly of small everyday objects, remains a complex challenge in the field of robotics. In this talk, I will present two studies addressing these challenges with DenseTact, a soft optical tactile sensor. The first study introduces an innovative approach to inter-finger manipulation using a tactile sensor-equipped gripper. This development not only enhances grasping accuracy in cluttered environments but also facilitates improved manipulation and reorientation of small objects, enabling more precise classification. The second study addresses the challenges of grasping objects of varying sizes on flat surfaces. I will introduce the DenseTact-Mini, an optical tactile sensor featuring a soft, rounded, smooth gel surface, compact design, and a synthetic fingernail. This sensor enables the grasping of multi-scale objects using three distinct strategies for different sizes and masses of objects. This presentation will underscore how these advancements open new avenues in robotics, particularly in enhancing manipulation capabilities in complex scenarios where vision is limited due to occlusions.

Uploaded 02-02-24

More about the speaker: https://arm.stanford.edu/people/wonkyung-do

More about the course can be found here: https://stanfordasl.github.io/robotics_seminar/

View the entire AA289 Stanford Robotics and Autonomous Systems Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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Stanford Seminar: Forcing Impact through the Gauntlet of an Emerging Ecosystem

Fri, 23 Oct 2026 15:00:00 -0000

EE402T: Entrepreneurship in Asian High-Tech Industries Seminar
Relentless: Forcing Impact through the Gauntlet of an Emerging Ecosystem
Speaker: Maoi Arroyo, Hybridigm Consulting

Uploaded 26-04-17

The Philippines has been the underachiever of South East Asia – a country teeming with potential, from the talent of its people to its stunning biodiversity. In this talk, serial entrepreneur Maoi Arroyo discusses how a boutique consulting firm she founded has worked with academia, government, investors, corporations, and startups to push innovations where they can make the most impact. Specializing in “inclusive innovations”, Hybridigm Consulting, focuses on combining technology, business, and community development to increase average household net worth in a country where 85% of people live on less than $5 PPP. Her firm is now transforming into a startup studio and fund dedicated to eradicating poverty in the Philippines in a decade.

About the Speaker:
Ma. Antonia Odelia G. Arroyo is the founder and CEO of Hybridigm Consulting, the Philippines’ pioneering biotechnology consulting firm, whose clients include companies in Europe, the US, and the ASEAN region. She was named as one of the World Economic Forum’s Young Global Leaders of 2015. She was also awarded in 2011 as one of the Ten Outstanding Young Men (TOYM), by the President of the Republic of the Philippines. The TOYM is the highest and most prestigious award given to Filipinos under 40. She was honored by the Philippine Development Foundation USA as the youngest of 45 awardees in Science and Technology (2010) for her contributions to national development; and hailed by Entrepreneur Magazine (2006) as one of the top 35 entrepreneurs under 35. She co-organized two major private sector initiatives in the Philippines: TechnoNegosyo (spearheaded by the Philippine Center for Entrepreneurship) and PESO, an annual technology and innovation business plan competition. She was a member of the inaugural class of the Master’s in Bioscience Enterprise, a professional practice program developed by the University of Cambridge, Harvard-MIT Division of Health Sciences and Technology (HST) and the MIT Sloan School of Management. She graduated with a degree in Biology from the University of the Philippines.

To learn more about the Stanford Center for Professional Development: http://scpd.stanford.edu/home
To learn more about EE02T: http://asia.stanford.edu/?page_id=7818

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Stanford EE259 I Photodetection principles (direct vs. coherent), lidar system arch. I 2023 I Lec 17

Fri, 23 Oct 2026 14:00:00 -0000

To follow along, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 02-02-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 13: LLMs, Next-Word Prediction Loss

Fri, 23 Oct 2026 13:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

For information on the graduate course, CS229 Machine Learning, including the syllabus and course materials, visit the course's website: https://cs229.stanford.edu/index.html-spr26

Uploaded 31-07-26

Teaching Team:
Chris Ré, Professor of Computer Science
Tengyu Ma, Assistant Professor of Computer Science

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How to Build a Product III - Stanford CS183F: Startup School

Fri, 23 Oct 2026 13:00:00 -0000

Jason Lemkin, of SaaStr, sits down with Solomon Hykes, CEO and co-founder of Docker, Tracy Young, CEO and co-founder of Plangrid, and Harry Zhang, co-founder of Lob, to discuss their experience building products in our third installment of How to Build a Product.

Uploaded 26-04-17

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Stanford EE259 I Gaussian beams, beam scanning techniques I 2023 I Lecture 16

Fri, 23 Oct 2026 12:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 01-02-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford Seminar - Software-centric Visible Light Communication for the Internet of Things

Fri, 23 Oct 2026 11:00:00 -0000

EE380: Computer Systems Colloquium Seminar
Software-centric Visible Light Communication for the Internet of Things
Speaker: Thomas R. Gross, ETH Zurich

Uploaded 27-04-17

Visible Light Communication (VLC) based on LEDs for light emission and reception can be realized using simple components, delegating many of the "hard" problems to software. We present a software-centric approach that supports networking a wide range of devices -- devices that include only simple single LEDs (such as wearables, toys, consumer electronics) as well as LED light bulbs that run Linux and provide a VLC communication fabric (room-area network). One of the benefits of the software-centric approach is easy integration into distributed applications - a necessary condition for a pervasive communication infrastructure for the Internet of Things that requires a wide range of services (localization, time, authentication, etc).

Joint work with Stefan Mangold (Lovefield Wireless, Inc.) and Stefan Schmid (ETH Zurich).

About the Speaker:
Thomas Gross has been a PI or co-PI of various research grants and contracts. Recent projects, supported by the Swiss National Science Foundation, include the "Datacenter Observatory", a joint EPFL-ETH Zurich-USI project to support system-level research and teaching, and a collaboration with USI on novel approaches for dynamic program analysis.

For more information about this seminar and its speaker, you can visit http://ee380.stanford.edu/Abstracts/170426.html

Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum.

Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Learn more: http://bit.ly/WinYX5

0:00 Introduction
0:38 Handle difficult problems down to physical layer, media + access control synchronization
1:59 Internet of Toys
3:14 Radio Spectrum and Internet of Things
4:08 Visible Light Communication
7:34 Communication enables localization
9:00 Illumination and communication
11:17 Outline
11:54 Communication protocols
19:59 Software-based PHY/MAC layer
20:43 System design
21:32 Hardware building blocks
22:24 Prototype construction
22:49 System components
23:05 Light bulb casing
23:40 Fully assembled light bulb
24:50 PHY layer - constant light output
27:40 MAC layer - Listen-Before-Talk
29:05 VLC for the Internet of Things
30:00 Linux integration for LED light bulbs
31:16 Software-conscious design
32:36 Arduino Yun devices
34:16 Light bulb testbed infrastructure
35:01 Testbed software
36:09 Communication and networking
37:21 Throughput (multi-hop)
38:03 UDP multi-hop
39:41 ICMP round-trip time (distance)
39:49 ICMP round-trip time (multi-hop)
41:05 LED light bulbs testbed
41:38 RSSI measurements for localization
42:49 RSSI measured (w/ synchronization)
44:51 RSSI measurements with 3 light bulbs
45:39 Adaptive sensing
46:20 PHY layer modes
48:08 Sensing strategies
49:11 Synchronization correction
50:38 The bottom line
50:54 Evaluation results: LED - single link
54:38 Evaluation results: LED - network

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Stanford Webinar - Human-Robot Interaction

Fri, 23 Oct 2026 10:00:00 -0000

Learn more from Monroe Kennedy and Bill Burnett during their on-campus program happening March 20-22, 2024: https://online.stanford.edu/courses/xdes500-emerging-technologies-designing-strategy-and-forecasting-disruption

How will technology change the way we think, live, and work? Is there a future in which robots will work autonomously beside humans, and what work is being done to get us there?

Uploaded 01-02-24

In this webinar, Stanford University School of Engineering faculty Monroe Kennedy and Bill Burnett explore a future in which robots can think “independently” to enhance human performance and understand human intent. Get a glimpse at the future of collaborative design with previews from the Stanford Assistive Robotics and Manipulation Lab.

#robot #robotics #technology #design

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Professor Bill Burnett - a glimpse into the Innovation Masters Series

Fri, 23 Oct 2026 09:32:00 -0000

Executive Director Bill Burnett discusses this unique program, "Design Thinking and the Art of Innovation."

Uploaded 04-04-12

Learn more about the Innovation Masters Series: http://scpd.stanford.edu/design/

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How to Build a Product IV - Jan Koum, Co-founder of WhatsApp - Stanford CS183F: Startup School

Fri, 23 Oct 2026 09:00:00 -0000

In the fourth and final episode of How to Build a Product, Jan Koum shares his journey as the CEO and co-founder of WhatsApp.

Uploaded 28-04-17

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Stanford Seminar - Catalyzing AI Advances with Human-Centered Interactive systems

Fri, 23 Oct 2026 08:00:00 -0000

January 19, 2024
Xiang 'Anthony' Chen of UCLA

Despite the promises of AI, what we often ignore is the gap between a high-performing AI model and how such AI models can serve humanity. In this talk, I discuss how my research closes this gap by developing interactive systems that catalyze advances in AI to achieve three levels of human-centered objectives: aligning with human values, assimilating human intents, and augmenting human abilities.

Uploaded 31-01-24

About the speaker:
Xiang Anthony' Chen is an Assistant Professor in UCLA's Department of Electrical and Computer Engineering. He received a Ph.D. in the School of Computer Science at Carnegie Mellon University. Anthony's area of expertise is Human-Computer Interaction (HCI). His research employs human-centered design methods to build systems that catalyze advances in AI to augment human activities, supported by NSF CAREER Award, ONR YIP Award, Google Research Scholar Award, Intel Rising Star Award, Hellman Fellowship, NSF CRII Award, and Adobe Ph.D. Fellowship. Anthony s work has resulted in 50+ publications with three best paper awards and three honorable mentions in top-tier HCI conferences.

More about the course can be found here: https://hci.stanford.edu/seminar/

View the entire CS547 Stanford Human-Computer Interaction Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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Stanford Seminar: Taking AR to Task - Explaining Where and How in the Real World

Fri, 23 Oct 2026 07:00:00 -0000

Steven Feiner
Columbia University

Researchers have been actively exploring Augmented Reality (AR) and Virtual Reality (VR) for a half century, first in the lab and later in the streets. Over the past year, however, VR head-worn display developer kits have metamorphosed into early consumer products that are far superior to what most VR researchers previously had available. And compelling AR head-worn display developer kits are now being released that promise to beget everyday see-through eyewear. What can the upcoming generation of consumer AR make possible by interactively integrating virtual media with our experience of the physical world? I will try to answer this question in part by presenting some of the research being done by Columbias Computer Graphics and User Interfaces Lab to explore how we can support users in performing skilled tasks. Examples I will discuss range from providing standalone assistance, to enabling collaboration between a remote expert and a local user. I will address infrastructure spanning the gamut from lightweight, monoscopic eyewear, to hybrid user interfaces that synergistically combine tracked, stereoscopic, see-through head-worn displays with other displays and interaction devices.

Uploaded 01-05-17

Steve Feiner is a Professor of Computer Science at Columbia University, where he directs the Computer Graphics and User Interfaces Lab, and codirects the Columbia Vision and Graphics Center. His lab has been doing VR, AR and wearable research for over 25 years, designing and evaluating novel 3D interaction and visualization techniques, creating the first outdoor mobile AR system using a see-through head-worn display, and pioneering experimental applications of AR to fields such as tourism, journalism, maintenance, and construction. Steve received an A.B. in Music and a Ph.D. in Computer Science, both from Brown University. He is coauthor of Computer Graphics: Principles and Practice, received the IEEE VGTC Virtual Reality Career Award, and was elected to the CHI Academy. Together with his students, he has won the ACM UIST Lasting Impact Award and best paper awards at ACM UIST, ACM CHI, ACM VRST, IEEE ISMAR, and IEEE 3DUI. Steve is lead advisor to Meta, the AR company.

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford EE364A Convex Optimization I Stephen Boyd I 2023 I Lecture 1

Fri, 23 Oct 2026 06:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee364a/

Uploaded 11-03-24

Stephen Boyd
Professor of Electrical Engineering at Stanford University
https://web.stanford.edu/~boyd/

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee364a-convex-optimization-i

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 12: Representation Learning

Fri, 23 Oct 2026 05:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

For information on the graduate course, CS229 Machine Learning, including the syllabus and course materials, visit the course's website: https://cs229.stanford.edu/index.html-spr26

Uploaded 31-07-26

Teaching Team:
Chris Ré, Professor of Computer Science
Tengyu Ma, Assistant Professor of Computer Science

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How and Why to Start A Startup - Sam Altman and Dustin Moskovitz - Stanford CS183F: Startup School

Fri, 23 Oct 2026 05:00:00 -0000

Sam Altman, President of Y Combinator, and Dustin Moskovitz, Cofounder of Facebook and Asana, kick off the How to Start a Startup Course. Dustin discusses Why to Start a Startup and Sam introduces the 4 key components of Starting a Startup: Idea, Product, Team and Execution.
http://www.slideshare.net/StevenPham7/lecture-1-how-to-start-a-startup-by-sam-altman

Uploaded 05-04-17

Explore Stanford Online courses: https://online.stanford.edu/explore

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Stanford EE259 I Waveform orthogonality in MIMO radar, radar noise and interference I 2023 I Lec. 14

Fri, 23 Oct 2026 04:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 30-01-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford Seminar - Rethinking Design for Accessibility

Fri, 23 Oct 2026 03:00:00 -0000

February 23, 2024
Anne Marie Piper, University of California, Irvine

Approximately 61 million Americans, or one in four U.S. adults, have a disability that affects daily life. Despite the prevalence of disability, accessibility is often an afterthought in technology design. Discussions of accessibility often center on checklists of requirements and whether or not a system has particular features. In this talk, I draw on theories from disability studies to argue for a view of accessibility that is collaboratively negotiated, situated, and enacted. Grounded in extensive field work, I will present three cases of design for accessibility that shift how we think about building systems with and for individuals with disabilities. Collectively, these projects reveal the interactive nature of accessibility that is often missing in individualistic system design and call attention to the importance of the social and political dimensions of accessibility alongside the technological.

Uploaded 07-03-24

About the speaker:
Anne Marie Piper is an Associate Professor in the Department of Informatics at the University of California, Irvine. Her research in human-computer interaction focuses on designing and studying new technologies to support communication, social interaction, and learning for people across the lifespan. Her research is funded by NSF, including a CAREER award, the Mellon Foundation, and Microsoft, and has been recognized with numerous Best Paper Awards and Nominations at ACM CHI, CSCW, DIS, and ASSETS. Anne Marie earned her PhD in Cognitive Science from the University of California, San Diego, MA in Education from Stanford University, and BS in Computer Science from Georgia Tech. Prior to joining UC-Irvine, she was a tenured faculty member at Northwestern University

More about the course can be found here: https://hci.stanford.edu/seminar/

View the entire CS547 Stanford Human-Computer Interaction Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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Stanford Seminar - Emergence of Entrepreneurship Ecosystems Across Southeast Asia

Fri, 23 Oct 2026 02:00:00 -0000

EE402T: Entrepreneurship in Asian High-Tech Industries
The Emergence of Entrepreneurship Ecosystems Across Southeast Asia: Views of a Venture Capitalist
Speaker: Peng T. Ong, Managing Director, Monk’s Hill Ventures

Uploaded 05-04-17

Over the course of our public seminar series, we will explore the most recent trends, patterns, and challenges of entrepreneurship in Asia and their relevance to Silicon Valley and the U.S. Guest speakers include entrepreneurs, investors and mentors, and other experts on the current entrepreneurial ecosystems of major Asia economies. Read more about the course here: http://asia.stanford.edu/?page_id=7818

About the Speaker:
Peng T. Ong is an entrepreneur who invests in entrepreneurs. He does so primarily through his role as Managing Director at Monk’s Hill Ventures — a technology venture fund based in Southeast Asia that he co-founded. Peng is an independent board director of YY Inc. [NASDAQ:YY], IE Singapore, IMDA, and Solve Education!. He is a trustee of the Singapore University of Technology and Design (SUTD) where he chairs the Investment Committee of SUTD’s Endowment Fund.

Venture Funds. Peng was the chair of Infocomm Investments from 2006 through 2013. He was also Venture Partner at GSR Ventures in Beijing from 2010 to 2013 and was Chairman of Jungle Ventures from 2011 to 2013.

Companies. Peng founded Encentuate in 2002. From 2008 through 2009, he was an executive at IBM, after Encentuate’s acquisition. Prior to founding Encentuate, Peng was the founder and chairman of Interwoven, Inc. (now part of HP), the leading provider of content infrastructure. He was the president and CEO of Interwoven through 1997, and its chairman through 2002. During his tenure at Interwoven he led the company to a successful IPO [NASDAQ: IWOV]. In 2002, for his leadership in building Interwoven into the world’s leading provider of enterprise-class content infrastructure software, Peng was awarded the University of Illinois Urbana-Champaign College of Engineering’s Alumni Award for Distinguished Service. Before Interwoven, Peng was co-founder and chief architect of Electric Classifieds, Inc.—the creators of Match.com (now part of IAC). The three businesses he started now generate annual revenues that total more than US$1 billion.

Peng has also held various engineering and management roles at Sybase, Inc. (now SAP), Illustra (now IBM Informix), and Gensym Corporation. Peng co-authored numerous patents for technologies developed at his companies.

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Design Thinking: A Strategic Tool

Fri, 23 Oct 2026 01:32:00 -0000

Professor Banny Banerjee, of Stanford's Design Group, discusses how to use design thinking to foster a culture of strategic innovation.

Uploaded 06-04-12

Learn more about the Innovation Masters Series: http://scpd.stanford.edu/design/

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Stanford Seminar - Robot Skill Acquisition: Policy Representation and Data Generation

Fri, 23 Oct 2026 01:00:00 -0000

February 16, 2024
Shuran Song of Stanford University

What do we need to take robot learning to the 'next level?' Is it better algorithms, improved policy representations, or is it advancements in affordable robot hardware? While all of these factors are undoubtedly important, however, what I really wish for is something that underpins all these aspects – the right data. In particular, we need data that is scalable, reusable, and robot-complete. While ‘scale’ often takes center stage in machine learning today; I would argue that in robotics, having data that is also both reusable and complete can be just as important. Focusing on sheer quantity and neglecting these properties make it difficult for robot learning to benefit from the same scaling trend that other machine learning fields have enjoyed. In this talk, we will explore potential solutions to such data challenges, shed light on some of the often-overlooked hidden costs associated with each approach, and more importantly, how to potentially bypass these obstacles.

Uploaded 05-03-24

About the speaker: https://shurans.github.io/

More about the course can be found here: https://stanfordasl.github.io/robotics_seminar/

View the entire AA289 Stanford Robotics and Autonomous Systems Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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Stanford Seminar - Silicon Valley and The U.S. Government: Saronic Technologies’ Dino Mavrookas

Fri, 23 Oct 2026 00:00:00 -0000

May 13, 2024
Dino Mavrookas, Saronic Technologies

Dino Mavrookas, co-founder and CEO of Saronic Technologies, is interviewed by Ernestine Fu Mak with special remarks from Joe Felter and Steve Blank. Saronic is building unmanned surface vehicles for maritime security and domain awareness.

Uploaded 10-07-24

—

This session is part of the “Silicon Valley and The U.S. Government” speaker series co-led by Ernestine Fu Mak, Steve Blank, Joe Felter, Eric Volmar, and Paul Kwan.

Silicon Valley collaborations with the U.S. government have led to some of the most important technologies in our society. Driven by visionary leaders, these collaborations have overcome regulatory, business, and technological challenges to achieve exceptional innovation. The results of these innovations have addressed national issues and societal challenges.

As government and industry collaboration is more important than ever to accelerate private sector innovation and shape the future of technology, join us in hearing the personal accounts of technology experts, business executives, and public service leaders who have paved the way with these types of innovations.

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Stanford Seminar - Doubly-efficient zkSNARKs Without Trusted Setup

Thu, 22 Oct 2026 23:00:00 -0000

zkSNARKs: what are they? How do their principles apply to security? And more importantly, what is their relationship to cryptocurrency?

Uploaded 26-06-18

Join Riad Wahby as he presents his team’s work on Zero-Knowledge Succinct Non-Interactive Argument of Knowledge, also known as zkSNARKs.

This presentation is brought to you by the Stanford Computer Forum and the Stanford Advanced Computer Security Program. If you would like information on how to join the forum and attend the next meeting, see our website: http://forum.stanford.edu/about/howtojoin.php

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Stanford Seminar - Silicon Valley and The U.S. Government: Lockheed Martin Ventures’ Chris Moran

Thu, 22 Oct 2026 22:00:00 -0000

May 6, 2024
Chris Moran, Lockheed Martin Ventures

Lockheed Martin Ventures’ Executive Director Chris Moran is interviewed by Ernestine Fu Mak with special remarks from Eric Volmar and Steve Blank. Lockheed Martin Ventures makes strategic investments in early stage companies developing disruptive technologies in core interest areas and new markets important to Lockheed Martin.

Uploaded 10-07-24

—
This session is part of the “Silicon Valley and The U.S. Government” speaker series co-led by Ernestine Fu Mak, Steve Blank, Joe Felter, Eric Volmar, and Paul Kwan.

Silicon Valley collaborations with the U.S. government have led to some of the most important technologies in our society. Driven by visionary leaders, these collaborations have overcome regulatory, business, and technological challenges to achieve exceptional innovation. The results of these innovations have addressed national issues and societal challenges.

As government and industry collaboration is more important than ever to accelerate private sector innovation and shape the future of technology, join us in hearing the personal accounts of technology experts, business executives, and public service leaders who have paved the way with these types of innovations.

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 11: Diffusion Models

Thu, 22 Oct 2026 21:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

For information on the graduate course, CS229 Machine Learning, including the syllabus and course materials, visit the course's website: https://cs229.stanford.edu/index.html-spr26

Uploaded 31-07-26

Teaching Team:
Chris Ré, Professor of Computer Science
Tengyu Ma, Assistant Professor of Computer Science

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Stanford Seminar - Silicon Valley and The U.S. Gov: Go-to-Market Strategies for Dual-Use Companies

Thu, 22 Oct 2026 21:00:00 -0000

May 20, 2024
Steve Blank, Stanford University

Steve Blank presents on “Go-to-Market Strategies for Dual-Use Companies.” He dives into strategies for effectively navigating sales to both commercial and government customers.

Uploaded 10-07-24

—

This session is part of the “Silicon Valley and The U.S. Government” speaker series co-led by Ernestine Fu Mak, Steve Blank, Joe Felter, Eric Volmar, and Paul Kwan.

Silicon Valley collaborations with the U.S. government have led to some of the most important technologies in our society. Driven by visionary leaders, these collaborations have overcome regulatory, business, and technological challenges to achieve exceptional innovation. The results of these innovations have addressed national issues and societal challenges.

As government and industry collaboration is more important than ever to accelerate private sector innovation and shape the future of technology, join us in hearing the personal accounts of technology experts, business executives, and public service leaders who have paved the way with these types of innovations.

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Stanford Seminar- Capturing Human Behavior and Language for Interactive Systems

Thu, 22 Oct 2026 20:00:00 -0000

Speaker: Ethan Fast

Computers are more integrated into our lives and society as time goes by yet they still don't understand many of the human actions that surround them. To make progress, we need systems that deeply understand people and capture human experience through humans' daily activities, whether simple or more advanced. The key to advancing human- machine interaction is through acquiring data and creating models that allow machines to interpret the data and perform the actions needed. Using this technology, will allow us move forward with other processes that affect the public, such as driving public policy, detecting gender bias, and building interfaces that shape communities the way we want.

Uploaded 05-07-18

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Stanford Seminar - Silicon Valley and The U.S. Government: Army Application Laboratory’s Casey Perley

Thu, 22 Oct 2026 19:00:00 -0000

June 3, 2024
Casey Perley, Army Application Laboratory

Army Application Laboratory’s Executive Director Casey Perley is interviewed by Eric Volmar with special remarks from Joe Felter.

Uploaded 10-07-24

—

This session is part of the “Silicon Valley and The U.S. Government” speaker series co-led by Ernestine Fu Mak, Steve Blank, Joe Felter, Eric Volmar, and Paul Kwan.

Silicon Valley collaborations with the U.S. government have led to some of the most important technologies in our society. Driven by visionary leaders, these collaborations have overcome regulatory, business, and technological challenges to achieve exceptional innovation. The results of these innovations have addressed national issues and societal challenges.

As government and industry collaboration is more important than ever to accelerate private sector innovation and shape the future of technology, join us in hearing the personal accounts of technology experts, business executives, and public service leaders who have paved the way with these types of innovations.

Learn more about your ad choices. Visit megaphone.fm/adchoices

Stanford Seminar- New Trends Among Asia-Focused Accelerators-Incubating at a Distance

Thu, 22 Oct 2026 18:00:00 -0000

The high-tech industry is booming in Asian countries, with many leading new, innovative technologies and startups that place them high in the tech industry and competitive markets, such as TechGrind. Some challenges faced along the way include lack of technical skills and expertise, lack of credible mentorship in international businesses, and lack of investment capital, which lead to failed startups. The positive side includes getting global attention, funding availability, and having an innovative infrastructure.

Uploaded 05-07-18

Speaker: Elisa Chiu, CEO, Anchor Taiwan

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Stanford Webinar - Project Innovation through Design Thinking

Thu, 22 Oct 2026 17:32:00 -0000

Increasingly, project managers are being asked to lead their teams to innovate new products, processes, and services for internal or external customers. In this webinar, you will learn how to accelerate innovation with design thinking and be introduced to some of the key challenges and opportunities in this new and exciting space.

Uploaded 26-04-12

Learn more about professional programs offered by the Stanford Center for Professional Development by visiting http://scpd.stanford.edu/programs/professional-certificates.

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Stanford Seminar - Silicon Valley and The U.S. Government: Shield AI’s Brandon Tseng

Thu, 22 Oct 2026 17:00:00 -0000

April 29, 2024
Brandon Tseng, Shield AI

Shield AI’s Co-founder and President Brandon Tseng is interviewed by Ernestine Fu Mak with special remarks from Joe Felter. Shield AI was founded to bring the best of AI and autonomy technology to the DoD and our allies to protect service members and civilians.

Uploaded 10-07-24

—

This session is part of the “Silicon Valley and The U.S. Government” speaker series co-led by Ernestine Fu Mak, Steve Blank, Joe Felter, Eric Volmar, and Paul Kwan.

Silicon Valley collaborations with the U.S. government have led to some of the most important technologies in our society. Driven by visionary leaders, these collaborations have overcome regulatory, business, and technological challenges to achieve exceptional innovation. The results of these innovations have addressed national issues and societal challenges.

As government and industry collaboration is more important than ever to accelerate private sector innovation and shape the future of technology, join us in hearing the personal accounts of technology experts, business executives, and public service leaders who have paved the way with these types of innovations.

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Stanford Seminar- Advancing Sustainability Development Goals (SDGs) in South Asia

Thu, 22 Oct 2026 16:00:00 -0000

Speakers: Rikin Gandhi, Radhika Shah

This talk focuses on the challenges social entrepreneurs have worked on, such as scaling and change adaptation. Social change frames the global normative framework and tech becomes a major player in the global economy, as it becomes a game-changer both positively and negatively. The younger generation demands a positive change which impacts technological infrastructure. Sustainability Development Goals (SDGs) play an important part acting as a framework in tech in South Asia as place for collaboration and connects between farmers and philanthropists.

Uploaded 05-07-18

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Course Overview: Mastering Generative AI for Product Innovation

Thu, 22 Oct 2026 15:00:00 -0000

Now open for enrollment! https://online.stanford.edu/courses/xapro210-mastering-generative-ai-product-innovation?utm_source=youtube&utm_medium=social

As a product leader or founder, you might be feeling pressure to harness the power of generative AI.

Uploaded 10-07-24

But where do you start? And which strategies will drive product success?

In this course, machine learning expert and principal PM Aditya Challapally, along with Stanford Professor Edison Tse, will help you navigate generative AI, from opportunity assessment to implementation. You’ll learn proven strategies for identifying the right problems to solve with generative AI, selecting the best AI models, and evaluating associated risks.

Walk away with a clear plan and the confidence to implement it.

- Explore the technology behind generative AI
- Evaluate opportunities for utilizing genAI to address user pain points
- Develop a strategic framework for integrating genAI into your products
- Enhance productivity and creativity by leveraging genAI in workflows
- Integrate genAI into your product design and development processes

#generativeai

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Stanford Seminar- Taraxa.io: A Globalized Blockchain Protocol Startup

Thu, 22 Oct 2026 14:00:00 -0000

Speaker: Steven Pu

The vast majority of IoT investments have been investments in hardware infrastructure. Some of the key obstacles involved with IoT and data sharing include data security, vendor risk, and high cost. Breaking the barriers with data sharing, will create more data sharing among consumers and will help create business and use cases.

Uploaded 05-07-18

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 10: GMM (EM), PCA

Thu, 22 Oct 2026 13:32:00 -0000

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Stanford Seminar - Silicon Valley and The U.S. Government: In-Q-Tel’s George Hoyem

Thu, 22 Oct 2026 13:00:00 -0000

April 22, 2024
George Hoyem, In-Q-Tel

In-Q-Tel’s EVP of Investments George Hoyem is interviewed by Ernestine Fu Mak with special remarks from Joe Felter and Steve Blank. In-Q-Tel’s Managing Partner A.J. Bertone also participates in the conversation. Founded in 1999, In-Q-Tel is the non-profit strategic investor that accelerates the development and delivery of cutting-edge technologies to U.S. government agencies and allied nations.

Uploaded 10-07-24

—

This session is part of the “Silicon Valley and The U.S. Government” speaker series co-led by Ernestine Fu Mak, Steve Blank, Joe Felter, Eric Volmar, and Paul Kwan.

Silicon Valley collaborations with the U.S. government have led to some of the most important technologies in our society. Driven by visionary leaders, these collaborations have overcome regulatory, business, and technological challenges to achieve exceptional innovation. The results of these innovations have addressed national issues and societal challenges.

As government and industry collaboration is more important than ever to accelerate private sector innovation and shape the future of technology, join us in hearing the personal accounts of technology experts, business executives, and public service leaders who have paved the way with these types of innovations.

Learn more about your ad choices. Visit megaphone.fm/adchoices

Stanford Seminar- How the Chinese Government Fabricates Social Media Posts for Strategic Distraction

Thu, 22 Oct 2026 12:00:00 -0000

Speaker: Jen Pan, Stanford University

The speaker examines how the Chinese regime controls information online and how the government engages in censorship. The government uses different types of filters to control what content is viewed by the public and what content is removed and unseen. "Internet commentators" are prominent players in media fabrication as they post social media comments as if they are those of the general public, when in reality they are paid comments. Through this strategic distraction, the Chinese government has control of choosing what content to view audience members and checks for algorithms dealing with different issues and managing public opinion.

Uploaded 05-07-18

0:00 Introduction
1:23 China's Massive Propaganda Effort
4:17 Goal of China's 50c Party
6:27 First Systematic Evidence on China's 50c Party
8:52 Zhanggong Internet Propaganda Operations
15:16 Categories and Analyses of 50c Party Posts
16:10 Content of Leaked and Predicted 50c Party Posts
24:13 Survey Validation of 50c Party Membership Predictions
26:14 Validation by the Chinese Government
28:53 Theoretical Implications

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Stanford Seminar - Silicon Valley and The U.S. Government: Applied Intuition’s Qasar Younis

Thu, 22 Oct 2026 11:00:00 -0000

April 15, 2024
Qasar Younis, Applied Intuition

Qasar Younis, co-founder and CEO of Applied Intuition, is interviewed by Paul Kwan with special remarks from Joe Felter and Steve Blank. Applied Intuition is a vehicle software supplier that accelerates the adoption of safe and intelligent machines worldwide.

Uploaded 10-07-24

—

This session is part of the “Silicon Valley and The U.S. Government” speaker series co-led by Ernestine Fu Mak, Steve Blank, Joe Felter, Eric Volmar, and Paul Kwan.

Silicon Valley collaborations with the U.S. government have led to some of the most important technologies in our society. Driven by visionary leaders, these collaborations have overcome regulatory, business, and technological challenges to achieve exceptional innovation. The results of these innovations have addressed national issues and societal challenges.

As government and industry collaboration is more important than ever to accelerate private sector innovation and shape the future of technology, join us in hearing the personal accounts of technology experts, business executives, and public service leaders who have paved the way.

Learn more about your ad choices. Visit megaphone.fm/adchoices

Stanford Seminar - Protecting Patient Privacy in Genomic Analysis

Thu, 22 Oct 2026 10:00:00 -0000

One of the challenges for patients with rare diseases is the lack of local data to use for analysis. As a result, patients are having to rely on 3rd party facilities to carry out the computations based on data collected around the globe. But due to patient privacy concerns, as well as heavy legal and regulatory barriers, these facilities are often prevented from sharing vital data which could help save lives.

Uploaded 13-07-18

Join David Wu as he discusses how modern cryptographic tools can be leveraged for genome privacy.

Screen reader support enabled.

This presentation is brought to you by the Stanford Computer Forum and the Stanford Advanced Computer Security Program. If you would like information on how to join the forum and attend the next meeting, see our website: http://forum.stanford.edu/about/howtojoin.php

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John Mitchell on Stanford and the Computer Security Field

Thu, 22 Oct 2026 09:32:00 -0000

Dr. John Mitchell talks about the Stanford experience, his approach to research and teaching, and opportunities in the field of computer security. Dr. Mitchell has research interests in Computer security: access control, network protocols, and software system security; Programming languages, type systems, object systems, and formal methods; Applications of mathematical logic to computer science.

Uploaded 01-05-12

Learn more about the Advanced Computer Security certificate program: http://computersecurity.stanford.edu/

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Stanford Seminar - Evaluating and Designing Computing Systems for the Future of Work

Thu, 22 Oct 2026 09:00:00 -0000

May 31, 2024
Hancheng Cao, Stanford University

From collaborative software to generative AI, computing technologies are redefining the way we work, communicate and collaborate. Yet with the growing complexities of computing platforms, it becomes increasingly challenging to foresee their impacts on human behavior, leading to not only poor user experience but also problematic applications that mirror and amplify societal issues. How can we better understand machine behavior and machine-mediated user behavior over computing platforms? How can we build applications that align with our needs and values with emerging computing technologies? My research aims to answer these questions through developing novel empirical measurements, technical methods and design. In this talk, I will present my work demonstrating this approach in the future of work context, where I have established data-driven, AI-powered and human-centered methods to understand, evaluate and design information systems at the workplace. I will present an analysis of remote meeting experience through mining millions of meetings, a study on how an AI algorithm can be built to predict team fracture, and a development and evaluation study on a generative AI-based scientific feedback system for researchers. These projects exemplify the opportunities to leverage computation to better understand, support and augment work practices.

Uploaded 08-07-24

About the speaker:
Hancheng Cao is a final year PhD candidate in computer science (with a PhD minor in management science and engineering) at Stanford University. He works in the field of computational social science and human computer interaction, where he mines large-scale data, develops algorithms and builds systems to study human behavior. Recognized as a Stanford Interdisciplinary Graduate Fellow, he has published 30 academic papers across fields, with three works he led recognized as Best Paper (CHI 2023) or Honorable Mention (CSCW 2020, CHI 2021) awards. His research has also appeared in leading social science journals (e.g. American Sociological Review). His research has been widely covered in the media, including Wired, Forbes, New Scientist, TED among others.

More about the course can be found here: https://hci.stanford.edu/seminar/

View the entire CS547 Stanford Human-Computer Interaction Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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Creating a Culture of Creativity: Conquering Fear and the Internal Censor

Thu, 22 Oct 2026 08:00:00 -0000

In this webinar, Stanford Professor Bill Burnett will take a deeper look into creativity, including the current neuroscience that attempts to explain creativity, what blocks individuals from being creative, and how you can become more confident in your creativity.

Uploaded 25-07-18

This presentation gives you a taste of what will be taught during the Innovation Masters Series being held this September 12-14, 2018 at Stanford.

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Stanford Webinar - Epidemiology and Clinical Research: Info Session, Rita Popat and Kristin Sainani

Thu, 22 Oct 2026 07:00:00 -0000

Stanford professors Rita Popat and Kristin Sainani introduce the new graduate certificate in Epidemiology and Clinical Research (https://stanford.io/2MLDKqV), now offered by the Epidemiology division of the Health Research and Policy Department at Stanford School of Medicine.

Uploaded 10-08-18

In this session, professors Popat and Sainani:
-Introduce current research and emerging trends in the field
-Discuss classes in this program, expected outcomes and required skills
-Respond to audience questions

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Stanford Online - New Developments in Cryptocurrencies and Blockchain Technologies - Dan Boneh

Thu, 22 Oct 2026 06:00:00 -0000

Virtual currency has the potential to revolutionize the way we make payments, but using these digital assets requires special data protection. Join Professor Dan Boneh as he discusses new developments in cryptocurrency and computer security and analyzes new systems that strengthen blockchain capabilities.

Uploaded 22-08-18

Hear more from Dan Boneh in our online cyber security programs: https://stanford.io/2nD5rXR

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 9: K-Means and GMM (non-EM)

Thu, 22 Oct 2026 05:32:00 -0000

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Stanford Seminar - The Case for Learned Index Structures

Thu, 22 Oct 2026 05:00:00 -0000

EE380: Computer Systems Colloquium Seminar
The Case for Learned Index Structures
Speaker: Alex Beutel and Ed Chi, Google

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this talk, we take this premise and explain how existing database index structures can be replaced with other types of models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of indexed data and use this signal to effectively predict the position or existence of records. We offer theoretical analysis under which conditions learned indexes outperform traditional index structures and we will delve into the challenges in designing learned index structures. Through addressing these challenges, our initial results show that learned indexes are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. Finally, we will discuss the broader implications of learned indexes on database design and future directions for the ML for Database Systems research.

Uploaded 18-10-18

About the Speaker:

Alex Beutel is a Senior Research Scientist in the Google Brain team working on neural recommendation, fairness in machine learning, and ML for Systems. He received his Ph.D. in 2016 from Carnegie Mellon University's Computer Science Department, and previously received his B.S. from Duke University in computer science and physics. His Ph.D. thesis on large-scale user behavior modeling, covering recommender systems, fraud detection, and scalable machine learning, was given the SIGKDD 2017 Doctoral Dissertation Award Runner-Up. He received the Best Paper Award at KDD 2016 and ACM GIS 2010, was a finalist for best paper in KDD 2014 and ASONAM 2012, and was awarded the Facebook Fellowship in 2013 and the NSF Graduate Research Fellowship in 2011. More details can be found at http://alexbeutel.com.

Ed H. Chi is a Principal Scientist at Google, leading machine learning research focusing on neural modeling and recommendation systems in the Google Brain team. He has launched significant improvements for YouTube, Google Play Store and Google+. With 39 patents and over 110 research articles, he is known for research on user behavior in web and social media. Prior to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group, where he led the team in understanding how social systems help groups of people to remember, think and reason.

Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Recognized as an ACM Distinguished Scientist and elected into the CHI Academy, he has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. Recognized recently with a 20-year Test of Time award for research in information visualization, Ed is also an avid swimmer, photographer and snowboarder in his spare time, and has a blackbelt in Taekwondo.

For more information about this seminar and its speaker, you can visit https://ee380.stanford.edu/Abstracts/181017.html

Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum.

Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Learn more: http://bit.ly/WinYX5

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Stanford CS236: Deep Generative Models I 2023 I Lecture 16 - Score Based Diffusion Models

Thu, 22 Oct 2026 04:00:00 -0000

For more information about Stanford's Artificial Intelligence programs, visit: https://stanford.io/ai

Uploaded 06-05-24

To follow along with the course, visit the course website:
https://deepgenerativemodels.github.io/

Stefano Ermon
Associate Professor of Computer Science, Stanford University
https://cs.stanford.edu/~ermon/

Learn more about the online course and how to enroll: https://online.stanford.edu/courses/cs236-deep-generative-models

To view all online courses and programs offered by Stanford, visit: https://online.stanford.edu/

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Webinar: Regulating at the Speed of AI with Francis Fukuyama and Nate Persily

Thu, 22 Oct 2026 03:00:00 -0000

Learn more about about AI, Power and the New Rules of Global Strategy - https://online.stanford.edu/courses/xfsi210-ai-power-and-new-rules-global-strategy

Anxiety about the increasing capabilities of AI systems has reached unprecedented levels in the past few months. Concerns about offensive cyber capabilities led the administration to suspend the deployment of an AI model for the first time. Powerful open models from China raise the spectre of a technology that is widely diffused but poorly controlled. News about models escaping company sandboxes to hack external websites has led many to update their p(doom) levels. Employees of the major AI labs are sounding the alarm about loss of control and existential risks from the newest AI models, and some political actors and technologists are calling for a pause in AI development. The topic of AI governance is, it is fair to say, having its moment.

Uploaded 20-10-26

In this webinar, Francis Fukuyama, Olivier Nomellini Senior Fellow at Stanford’s Freeman Spogli Institute for International Studies, interviews Nathaniel Persily, James B. McClatchy Professor of Law at Stanford Law School and co-director of the Stanford Law AI Initiative. Their conversation will examine the need for AI governance and the challenges confronting both governments and firms as they try to build in controls to chart a safe AI future.

You Will Learn:
- What obstacles stand in the way of effective AI governance
- How AI regulation and governance differs from other technological settings, such as social media
- How countries around the world have approached the topic of AI governance and what we can learn from their efforts

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | EKF Localization

Thu, 22 Oct 2026 02:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 17 — EKF Localization

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 21-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Computer Security - Stanford Professor Dan Boneh on its applications and its future

Thu, 22 Oct 2026 01:32:00 -0000

Dr. Dan Boneh discusses the field of computer security, its applications, and its future. Dr. Boneh's main research area is applied cryptography and network security. His focus is on building security mechanisms that are easy to use and deploy. He developed new mechanisms for improving web security, file system security, and copyright protection. Dr. Boneh contributed to the security and performance of the RSA cryptosystem and contributed to the study of cryptographic watermarking.

Uploaded 01-05-12

Learn more about the Advanced Computer Security certificate program: http://computersecurity.stanford.edu/

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Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Parametric Filtering

Thu, 22 Oct 2026 01:00:00 -0000

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

Lecture 15 — Parametric Filtering (KF, EKF, UKF)

Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video.

Uploaded 20-10-26

Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com

Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I

Speaker: Prof. Marco Pavone

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation.

Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk

#StanfordOnline #RobotAutonomy #Robotics #PhysicalAI

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Stanford Seminar: Neuromorphic Chips: Addressing the Nanostransistor Challenge

Thu, 22 Oct 2026 00:00:00 -0000

EE380: Computer Systems Colloquium Seminar
Neuromorphic Chips: Addressing the Nanostransistor Challenge by Combining Analog Computation with Digital Communication
Speaker: Kwabena Boahen, Stanford University

Uploaded 06-04-17

As transistors shrink to nanoscale dimensions, trapped electrons--blocking "lanes" of electron traffic--are making it difficult for digital computers to work. In stark contrast, the brain works fine with single-lane nanoscale devices that are intermittently blocked (ion channels). Conjecturing that it achieves error-tolerance by combining analog dendritic computation with digital axonal communication, neuromorphic engineers (neuromorphs) began emulating dendrites with subthreshold analog circuits and axons with asynchronous digital circuits in the mid-1980s. Three decades in, they achieved a consequential scale with Neurogrid, the first neuromorphic system with billions of synaptic connections. Neuromorphs then tackled the challenge of mapping arbitrary computations onto neuromorphic chips in a manner robust to lanes intermittently--or even permanently--blocked by trapped electrons. Having demonstrated scalability and programmability, they now seek to encode continuous signals with spike trains in a manner that promises greater energy efficiency than all-analog or all-digital computing across a five-decade precision range.

About the Speaker:
Kwabena Boahen is a Professor of Bioengineering and Electrical Engineering at Stanford University, where he directs the Brains in Silicon Lab. He is a neuromorphic engineer who is using silicon integrated circuits to emulate the way neurons compute, and linking the seemingly disparate fields of electronics and computer science with neurobiology and medicine. His lab developed Neurogrid, a specialized hardware platform created at Stanford that enables the cortex's inner workings to be simulated in real time--something outside the reach of even the fastest supercomputers. His interest in neural nets developed soon after he left his native Ghana to pursue undergraduate studies in Electrical and Computer Engineering at Johns Hopkins University, Baltimore, in 1985. He went on to earn a doctorate in Computation and Neural Systems at the California Institute of Technology in 1997. From 1997 to 2005 he was on the faculty of University of Pennsylvania, Philadelphia PA. With over ninety publications to his name, including a cover story in the May 2005 issue of Scientific American, his scholarship has been recognized by several distinguished honors, including the National Institute of Health Director's Pioneer Award in 2006. In 2016, he was named a fellow of the Institute of Electrical and Electronic Engineers and of the American Institute for Medical and Biological Engineering. His 2007 TED talk, A Computer that Works like the Brain, has been viewed over half-a-million times.

For more information about this seminar and its speaker, you can visit http://ee380.stanford.edu/Abstracts/170405.html

Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum.

Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Learn more: http://bit.ly/WinYX5

0:00 Introduction
0:18 Acknowledgments
0:57 A dollar bought more and more transistors until 2014
5:55 Beware of potholes!
6:30 Accidents happen!
16:32 Combining digital communication with analog computation
23:43 Digital versus Analog versus Neuromorphic
25:56 Neuromorphs' Five-Point "Secret" Master Plan 1. Implement dendritic computation with subthreshold analog
27:37 Overlapping dendrites eliminate axon's terminal branches
30:31 Hierarchical

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Stanford Seminar - Integrating interactive devices with the user's body

Wed, 21 Oct 2026 23:00:00 -0000

February 9, 2024
Pedro Lopes, University of Chicago

The main question that drives my research is: what is the next interface paradigm that supersedes wearable devices? I argue that the new paradigm is one in which interactive devices will integrate with the user s body.

Uploaded 01-03-24

My way of engineering devices that intentionally borrow parts of the user s biology puts forward a new generation of miniaturized devices; allowing us to circumvent traditional physical constraints. For instance, in the case of my devices based on electrical muscle stimulation, they demonstrate how to circumvent the constraints imposed by the size of motors used in traditional haptic devices (e.g., robotic exoskeletons). Taking this further, we can apply this integrated approach to other modalities. For instance, we engineered a device that delivers chemicals to the user to create temperature sensations, without the need to rely on cumbersome thermal actuators, such as air conditioners or heaters. My approach to miniaturizing devices is especially useful to advance mobile interactions, such as in virtual or augmented reality, where users have a desire to remain untethered and free.

Integrating devices with the users body allows to give users new physical abilities. For example, we have engineered a device that allows users to locate odor sources by smelling in stereo as well as a device that physically accelerates the users reaction time using muscle stimulation, which allows users to steer to safety or even catch a falling object that they would normally miss.

While this integration can offer many benefits (e.g., faster reaction time, realistic simulations in VR/AR, or faster skill acquisition), it also requires tackling new challenges, such as the question of agency: do we feel in control when our body is integrated with an interface? Together with our colleagues in neuroscience, we have been measuring how our brain encodes agency to improve the design of this new type of integrated interface. We found that, even in the extreme case of interfaces that electrically control the users muscles, it is possible to improve the sense of agency. More importantly, we found that it is only by preserving the user's sense of agency that these integrated devices provide benefits even after the user takes them out.

About the speaker:
Pedro Lopes is an Associate Professor in Computer Science at the University of Chicago. Pedro focuses on integrating interfaces with the human body exploring the interface paradigm that supersedes wearables. These include: muscle stimulation wearables that allow users to manipulate tools they have never seen before or that accelerate reaction time, or a device that leverages the smell to create an illusion of temperature. Pedro s work has received several academic awards, such as six CHI/UIST Best Papers, the Sloan Fellowship and the NSF CAREER award, and captured the interest of the public (e.g., New York Times, exhibited at Ars Electronica, etc.; more: https://lab.plopes.org).

More about the course can be found here: https://hci.stanford.edu/seminar/

View the entire CS547 Stanford Human-Computer Interaction Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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Startup Mechanics - Kirsty Nathoo, Partner and CFO at Y Combinator - Stanford CS183F

Wed, 21 Oct 2026 22:00:00 -0000

Kirsty Nathoo is a Partner and the CFO at Y Combinator. She has worked with thousands of companies through all stages of their life; from incorporation to hiring to fundraising. In this lecture, Kirsty will talk about Startup Mechanics. https://goo.gl/amhNoK

Uploaded 10-04-17

Explore all Stanford Online Courses and Programs: https://online.stanford.edu/explore

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 8: Neural Networks 2 (Backprop)

Wed, 21 Oct 2026 21:32:00 -0000

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Stanford Seminar - Embodied Intelligence for Extreme Environments

Wed, 21 Oct 2026 21:00:00 -0000

February 9, 2024
Paul Glick, JPL Robotics

Extreme environments penalize the sensing, actuation, computation, and communication that robotic systems rely upon. Compounding this challenge is the fact that these remote locations are often some of the least mapped areas on and beyond our planet. Structured compliance offers a pathway for robots to adapt to their environment at the mechanical level while preserving the strength to support payload mass and forceful interactions. This theme is explored across projects that include gripping in space, exploration of coral reefs, data acquisition under ice, and a cold-operable robotic arm.

Uploaded 29-02-24

About the speaker: https://www-robotics.jpl.nasa.gov/who-we-are/people/paul-glick/

More about the course can be found here: https://stanfordasl.github.io/robotics_seminar/

View the entire AA289 Stanford Robotics and Autonomous Systems Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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Stanford EE259 I Radar antenna designs and characteristics I 2023 I Lecture 11

Wed, 21 Oct 2026 20:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 26-01-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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How to Get Ideas and Measure - Stewart Butterfield of Slack and Adam D'Angelo of Quora - Stanford CS183F

Wed, 21 Oct 2026 19:00:00 -0000

In this lecture, we're joined by Stewart Butterfield, founder and CEO of Slack, and Adam D'Angelo, founder and CEO of Quora. Sam and Stewart will explore the process of getting an idea, followed by Adam on tracking metrics for your company.

Uploaded 12-04-17

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Kevin Blaine talks about Stanford's Medical Statistics Program

Wed, 21 Oct 2026 18:00:00 -0000

Learn more: https://online.stanford.edu/programs/medical-statistics-program

The fully online, 3-course Medical Statistics Program will give you the tools you need to analyze, interpret, and visualize data. With examples and real-world case studies, you'll learn how to effectively apply foundational statistical knowledge to your career.

Uploaded 29-02-24

#medicaleducation

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Bradford Parkinson "GPS for Humanity" - Stanford Engineering Hero Lecture

Wed, 21 Oct 2026 17:32:00 -0000

http://scpd.stanford.edu/ More than anything else, GPS has become the United States' gift to humanity. Cell phones rely on GPS for timing. Ship and aircraft carry multiple GPS receivers to provide positioning information. Other applications range from earth movement to disease tracking to search and rescue. Dr. Bradford Parkinson, chief GPS architect and Stanford Professor Emeritus of Aeronautics and Astronomics, will describe the origins and applications of GPS and explore its future.

Uploaded 02-05-12

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Stanford Seminar: Asia Entrepreneurship Update 2017 - Current Ecosystem Trends

Wed, 21 Oct 2026 17:00:00 -0000

EE402T: Entrepreneurship in Asian High-Tech Industries
Asia Entrepreneurship Update 2017: Current Ecosystem Trends
Speaker: Richard Dasher, Stanford University

Uploaded 13-04-17

Dr. Richard Dasher, Director or the US-Asia Technology Management Center, introduces current attitudes toward entrepreneurship and participation in entrepreneurial activities in major Asia economies, before discussing ecosystem trends such as investment data, career patterns, and the growth of other aspects of supporting infrastructure.

About the Speaker:
Dr. Dasher has directed the US-ATMC since 1994. He serves on advisory and review boards of major government S and T funding programs and research institutes in Canada, Japan, and Thailand. He is an advisor to incubators, VC funds, innovation initiatives, and startup companies in China, Japan, S. Korea, and Silicon Valley. Dr. Dasher is the first non-Japanese person ever named to the senior governance of a Japanese national university, serving a term on the Board of Directors of Tohoku University.

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Stanford CS25: V3 I Retrieval Augmented Language Models

Wed, 21 Oct 2026 16:00:00 -0000

December 5, 2023
Douwe Kiela, Contextual AI

Language models have led to amazing progress, but they also have important shortcomings. One solution for many of these shortcomings is retrieval augmentation. I will introduce the topic, survey recent literature on retrieval augmented language models and finish with some of the main open questions.

Uploaded 25-01-24

More about the course can be found here: https://web.stanford.edu/class/cs25/

View the entire CS25 Transformers United playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM

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Stanford Webinar - A People-First Strategy for Digital Transformation

Wed, 21 Oct 2026 15:00:00 -0000

Learn more on this topic: https://online.stanford.edu/courses/xdgt115-human-centered-design-digital-transformation?utm_source=youtube

As your team is being driven to quickly create new digital solutions, it’s easy to get caught up in the technology and lose sight of the people interacting with this technology. At the core, digital transformation is about problem-solving. And your new innovations, automations, and processes will only be successful if they make the lives of your customers or colleagues easier.

Uploaded 22-02-24

In this webinar, Julie Stanford shows you how to view digital transformation with a human-centered lens and use problem-solving techniques to plan and execute successful digital transformation projects.

#digitaltransformation #humancentereddesign

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Stanford Seminar: Living in Information Everywhere

Wed, 21 Oct 2026 14:00:00 -0000

EE380: Computer Systems Colloquium Seminar
Living in Information Everywhere
Speaker: Quentin Hardy, Head of Editorial Google Cloud

Uploaded 13-04-17

The term cloud computing increasingly describes not just the technology of large networked data centers, but is a proxy term for the unification of smartphones, apps, IoT, Big Data, and Artificial Intelligence -- in effect, the deployment of computational intelligence to virtually every point on the planet. One may view this as a fulfillment of Moore's Law, or the start of decades-long project that is likely to reshape civilization. As someone who both covers this topic and has been profoundly affected by it, I will speak about the technology, and what historical parallels tell us about the likely impact.

About the Speaker:
Quentin Hardy is Head of Editorial at Google Cloud.

As the head of editorial at Google Cloud, Quentin Hardy writes about the technology and impact of cloud computing, both inside Google and for other publications. He was previously Deputy Technology Editor at The New York Times, National Editor at Forbes, and a reporter at The Wall Street Journal. He frequently appears on television and moderates panels at The Computer History Museum and The World Affairs Council. His article, "Hope and Profit in Africa" received an Overseas Press Club Citation. He has degrees from Kenyon College, The School of Oriental and African Studies, and Columbia University.

For more information about this seminar and its speaker, you can visit http://ee380.stanford.edu/Abstracts/170412.html

Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum.

Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week.

Learn more: http://bit.ly/WinYX5

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 7: Neural Networks 1 (Architecture)

Wed, 21 Oct 2026 13:32:00 -0000

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Stanford Seminar - From Flat to Phantasmal

Wed, 21 Oct 2026 13:00:00 -0000

From Flat to Phantasmal: How Spatial Computing Advancements Enhance Contextual and Creative User Experiences

February 2, 2024
Jasmine Roberts
Microsoft Research

"What tools and systems will streamline the spatial computing creation pipeline?"

Uploaded 20-02-24

Advances in semantic understanding, object recognition, depth detection, and machine learning are needed to create tools that empower developers to prototype in a spatial context instead of current pipelines that require creators to transition between 3D creator tools and 3D game engines.

"How will we personalize experiences to accommodate a wide range of abilities?"

By understanding the context and environment in which people are using applications, we can dynamically modify the scale and location of interactive content relative to a person's needs. A combination of voice controls, wearables, and configurable UI are needed to adapt the application to fit each individual's preferences. Hard-coded components interfere with the ability to scale testing and reduce the amount of actionable feedback from a variety of demographics.

"How will the diverse set of capabilities provided by adjacent technologies advance mixed-reality?"

How can we use physiological, gestural, and biometric data to both determine the effectiveness of VR and to drive immersive experiences? How will the blending of quantitative data, informed with insights from qualitative interpretation, emphasize user-determined creativity?

About the speaker:

Jasmine Roberts is a software engineer and research design prototyper. Contributing to Google's ARCore Depth API, PlayStation VR 2's eye-tracking and interface design, and Unity MARS (Mixed and Augmented Reality Studio), Jasmine has played a pivotal role in shaping cutting-edge mixed-reality technologies. Notably, in her role as part of the Microsoft Research team, she is actively involved in the initial investigations of generative AI within game engines and mixed-reality environments.

Jasmine has been acknowledged by the industry for her dedication to practical application, earning distinctions such as the Oculus Launch Pad Fellowship, Snap's AR Creator Residency Fellowship, Mozilla XR Studio Fellowship, Intel Scholar Award, and Epic Games' Unreal Engine Fellowship. In 2020, she was recognized as Forbes 30 Under 30 for Games.

More about the course can be found here: https://hci.stanford.edu/seminar/

View the entire CS547 Stanford Human-Computer Interaction Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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Stanford EE259 I Sonar ranging and bearing angle estimation I 2023 I Lecture 9

Wed, 21 Oct 2026 12:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 24-01-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Live Office Hours with Yuri Sagalov and Sam Altman - Stanford CS183F: Startup School

Wed, 21 Oct 2026 11:00:00 -0000

In this lecture, Sam Altman and Yuri Sagalov, CEO of Amium, hold Live Office Hours with three startups: MoonlightWork, Canny and Tuml. They'll discuss their ideas and their thought processes around building their companies.

Uploaded 17-04-17

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Stanford Seminar - Flying Robots: Exploring Hybrid Locomotion and Physical Interaction

Wed, 21 Oct 2026 10:00:00 -0000

January 26, 2024
Dr. Raphael Zufferey of EPFL

Autonomous flying robots have become widespread in recent years, yet their capability to interact with the environment remains limited. Moving in multiple fluids is one of the great challenges of mobile robotics, and carries great potential for application in biological and environmental studies. In particular, hybrid locomotion provides the means to cross large distances and obstacles or even change from one body of water to another thanks to flight. At the same time, they are capable of operating underwater, collecting samples, video and aquatic metrics. However, the challenges of operating in both air and water are complex. In this talk, we will introduce these challenges and cover several research solutions which aim to adress these in different modalities, depending on locomotion and objectives. Bio-inspiration plays a crucial role in these solutions, and the topic of flapping flight in the context of physical interaction will also be presented.

Uploaded 13-02-24

About the speaker: https://raphzufferey.com/

More about the course can be found here: https://stanfordasl.github.io/robotics_seminar/

View the entire AA289 Stanford Robotics and Autonomous Systems Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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d.thinking - Big Questions

Wed, 21 Oct 2026 09:32:00 -0000

Stanford's Bill Burnett, executive director of the Design Program, explores the big questions that face those making important decisions and how design thinking can help you tackle those challenges.

Uploaded 02-05-12

Learn more about the Innovation Masters Series: http://scpd.stanford.edu/design

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Stanford Seminar: Solve the Problem You Have

Wed, 21 Oct 2026 09:00:00 -0000

Tracy Young, Plan Grid
Doug Leone, Sequoia

Entrepreneur Tracy Young and Doug Leone, global managing partner at Sequoia, discuss the nature of a harmonious relationship between a startup’s founders and the VC firm investing in them. Young is co-founder and CEO of PlanGrid, which allows construction managers to oversee projects via their device. She and Leone speak with Toby Corey, a lecturer in Stanford University’s School of Engineering.

Uploaded 19-04-17

Prior to PlanGrid, Tracy Young was a construction field engineer and helped build and deliver several hospitals and heavy civil projects. She also spent time managing coordination of MEPF systems and bidding projects. She jokes that, with PlanGrid, she went from working for one superintendent to hundreds of thousands of them.

Leone emigrated from Italy with his family aged 11, and they settled in Mount Vernon, New York. Leone earned a BS in mechanical engineering from Cornell University in 1979, an MS in industrial engineering from Columbia University in 1986, and an MS in management from the MIT Sloan School of Management in 1988. Leone joined Sequoia Capital in 1988, and became a managing partner in 1996. He was ranked in the top 10 investors in the United States by Forbes Magazine. He led Sequoia's international expansion into China and India. Leone, who previously worked for Hewlett-Packard, Prime Computer, and Sun Microsystems, earned a mechanical engineering degree from Cornell University. He was responsible for investments including ServiceNow, Aruba, Meraki, Rackspace, Netezza, Arbor/Hyperion, RingCentral and MedExpress. He sits on the board of PlanGrid, NuBank, Medallia, ZirMed, ActionIQ, Numerify, Birst and Lattice Engines.

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Seminar - Reducing Misinformation Sharing at Scale Using Digital Accuracy Prompt Ads

Wed, 21 Oct 2026 08:00:00 -0000

January 26, 2024
David Rand of MIT

Interventions to reduce misinformation sharing have been a major focus in recent years. Developing "content-neutral" interventions that do not require specific fact-checks or warnings related to individual false claims is particularly important in developing scalable solutions. Here, we provide the first evaluations of a content-neutral intervention to reduce misinformation sharing that are conducted at scale in the field. Specifically, across two large-scale on-platform randomized control trials, one on Meta's Facebook and the other on Twitter, we find that simple messages reminding people to think about accuracy--delivered to large numbers of users using digital advertisements--reduce misinformation sharing, with effect sizes on par with what is typically observed in digital advertising experiments. These findings suggest that content-neutral interventions which prompt users to consider accuracy have the potential to complement existing content-specific interventions in reducing the spread of misinformation online.

Uploaded 12-02-24

About the speaker:
David Rand is the Erwin H. Schell Professor and Professor of Management Science and Brain and Cognitive Sciences at MIT. Bridging the fields of cognitive science, behavioral economics, and social psychology, David's research combines behavioral experiments and online/field studies with mathematical/computational models to understand human decision-making. His work focuses on illuminating why people believe and share misinformation and "fake news"; understanding political psychology and polarization; and promoting human cooperation. He has published over 200 articles in peer-reviewed journals such Nature, Science, PNAS, the American Economic Review, Psychological Science, CHI, CSCW, Management Science, New England Journal of Medicine, and the American Journal of Political Science, and his work has received widespread media attention. David regularly advises technology companies such as Google, Meta/Facebook, and TikTok in their efforts to combat misinformation, and has provided testimony about misinformation to the US and UK governments. He has also written for popular press outlets including the New York Times, Wired, and New Scientist. He was named to Wired magazine's Smart List 2012 of "50 people who will change the world," chosen as a 2012 Pop!Tech Science Fellow, awarded the 2015 Arthur Greer Memorial Prize for Outstanding Scholarly Research, chosen as fact-checking researcher of the year in 2017 by the Poyner Institute's International Fact-Checking Network, awarded the 2020 FABBS Early Career Impact Award from the Society for Judgment and Decision Making, and selected as a 2021 Best 40-Under-40 Business School Professor by Poets and Quants. Papers he has coauthored have been awarded Best Paper of the Year in Experimental Economics, Social Cognition, and Political Methodology.

More about the course can be found here: https://hci.stanford.edu/seminar/

View the entire CS547 Stanford Human-Computer Interaction Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD

► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore

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Stanford Seminar: Writing Software Faster with Crowds, Anand Kulkarni of Crowbotics

Wed, 21 Oct 2026 07:00:00 -0000

Anand Kulkarni
Crowbotics

Crowd computing systems are now capable of doing more sophisticated work than ever before, accelerating the pace of creative work such as creating movies, carrying out research, and writing stories. How can crowds be used to accelerate software creation? This is a complex problem, requiring good methods for expert selection, program synthesis and collaboration. Well discuss strategies for accelerating software creation using crowds of geographically-diverse communities of software engineers, as well as the cultural and technical challenges that emerge.

Uploaded 19-04-17

Anand is founder of Crowdbotics, a startup using crowdsourcing and machine intelligence to accelerate the process of software development. Crowdbotics brings developers worldwide into a global community of software engineers that collaborate 1-1 with technology companies. Prior to that, Anand was Chief Scientist of LeadGenius, a Y Combinator-backed startup using human computation and deep learning to automate account-based marketing (ABM). Anand has been named as one of Forbes Magazine's "30 under 30" Top 30 Entrepreneurs Under 30. Anand has published in ACM, AAAI and IEEE magazines, journals, and conferences. Anand previously held a National Science Foundation graduate research fellowship in mathematics. He holds degrees in Industrial Engineering and Operations Research, Mathematics, and Physics from UC Berkeley.

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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How to Build a Product I - Michael Seibel, Steve Huffman, Emmett Shear - Stanford CS183F

Wed, 21 Oct 2026 06:00:00 -0000

In the first of four lectures on How to Build a Product, Michael Seibel, CEO of Y Combinator, interviews Steve Huffman and Emmett Shear on how they built their products as founder-CEO's of Reddit and Twitch, respectively.

Uploaded 19-04-17

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 6: Dataset Split, ML Advice

Wed, 21 Oct 2026 05:32:00 -0000

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Stanford EE259 I Inertial sensors: gyroscope principle of operation and architecture I 2023 I Lec. 6

Wed, 21 Oct 2026 05:00:00 -0000

To follow along with the course, visit the course website:
https://web.stanford.edu/class/ee259/index.html

Uploaded 21-01-24

Reza Nasiri Mahalati
Adjunct Professor of Electrical Engineering at Stanford University
https://profiles.stanford.edu/reza-nasiri-mahalati

Learn more about the course and how to enroll: https://online.stanford.edu/courses/ee259-principles-sensing-autonomy

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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Stanford Seminar - Creating a Buzz Around B2B Software

Wed, 21 Oct 2026 01:32:00 -0000

Christine Yen
Honeycomb

May 29, 2019
Honeycomb co-founder and CEO Christine Yen spent a decade as a software engineer before creating her own company. She describes how her deep domain knowledge and relationships with like-minded software developers propelled her startup’s launch, and shares how she built an energetic human architecture around a highly technical B2B product.

Uploaded 05-06-19

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Webinar - Achieving a Sustainable Future with Clean, Renewable Energy and Storage

Tue, 20 Oct 2026 21:32:00 -0000

Learn more about our Clean, Renewable Energy and Storage course (https://stanford.io/3TZbXqe)
Learn more about the Stanford Energy Innovation and Emerging Technologies Program (https://stanford.io/3KljRXU)

Uploaded 31-03-23

The world is experiencing unprecedented fuel price increases in part due to energy and political insecurities, up to 7 million air pollution deaths per year worldwide and one climate-related disaster after another. Solutions to these problems invariably require a concerted effort to transform our energy infrastructure, technologies, and policies. Research at Stanford University concludes that these problems can be solved in 145 countries that were examined--without blackouts and at low cost using only clean, renewable energy and storage.

Professor Mark Jacobson sheds light on the pressing need to shift towards clean, renewable energy and storage to address the harmful effects of climate change on air quality, energy accessibility and political stability.

#climatechange #sustainability #renewableenergy #energy

Learn more about how to achieve a sustainable future: https://stanford.io/3TZbXqe

Learn more about Stanford Online's Energy Innovation and Emerging Technologies program: https://stanford.io/3KljRXU

0:00 Introduction
1:30 Problems we need to solve
2:57 Solutions
6:10 Hydrogen
9:15 Direct Air Capture
16:39 Underground Borehole System
18:55 World Energy Transition
20:57 Land
23:29 Policy
25:25 Summary
27:36 Conclusion

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Stanford Seminar - SMILE: Synchronized, Multi-sensory Integrated Learning Environment

Tue, 20 Oct 2026 17:32:00 -0000

Robert Iannucci
Carnegie Mellon University
Director, Cylab Mobility Research Center

May 29, 2019
Wirelessly-enabled sensing technologies offer the hope of enabling economically and socially important applications including intelligent transportation systems and other so-called smart city initiatives, home automation, improvements in manufacturing, and tools for humanitarian assistance and disaster response. Importantly, the value may only be realizable through the integration of a number of dis-similar, separately-built, separately-owned, and separately-controlled sensing sub-systems. SMILE -- the Synchronized Multi-sensory Integrated Learning Environment -- is a CMU project that aims to create, deploy and validate a framework that addresses key challenges that arise in such a system-of-systems.

Uploaded 05-06-19

In this talk, we present early results of the SMILE project. We begin with an examination of some practical barriers in a typical smart city application. From this, we develop themes related to low-power operation, programmability, synchronization, federation, and the interplay between them. Within this context, we discuss four sub-projects: (1) a synchronization approach for low-power devices, (2) a family of visual sensing techniques that explore the domain of real-time learning onboard small unmanned aerial systems (sUAS), (3) a demonstration system that combines information from such sUAS with intelligent ground-based sensors, and (4) the sketch of a new language framework, called TickTalk, that seeks to ease the programmer's burden in creating software for such complex, heterogeneous, distributed, fault-prone systems.

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMWw6rRoeSpkiseTHzWj6vu

0:00 Introduction
1:53 Observations
6:18 Adoption Depends on Behaviors and Habits
7:19 Some Implicit Criteria for Success
12:16 Corollaries to the OHIO Principle
17:21 An Hour in the Life of an OHIO Device Designed for a Five-year Mission
19:33 Application-Specific Time Accuracy Requirements
21:42 The Value is in Sharing Data
22:04 Programing Today's Distributed System
23:17 Programming in the Cyber-Physical Network
24:06 A Perspective on our Challenges
25:58 Platformization and lannucci's Law
26:44 Computing Platforms: Past and Present
35:27 Hypothesis: it will be the The Cyber-Physical Network
36:14 Design Parameters of this New Computing Platform
39:30 CCSG: Integrating Computing and Communications
41:07 Projects
41:39 LongShot: Long-Range Synchronization of Time
43:13 Main Challenges
47:03 How accurately can we timestamp?
47:38 Semtech SX1276 Timing Characterization - Transmit
48:40 Approach
48:43 Two-Step Synchronization Result 3 Devices over a 2 hour period @ 4km
50:19 PowerDue: Power / Energy Aware Programming
55:26 Synchronized Multisensory Learning
55:54 ClickBAIT: Click-Based Accelerated Incremental Training
59:04 Results: Classification

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Stanford CS330 Deep Multi-Task and Meta Learning - Multi-Task Learning Basics I 2022 I Lecture 2

Tue, 20 Oct 2026 13:32:00 -0000

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai

To follow along with the course, visit:
https://cs330.stanford.edu/

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Chelsea Finn
Computer Science, PhD

Multi-Task Learning
- Problem statement
- Models, objectives, optimization
- Challenges
- Case study of real-world multi-task learning

Uploaded 31-03-23

Goals for by the end of lecture:
- Understand the key design decisions when building multi-task learning systems

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Stanford Seminar - HabitLab: In-the-wild Behavior Change Experiments at Scale

Tue, 20 Oct 2026 09:32:00 -0000

Geza Kovacs
Stanford University

May 31, 2019
Behavior change systems help people manage their time online. However, existing productivity systems have tended to assume a one-size-fits all solution, whereas there are many factors - novelty effects, attrition, influences from other apps and devices, and differences in individual motivation - that we must take into account. In this thesis we present HabitLab, an in-the-wild experimentation platform we developed for conducting behavior change experiments, as well as a set of studies we run on the platform. HabitLab is a browser extension and android app with over 12,000 daily active, voluntary users, that users install to help them reduce time online and on their phones. It works by displaying one of 20+ interventions whenever they open an app or site they wish to spend less time on.

Uploaded 08-06-19

We use HabitLab as a large-scale experiment platform to understand behavior change. In our first set of studies, we investigate novelty effects of interventions, finding that compared to always showing the same intervention, a strategy of rotating between different interventions improves intervention effectiveness, but at the cost of increased attrition. This attrition is partly due to users being unfamiliar with rotating interventions, and improving users mental models with a notice shown whenever a new intervention is shown is able to reduce this attrition. In our second set of studies, we investigate whether reducing time on one site or app by intensifying interventions influences time on other sites, apps, and devices. We find that on the browser, reducing time on one site reduces time spent elsewhere, but we do not observe the effect on mobile devices, and do not observe cross-device effects. In our third set of studies, we investigate users motivation levels over time as indicated by the difficulty of interventions they select. We find that users initially overestimate how difficult of interventions they want, and their choices of difficulty progressively decline over time.

Details at https://habitlab.stanford.edu/

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford CS330 Deep Multi-Task and Meta Learning - What is multi-task learning? I 2022 I Lecture 1

Tue, 20 Oct 2026 05:32:00 -0000

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai

To follow along with the course, visit:
https://cs330.stanford.edu/

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Chelsea Finn
Computer Science, PhD

What will you learn in this course?
1. The foundations of modern deep learning methods for learning across tasks
2. How to implement and work with practical multi-task and transfer learning
systems (in PyTorch)
3. A glimpse into the scientific and engineering process of building and
understanding new algorithms

Uploaded 30-03-23

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Lecture 1 – NLU Course Overview | Stanford CS224U: Natural Language Understanding | Spring 2019

Tue, 20 Oct 2026 01:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Goals of Natural Language Understanding include:
- Gain insights into human cognition
- Develop artificial intelligence agents as assistants or companions
- Solve a major subproblem of AI

Uploaded 18-06-19

What is understanding?
To understand a statement is to:
- determine the truth (with justification)
- calculate its entailments
- take appropriate action in light of it
- translate it into another language

Professor Christopher Potts and Consulting Assistant Professor Bill MacCartney, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Potts
Professor of Linguistics and, by courtesy, Computer Science
Director, Stanford Center for the Study of Language and Information
http://web.stanford.edu/~cgpotts/

Consulting Assistant Professor Bill MacCartney
Senior Engineering Manager, Apple
https://nlp.stanford.edu/~wcmac/

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/

0:00 Introduction
1:34 Goals of NLU
6:17 Technological and cognitive goals
7:57 What is understanding?
15:01 Philosophical debates
19:29 A question of fact, or a question of usage?
20:41 A brief history of NLU
25:49 How do conversational agents work?
28:04 The promise of conversational agents
29:51 The reality of conversational agents
35:26 Reminiscent of Eliza (1966)
36:39 Conversational search at Google
39:44 Application: sentiment analysis
40:39 Twitter prognostication
41:57 Hathaway vs. Hathaway
42:26 Application: automated trading
43:21 The 2008 United Airlines "bankruptcy"
45:49 The 2013 @AP Twitter hack
46:41 NLU: Traditional organization
47:30 Semantic representations
49:23 Big themes for this class
51:05 Course goals

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Stanford Seminar - Intellectual Property and Artificial Intelligence

Mon, 19 Oct 2026 21:32:00 -0000

February 15, 2023
Dennis Allison of Stanford University

Who owns the works of authorship and the inventions of artificial intelligence programs? How is this different class of intellectual property to be managed? Who should benefit from the discoveries and creations of artificial intelligence computations? Everyone participating in the forum, enrolled or not, is urged to prepare a short comment or observation.

Uploaded 03-03-23

#intellectualproperty #artificialintelligence

0:00 Introduction
4:00 What is intellectual property?
5:33 What are patents?
6:40 Utility patents
7:15 Nontechnical patents
9:27 Fair use
10:45 Trade secrets
11:30 Fundamental question
17:45 Open forum
19:50 Who would profit
22:18 Licensing
26:05 Copyrights
34:02 Copyright incentives
39:10 Artificial Intelligence
50:53 Artificial personhood

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MATSCI Demo

Mon, 19 Oct 2026 17:32:00 -0000

Uploaded 19-06-19

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Stanford Seminar - Challenges in AI Safety: A Perspective from an Autonomous Driving Company

Mon, 19 Oct 2026 13:32:00 -0000

April 6, 2022
Jerry Lopez of Motional

There is a long legacy of deploying complex software in safety critical applications in industries like aviation and automotive. The increasing use of Machine Learning (ML) models in these applications has forced the engineering community to rethink what it means to ensure that software applications can operate with extremely low probabilities of failure. This talk will highlight some of the biggest challenges with AI safety in the autonomous driving community and discuss some of the most promising methods currently being researched for ensuring a high degree of safety.

Uploaded 02-03-23

https://arxiv.org/pdf/2107.09918.pdf
https://arxiv.org/pdf/2012.07170.pdf

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Lecture 10 – Grounding | Stanford CS224U: Natural Language Understanding | Spring 2019

Mon, 19 Oct 2026 09:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Professor Christopher Potts and Consulting Assistant Professor Bill MacCartney, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Potts
Professor of Linguistics and, by courtesy, Computer Science
Director, Stanford Center for the Study of Language and Information
http://web.stanford.edu/~cgpotts/

Uploaded 24-07-19

Consulting Assistant Professor Bill MacCartney
Senior Engineering Manager, Apple
https://nlp.stanford.edu/~wcmac/

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/

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Stanford Seminar - Using Narrative to Transform Publishing and Business Analytics

Mon, 19 Oct 2026 05:32:00 -0000

January 18, 2012
Kristian Hammond of Narrative Science, Inc

Writing Stories from Data: Using Narrative to Transform Publishing and Business Analytics

As the publishing industry struggles to deal with a post Google world and more and more data comes online, there is a growing opportunity for the automatic creation of content based upon the analysis of data and the articulation of the stories hidden with it. In this talk, Dr. Hammond will discuss the technology developed by Narrative Science that enables this work and outline the possibilities that it opens up to both publishers and organizations that are generating data related to their internal business processes. Focused on content generation, the technology is driven by the idea that stories are the bridge between numbers and knowing.

Uploaded 28-02-23

About the speaker:
Dr. Kristian Hammond one of the founders and CTO of Narrative Science, a company focused on the automatic generation of stories and related content based upon data. On leave from the Department of Electrical Engineering and Computer Science at Northwestern University he is a researcher in the areas of human-machine interaction, context-driven information systems and artificial intelligence. After completing his Ph.D. in computer science at Yale University in 1986, Dr. Hammond founded The University of Chicago's Artificial Intelligence Laboratory. Under his direction, the laboratory flourished as a center for innovative artificial intelligence research for over a decade. In 1998, Dr. Hammond moved to Northwestern University to form Northwestern's Intelligent Information Laboratory (InfoLab) and began focusing on the generation of content based on search and data analytics. In 2010 he helped found Narrative Science and went on leave in 2011 to help guide the movement of the technology into products that are transforming the way in which data is understood and content is generated.

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Lecture 11 – Semantic Parsing | Stanford CS224U: Natural Language Understanding | Spring 2019

Mon, 19 Oct 2026 01:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Professor Christopher Potts and Consulting Assistant Professor Bill MacCartney, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Potts
Professor of Linguistics and, by courtesy, Computer Science
Director, Stanford Center for the Study of Language and Information
http://web.stanford.edu/~cgpotts/

Uploaded 24-07-19

Consulting Assistant Professor Bill MacCartney
Senior Engineering Manager, Apple
https://nlp.stanford.edu/~wcmac/

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/

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Stanford Seminar - An Intersectional Approach to Designing for Disability

Sun, 18 Oct 2026 21:32:00 -0000

February 10, 2023
Robin Brewer of University of Michigan

Recent reports show that nearly 1 in 4 people, globally, have at least one disability. These numbers increase with age and can be more common in non-Western contexts. In this talk, I argue for a departure from accessibility research that focuses solely on disability and discuss how intersectional approaches can help designers and researchers create technologies that represent a more holistic and nuanced approach to disability. I will focus on designing for better information access and use at the intersection of (1) disability and older age and (2) disability and ethnicity.

Uploaded 24-02-23

About the speaker:
Robin Brewer (she/her) is an Assistant Professor in the School of Information at the University of Michigan. Her research lies at the intersection of accessibility and social computing where she studies how disabled people engage with technology, leveraging strengths of these communities to design for expression, representation, and agency. Currently, her research focuses on conversational technologies for late-life disability and care, and better disability and age representation in systems. Robin received her Ph.D. in Technology and Social Behavior (joint program in Computer Science and Communication Studies) from Northwestern University. Her research has been supported by the National Science Foundation, the National Institutes of Health, the Retirement Research Foundation, Google, and the U.S. Department of Transportation

#disability

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Lecture 12 – Evaluation Methods | Stanford CS224U: Natural Language Understanding | Spring 2019

Sun, 18 Oct 2026 17:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Professor Christopher Potts and Consulting Assistant Professor Bill MacCartney, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Potts
Professor of Linguistics and, by courtesy, Computer Science
Director, Stanford Center for the Study of Language and Information
http://web.stanford.edu/~cgpotts/

Uploaded 24-07-19

Consulting Assistant Professor Bill MacCartney
Senior Engineering Manager, Apple
https://nlp.stanford.edu/~wcmac/

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/

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Stanford Seminar - DAO Liability

Sun, 18 Oct 2026 13:32:00 -0000

February 13, 2023
Rodrigo Seira of Paradigm

Uploaded 23-02-23

#dao

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Lecture 13 – Evaluation Metrics | Stanford CS224U: Natural Language Understanding | Spring 2019

Sun, 18 Oct 2026 09:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Professor Christopher Potts and Consulting Assistant Professor Bill MacCartney, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Potts
Professor of Linguistics and, by courtesy, Computer Science
Director, Stanford Center for the Study of Language and Information
http://web.stanford.edu/~cgpotts/

Uploaded 24-07-19

Consulting Assistant Professor Bill MacCartney
Senior Engineering Manager, Apple
https://nlp.stanford.edu/~wcmac/

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/

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Stanford Seminar - Towards Shape Changing Displays and Shape Changing Robots

Sun, 18 Oct 2026 05:32:00 -0000

February 10, 2023
Sean Follmer of Stanford University

Morphological change can afford both information transfer (through both vision and touch) as well as functional adaptation to the environment or the task at hand. In my research, I explore the design, development, and modeling of shape changing systems in both haptic user interfaces and robotics. Towards a goal of more human-centered computing, I believe that interaction must be grounded in the physical world and leverage our innate abilities for spatial cognition and dexterous manipulation with our hands. By creating interfaces that allow for richer physical interaction, such as bimanual, whole hand haptic exploration, these systems can help people with different abilities (e.g., children, people with visual impairments, or even expert designers) better understand and interact with information. The first part of my talk will discuss a central challenge in the widespread adoption of such haptic user interfaces – how can we create physical interactive displays that update dynamically, and what are the interaction techniques and enabling technologies necessary to support such systems? In a parallel domain, Robotics, these same technologies and approaches can support new multifunctionality and adaptation. In the second part of my talk, I will detail our recent progress in large shape changing truss robots. I will present methods for high-extension and compliant actuation in truss robots and explore how the compliance can be utilized for unique behaviors. This shape change can be applied to locomotion, physical interaction with the environment, and the engulfing, grasping, and manipulation of objects.

Uploaded 22-02-23

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Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019

Sun, 18 Oct 2026 01:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Professor Christopher Potts and Consulting Assistant Professor Bill MacCartney, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Potts
Professor of Linguistics and, by courtesy, Computer Science
Director, Stanford Center for the Study of Language and Information
http://web.stanford.edu/~cgpotts/

Uploaded 24-07-19

Consulting Assistant Professor Bill MacCartney
Senior Engineering Manager, Apple
https://nlp.stanford.edu/~wcmac/

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/

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Stanford Seminar - Crypto Governance

Sat, 17 Oct 2026 21:32:00 -0000

February 6, 2023
Andy Hall of Stanford University

Uploaded 21-02-23

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Lecture 15 – Presenting Your Work | Stanford CS224U: Natural Language Understanding | Spring 2019

Sat, 17 Oct 2026 17:32:00 -0000

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Professor Christopher Potts and Consulting Assistant Professor Bill MacCartney, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Potts
Professor of Linguistics and, by courtesy, Computer Science
Director, Stanford Center for the Study of Language and Information
http://web.stanford.edu/~cgpotts/

Uploaded 24-07-19

Consulting Assistant Professor Bill MacCartney
Senior Engineering Manager, Apple
https://nlp.stanford.edu/~wcmac/

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/

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Stanford Seminar - Data Imaginaries

Sat, 17 Oct 2026 13:32:00 -0000

February 3, 2023
Audrey Desjardins of University of Washington

0s and 1s on a screen. The Cloud. Fast moving. Clean. Efficient. Exponentially growing. Data Centers. Code on the black screen of a terminal window. Buzzing. Such images construct part of commonly shared imaginaries around data. As data increasingly become part of the most intimate parts of people's lives (often at home), it remains a largely invisible phenomenon. In particular, one of the leading challenges currently facing Internet of Things (IoT) is algorithmic transparency and accountability with regards to how IoT data are collected, what is inferred and who they are shared with. From a home dweller's perspective, data may be available for review and reflection via graphs, spreadsheets, and dashboards (if at all available!).

Uploaded 14-02-23

In this talk, I instead argue for other modes of encountering IoT data: ways that are creative, critical, subtle, performative, and at times analog or fictional. By translating data into ceramic artifacts, performance and interactive installation experiments, fiction stories, imagined sounds, faded fabric, and even data cookies, I show a diversity of approaches for engaging data that might capture people's attention and imagination. As a result, this work uncovers ways to make data more real, showing its messiness and complexities, and opens questions about how data might be interpreted, and by whom.

About the speaker:
Audrey Desjardins is an interaction designer who speculatively and critically examines how people live with technology. She designs interactive artifacts and systems that reimagine the familiar co-existence of humans and things. Her current projects touch on ways to inquire and reclaim Internet of Things data through crafting and materializing ways of living with that data. As a design scholar, she believes that the design and making of artifacts is a rich site of knowledge production. She designs to articulate questions, to propose alternatives and to provoke reflection.

Audrey is an associate professor in interaction design, director of Studio Tilt, and graduate program chair for the Master of Design at University of Washington, in the School of Art + Art History + Design, and adjunct assistant professor in Human Centered Design and Engineering (HCDE) and Digital Arts and Experimental Media (DXARTS). She holds a PhD and Master of Arts from the School of Interactive Arts + Technology at Simon Fraser University, and a bachelor degree in industrial design at Université de Montreal. Photo Credit: Tegra Stone Nuess

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Stanford Graduate Certificate: Epidemiology and Clinical Research

Sat, 17 Oct 2026 09:32:00 -0000

The Graduate Certificate in Epidemiology and Clinical Research is offered by the Epidemiology division of the Health Research and Policy Department at Stanford School of Medicine.
Learn more: https://online.stanford.edu/programs/epidemiology-and-clinical-research-graduate-certificate

Uploaded 06-08-19

Classes in this program – accessible to students worldwide via online learning – provide students the knowledge and skills essential to patient-oriented clinical research, including epidemiologic methods and statistical analysis, specifically introductory and intermediate coding in R and SAS.

#graduateprogram

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Stanford Seminar - The History of Crypto Art

Sat, 17 Oct 2026 05:32:00 -0000

January 30, 2023
Richard Chen of 1confirmation

Uploaded 13-02-23

#cryptoart

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Stanford Webinar - How to Lead [Not Just Survive] Digital Transformation, Pamela Hinds

Sat, 17 Oct 2026 01:32:00 -0000

Learn more at: https://stanford.io/3NL2Ea9
Professor Hinds teaches XDGT100 Foundations for Digital Transformation
https://online.stanford.edu/courses/xdgt100-foundations-digital-transformation

In this webinar, Professor Pamela Hinds discusses the delicate balance of technology and strategy as required for successful digital transformation. You'll learn key differentiators between companies that are leading the pack and those that are lagging behind. Ensure that you’re poised for digital transformation by learning how successful organizations align their strategies, cultures, and processes to remain competitive as the digital landscape evolves.

Uploaded 08-08-19

0:00 Introduction
0:11 Today's Speaker
1:45 What is Digital Transformation?
4:10 Disruptive Emerging Technologies
7:50 How is the imperative for digital transformation different from past disruptions?
11:00 Poll
11:47 To what extent do you believe digital technologies will disrupt your industry?
14:48 What does digital transformation
20:24 It Isn't As Much About the Technology As We Might Think
23:38 If it isn't all about technology, what is it?
26:14 Characteristics of Digital Culture
29:30 What is nimbleness/agility?
29:55 Nimbleness and Digital Transformation
31:11 Designing for Nimbleness
34:23 Collaboration and Digital Transformation
37:16 Successful Digital Transformation
38:05 Wrap Up
38:53 Presented By The Stanford Digital Transformation Program

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Stanford Seminar - Crypto Entrepreneurship 101

Fri, 16 Oct 2026 21:32:00 -0000

January 23, 2023
Haseeb Qureshi of Dragonfly Capital

Uploaded 09-02-23

#crypto

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Stanford Webinar: Battery Innovations to Fight Climate Change, Abbas El Gamal and Ram Rajagopal

Fri, 16 Oct 2026 17:32:00 -0000

To learn more about the Stanford Online energy storage professional education course, visit https://online.stanford.edu/courses/xeiet139-energy-storage

Stanford Professors Abbas El Gamal and Ram Rajagopal introduce their new Stanford course on Battery Systems for Transportation and Grid Services. They will discuss the motivation behind this unique, interdisciplinary offering, as well as recent innovations in battery systems, and Stanford's Powernet and EV50 initiatives.

Uploaded 30-08-19

Learn more about Stanford's Energy Innovation and Emerging Technologies program: https://online.stanford.edu/programs/energy-innovation-and-emerging-technologies-certificate

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Stanford Seminar - From open-source to safety-certified robotic software

Fri, 16 Oct 2026 13:32:00 -0000

January 27, 2023
Jan Becker of Apex.ai

For decades, automotive and robotics developers have reinvented the software wheel many times. With its launch in 2010, ROS has enabled rapid software prototyping and reuse of software in prototyping and development. In 2018, the launch of ROS 2 further improved ROS and its architecture and ROS 2 now provides an efficient prototyping and rapid development platform. But the lack of real-time performance and safety certification still prevents the wide-scale adoption of ROS-based software into products in safety-critical products. In this talk, we will discuss the state of the art in robotics and automotive software and how we have taken ROS from an open-source project to a safety-certified robotic and automotive software development kit.

Uploaded 08-02-23

About the speaker:
Jan Becker is President, CEO and Co-Founder of Apex.AI, Inc. He is also the Managing Director of the Apex.AI GmbH.

Prior to founding Apex.AI, he was Senior Director at Faraday Future responsible for Autonomous Driving and Director at Robert Bosch LLC responsible for Automated Driving in North America. He also served as a Senior Manager and Principal Engineer at the Bosch Research and Technology Center in Palo Alto, CA, USA, and as a senior research engineer for Corporate Research at Robert Bosch GmbH, Germany.

Since 2010, Jan is a Lecturer at Stanford University for autonomous vehicles and driver assistance. Previously, he was a visiting scholar at the University’s Artificial Intelligence Lab and a member of the Stanford Racing Team for the 2007 DARPA Urban Challenge. In 2019, Jan was appointed to serve on the external Advisory Board of MARELLI to provide strategic advice to the MARELLI Board. In 2018, he co-founded the Autoware Foundation and was on the foundation’s board of directors until 2020. Jan earned a Ph.D. in control engineering from the Technical University of Braunschweig, Germany, a master’s degree in mechanical and aerospace engineering from the State University of New York at Buffalo, USA, and a master’s degree in electrical engineering from the Technical University of Darmstadt, Germany.

► Check out our entire catalog of online courses and programs: https://online.stanford.edu/explore

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Stanford Webinar: Diet, Genes and Your Health: Unlock Genetic Potential w/ Science of Epigenetics

Fri, 16 Oct 2026 09:32:00 -0000

Dr. Lucia Aronica, Lecturer at the Stanford Prevention and Research Center, provides an introduction to the emerging science of epigenetics, and describes how it can help you unlock your genetic potential. She explains how you can leverage epigenetic science to improve your health and well-being. Learn more about the course: https://online.stanford.edu/courses/xgen207-epigenetics-and-microbiomics-precision-health?utm_source=youtube&utm_medium=social&utm_campaign=webinar

Uploaded 26-09-19

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Stanford Seminar - Content Moderation as Systems Thinking

Fri, 16 Oct 2026 05:32:00 -0000

January 27, 2023
Evelyn Doeuk of Stanford University

The stylized picture of content moderation that forms the basis for most regulatory and academic discussion of online speech governance is misleading and incomplete. That picture depicts content moderation as a rough online analog of offline judicial adjudication of speech rights, with legislative-style substantive rules being applied over and over again to individual pieces of content by a hierarchical bureaucracy of moderators. But content moderation should instead be understood as a project of mass speech administration and that looking past a post-by-post evaluation of platform decision making reveals a complex and dynamic system that needs a more proactive and continuous form of governance than the vehicle of individual error correction allows.

Uploaded 07-02-23

About the speaker:
Evelyn Douek is an Assistant Professor of Law at Stanford Law School. Before joining the Stanford faculty she was the Senior Research Fellow at the Knight First Amendment Institute at Columbia University and did a doctorate at Harvard Law School on the topic of private and public regulation of online speech.

Evelyn's research has appeared or is forthcoming in the Harvard Law Review, Columbia Law Review, the University of Chicago Law Review Online, Lawfare, The Atlantic, WIRED, Slate, and a number of other publications. Being human, Evelyn naturally has a couple of podcasts, most recently as host of Moderated Content, podcast content from Stanford Law School about content moderation.

► Check out our entire catalog of online courses and programs: https://online.stanford.edu/explore

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Stanford Seminar - Conflict and Technology

Fri, 16 Oct 2026 01:32:00 -0000

Oskar Mencer
Maxeler Technologies and Imperial College London

September 25, 2019
We live in a world of seemingly increasing numbers of types of conflicts and less and less time and energy for each one. Chip Wars, Internet vs Democracy, and Flat Earthers,... At the same time, Technology reduces the information difference around the world, predicting what can be predicted, leaving the unpredictable as our future. Recently, as Enterprise Champion at Imperial College London, I started thinking more about the innovation challenge in a broader context. Each innovation addresses one or multiple pain points. Each time a possible solution to a pain point succeeds, there is a chance for a new pain point to arise from the new solution. The successful entrepreneur finds him or herself constantly chasing solutions to problems created by their success. I will complement the above journey with examples from building fast computers with FPGAs.

Uploaded 02-10-19

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMWw6rRoeSpkiseTHzWj6vu

0:00 Introduction
4:41 Technology in banking - a problem in scale and complexity
9:48 Fastest computers for top 4 HPC applications
11:48 Commercializing FPGAs for Computation
16:31 Conflicts We live in a world of many conflicts
20:15 New Conflicts
24:15 The Kill Switch Product Idea
28:09 Uncertainty
30:34 The Homework Problem
32:58 Start a company, build a product Pain Points
35:18 Scaling is a race against cashflow
36:17 Top 10 Conflicts in Computing with FPGAS Conflict 1: HDL is hard, need a high level programming language Conflict 2: FPGAS DRAM Memory interfaces
39:57 HDL is hard, high level programming
40:30 C1: Connect language to space on the chip
43:33 FPGA floating point is inefficient (due to the barrel shifter)
45:02 Separating CPUs and FPGAS Conflict: CPU and FPGA in the same server is inefficient. The optimal balance between FPGAs and CPUs is never exactly 50-50, Server+FPGA card is inefficient Solution: build an Infiniband-connected appliance
47:32 There are no Applications for FPGAS Why would you buy a computer for which there are no applications
47:56 Need to rewrite parts of the application Solution 1: Develop the Maxeler acceleration process
49:06 Hardware Debug is Hard MaxDebug tool example
49:21 C7 Hardware Efficiency Debug is Hard
49:43 c7 Hardware Performance Debug is Hard MaxProfile tool example
50:00 Place-and-route takes 3 days
51:34 High level language obsoletes the HDL expert
52:43 Most software does not need acceleration
58:58 Conclusions To scale, you need to keep up with the conflict cycle, predict and solve the next next conflict before it happens!
59:45 World's hardest simulation, Quantum Chromodynamics on a Xilinx VU9P FPGA

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Stanford Seminar - Systems for Supporting Intent Formation and Human-AI Communication

Thu, 15 Oct 2026 21:32:00 -0000

January 20, 2023
Elena Glassman of Harvard University

Systems for Supporting Intent Formation and Human-AI Communication: Existing Challenges and Promising Design Strategies

Uploaded 04-02-23

Communication between humans is often a multimodal conversation. It can be a recurring loop with optional steps depending on the complexity of the situation and our possibly evolving understanding of what we want. The same is true of our communication with computers. When a human is reasoning about complex data and asking for automated assistance, this conversation can be especially hard. Carefully designed data rendering and interface affordances can help significantly. I will present a selection of novel interactive systems whose interface designs help users (1) quickly and accurately form a mental model of a concept from relevant data, (2) form - and update - their beliefs about what they want (3) communicate what they want to a computer when its automation capabilities are helpful and/or (4) have justified confidence that the automation being carried out reflects their current intentions. Systems discussed will include an interface for comparing and contrasting similar but distinct programming libraries based on real-world usage and a mixed-initiative neurosymbolic system that helps users perform qualitative coding, i.e., label large volumes of short texts with emergent themes.

About the speaker:
Elena L. Glassman is an Assistant Professor of Computer Science at the Harvard Paulson School of Engineering and Applied Sciences, specializing in human-computer interaction. She recently served as the Stanley A. Marks and William H. Marks Professor at the Radcliffe Institute for Advanced Study. At MIT, she earned a PhD and MEng in Electrical Engineering and Computer Science and a BS in Electrical Science and Engineering. Before joining Harvard, she was a postdoctoral scholar in Electrical Engineering and Computer Science at the University of California, Berkeley, where she received the Berkeley Institute for Data Science Moore/Sloan Data Science Fellowship. Her recent work has been awarded funding by the Harvard Data Science Institute, the NSF programs on Fairness in Artificial Intelligence, Formal Methods in the Field, Cyber-Human Systems, and the Science of Learning and Augmented Intelligence.

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Stanford Seminar - The Future of Edge Computing from an International Perspective

Thu, 15 Oct 2026 17:32:00 -0000

Dr. Richard B. Dasher
Stanford University

September 26, 2019
Dr. Richard B. Dasher provides an overview of seminar series where distinguished guest speakers discuss Asian and U.S. approaches to 5G network integration, federated learning, computer chips and servers for edge AI processing, and IOT systems built around intelligent clients, such as register-less stores, smart factories, autonomous vehicles, and augmented reality applications.

Uploaded 03-10-19

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rOY_McwVddhHBnO2ey7F7oJ

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Stanford Seminar - Remembering Carl Hewitt

Thu, 15 Oct 2026 13:32:00 -0000

January 18, 2023

Carl Hewitt, an eminent AI researcher, visionary computer scientist, emeritus MIT faculty, and frequent EE380 speaker died December 7th 2022 at his home in Aptos, California. A memorial gathering was held on a rainy Saturday in Santa Cruz on January 14th followed by a well attended Zoom reception.

Uploaded 04-02-23

EE380 for January 18th 4PM will be a lightly moderated open Zope forum to discuss Carl’s contributions to Computer Science and Engineering and recognize their significance. Everyone attending is encouraged to participate and share both technical and personal comments.

Here is Carl’s published obituary from the Santa Cruz Sentinal and his Wikipedia page.
https://www.legacy.com/us/obituaries/santacruzsentinel/name/carl-hewitt-obituary?id=38594220
https://en.wikipedia.org/wiki/Carl_Hewitt

Carl was a prolific author and published frequently and widely. His work in progress often went to one or another of the open access repositories, for example Hal or arXiv.
https://en.wikipedia.org/wiki/HAL_(open_archive)

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Stanford Seminar - Bridging model-based and data-driven reasoning for safe human-centered robotics

Thu, 15 Oct 2026 09:32:00 -0000

Jaime Fisac is an Assistant Professor of Electrical and Computer Engineering at Princeton University.

Uploaded 14-10-19

This talk was given on September 27, 2019.

Spurred by recent advances in perception and decision-making, robotic technologies are undergoing a historic expansion from factory floors to the public space. From autonomous driving and drone delivery to robotic devices in the home and workplace, robots are bound to play an increasingly central role in our everyday lives. However, the safe deployment of these systems in complex, human-populated spaces introduces new fundamental challenges. Whether safety-critical failures (e.g. collisions) can be avoided will depend not only on the decisions of the autonomous system, but also on the actions of human beings around it. Given the complexity of human behavior, how can robots reason through these interactions reliably enough to ensure safe operation in our homes and cities? In this talk I will present a vision for safe human-centered robotics that brings together control-theoretic safety analysis and Bayesian machine learning, enabling robots to actively monitor the “reality gap” between their models and the world while leveraging existing structure to ensure safety in spite of this gap. In particular, I will focus on how robots can reason game-theoretically about the mutual influence between their decisions and those of humans over time, strategically steering interaction towards safe outcomes despite the inevitably limited accuracy of human behavioral models. I will show some experimental results on quadrotor navigation around human pedestrians and simulation studies on autonomous driving. I will end with a broader look at the pressing need for assurances in human-centered intelligent systems beyond robotics, and how control-theoretic safety analysis can be incorporated into modern artificial intelligence, enabling strong synergies between learning and safety.

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD

Learn more about our Robotics and Autonomous Systems Graduate Program: https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-program

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Stanford Seminar - Incorporating Sample Efficient Monitoring into Learned Autonomy

Thu, 15 Oct 2026 05:32:00 -0000

January 20, 2023
Rachel Luo of Stanford University

When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial. Warning systems are thus designed to provide alerts when an unsafe situation is imminent (in the absence of corrective action), with the objective of issuing alerts as quickly as possible when there is a problem (i.e. they should be sample-efficient). They should also come with statistical guarantees ensuring that whenever there is an unsafe situation, the warning system will detect it (i.e. a low false negative rate) or that not too many false alarms will be issued (a low false positive rate). In this talk, I will present warning systems for two types of situations. First, I will introduce a real-time warning system framework that can detect unsafe situations when there is no distribution shift. We provide a guarantee on the false negative rate (i.e. of the situations that are unsafe, fewer than epsilon will occur without an alert) using very few samples (only 1/epsilon), and we empirically observe low false detection (positive) rates. Second, I will present a warning system for identifying distribution shifts. Our method is capable of detecting these distribution shifts up to 11x faster than prior work on realistic robotics settings, while providing a high probability guarantee against false alarms. We empirically observe low false negative rates (whenever there is a distribution shift in our experiments, our method indeed emits an alert).

Uploaded 03-02-23

#robotics

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Stanford Seminar - Redesigning the CEO

Thu, 15 Oct 2026 01:32:00 -0000

Sarah Nahm
Lever

October 2, 2019
In 2012, inspired by the HR headaches they’d observed working for technology companies, Sarah Nahm and a few friends founded Lever, a talent recruitment platform aimed at transforming the hiring process with intuitive yet data-driven software. Two years later, in 2014, she was named CEO. Based on her experiences designing what became Lever and then leading the company, she puts forward a model of entrepreneurial leadership that is about more than just stubborn confidence, and thrives by embracing the unknown and learning how to observe and trust others.

Uploaded 14-10-19

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Seminar - Designing Robotic Grippers for Interaction with Real-World Environments

Wed, 14 Oct 2026 21:32:00 -0000

January 20, 2023
Tony Chen of Stanford University

Equipping robots with the functionality to traverse and interact with real-world environments beyond the laboratory is the crucial next step in advancing robotics, particularly for field robotic surveying and exploration. In order to achieve this, robots need to have the capability of interacting with the environment – that is, the capability to manipulate. In this talk, I will discuss the design, prototype, and experimentation process of two grippers for two robotic systems. First, I will introduce a gripper for aerial grasping with drones in mid-flight, where starting from simple dynamic models leads to the design principles behind a passively activated gripper, and the implications of flight controls. Second, I will introduce ReachBot, a novel rock climbing robot designed for planetary exploration such as Martian lava tubes, and focus on the mechanical design challenges and the gripper designs.

Uploaded 02-02-23

#robotics

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Stanford Seminar - Promise of 5G Wireless – The Journey Begins

Wed, 14 Oct 2026 17:32:00 -0000

Arogyaswami Paulraj
Stanford University

October 3, 2019
Professor Emeritus Arogyaswami Paulraj, Stanford University, is a pioneer of MIMO wireless communications, a technology break through that enables improved wireless performance. MIMO is now incorporated into all new wireless systems.

Uploaded 15-10-19

Paulraj is the author of over 400 research papers, two text books and a co-inventor in 79 US patents.

Paulraj has won over a dozen awards, notably the National Inventors Hall of Fame (USPTO), Marconi Prize and Fellowship, 2014 and the IEEE Alexander Graham Bell Medal, 2011. He is a fellow of eight scientific / engineering national academies including the US, China, India and Sweden. He is a fellow of IEEE and AAAS.

In 1999, Paulraj founded Iospan Wireless Inc. – which developed and established MIMO-OFDMA wireless as the core 4G technology. Iospan was acquired in by Intel Corporation in 2003. In 2004, he co-founded Beceem Communications Inc. The company became the market leader in 4G-WiMAX semiconductor and was acquired by Broadcom Corp. in 2010. In 2014 he founded Rasa Networks to develop Machine Learning tools for WiFi Networks. The company was acquired HPE in 2016.

During his 30 years in the Indian (Navy) (1961-1991), he founded three national level laboratories in India and headed one of India’s most successful military R and D projects – APSOH sonar. He received over a dozen awards (many at the national level) in India including the Padma Bhushan, Ati Vishist Seva Medal and the VASVIK Medal.

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Stanford Webinar - GPT-3 and Beyond

Wed, 14 Oct 2026 13:32:00 -0000

GPT3 and Beyond: Key concepts and open questions in a golden age for natural language understanding

Listen in as Professor Christopher Potts discusses the significance and implications of recent Natural Language Understanding developments including GPT-3. He describes the fundamental building blocks of these systems and describes how we can reliably assess and understand them.

Uploaded 31-01-23

Learn more about the AI Professional Program: https://stanford.io/3kYThd2

View the slides for this webinar here: https://stanford.io/potts-GPT3-webinar2023

#gpt3 #stanfordwebinar

Chapters:
0:00 Opening
00:09 Introduction for Chris Potts
1:09 Chris Potts - Welcome to the webinar
03:43 Quick Demo of GPT-3
06:41 GLUE benchmark
10:26 How can you contribute to NLU in this era of these gargantuan models?
11:44 The last mile problem
13:35 GPT example
14:50 Contrast In-Context learning with the standard paradigm of standard supervision
16:47 What are the mechanisms behind this?
18:18 Why does this work so well?
18:35 Self-Supervision
21:08 The role of human feedback
21:32 Chat GPT Diagram
23:42 Step-by-step reasoning
28:13 LLMs for everything approach
41:29 AI Courses at Stanford
46:28 Predictions about the future

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Stanford Seminar - Participating and Designing around Algorithmic Sociotechnical Systems

Wed, 14 Oct 2026 09:32:00 -0000

Motahhare Eslami
Carnegie Mellon University

October 4, 2019
Algorithms play a vital role in curating online information in socio-technical systems, however, they are usually housed in black-boxes that limit users' understanding of how an algorithmic decision is made. While this opacity partly stems from protecting intellectual property and preventing malicious users from gaming the system, it is also designed to provide users with seamless, effortless system interactions. However, this opacity can result in misinformed behavior among users, particularly when there is no clear feedback mechanism for users to understand the effects of their own actions on an algorithmic system. The increasing prevalence and power of these opaque algorithms coupled with their sometimes biased and discriminatory decisions raise questions about how knowledgeable users are and should be about the existence, operation and possible impacts of these algorithms. In this talk, I will address these questions by exploring ways to investigate users' behavior around opaque algorithmic systems. I will then present new design techniques that communicate opaque algorithmic processes to users and provide them with a more informed, satisfying, and engaging interaction. In doing so, I will add new angles to the old idea of understanding the interaction between users and automation by designing around algorithm sensemaking and transparency.

Uploaded 17-10-19

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford Seminar - Why would we want a multi-agent system unstable

Wed, 14 Oct 2026 05:32:00 -0000

Mrdjan Jankovic of Ford Research
January 13, 2023

In everyday driving, many traffic maneuvers such as merges, lane changes, passing through an intersection, require negotiation between independent actors/agents. The same is true for mobile robots autonomously operating in a space open to other agents (e.g., humans, robots, etc.). Negotiation is an inherently difficult concept to code into a software algorithm. It has been observed in computer simulations that some “decentralized” algorithms produce gridlocks while others never do. It has turned out that gridlocking algorithms create locally stable equilibria in the joint inter-agent space, while, for those that don’t gridlock, equilibria are unstable – hence the title of the talk.

Uploaded 24-01-23

We use Control Barrier Function (CBF) based methods to provide collision avoidance guarantees. The main advantage of CBFs is that they result in relatively easier to solve convex programs even for nonlinear system dynamics and inherently non-convex obstacle avoidance problems. Six different CBF-based control policies were compared for collision avoidance and liveness (fluidity of motion, absence of gridlocks) on a 5-agent, holonomic-robot system. The outcome was then correlated with stability analysis on a simpler, yet representative problem. The results are illustrated by extensive simulations and a vehicle experiment with stationary obstacles.

0:00 Introduction
0:49 Objective - unstable feedback loop? ord
2:43 Why CBFs? Short answer - convex QP
6:31 CBF based safety filter
7:58 Barrier margin for robustness
9:14 Robust Control Barrier Functions
10:32 Turning obstacles into barriers
12:16 CBF based obstacle avoidance
13:42 Traffic flow and gridlocks
14:42 Avoiding interacting obstacles
17:26 Decentralized multi-agent controllers
19:07 Centralized CBF Controller
20:06 Co-optimization and CCS
23:19 PCCA algorithm guarantees
24:00 5 agents Monte Carlo Simulations
24:52 Comparison of CBF based methods
26:08 Deadlock resolution
26:51 Cause of gridlocks - stability?
30:19 DR: simulation perspective
30:49 Centralized and PCCA equilibrium analysis
32:36 PCCA: simulation perspective
34:27 Properties of CBF algorithms
36:12 Some MA unstable modes are undesirable
39:10 Lower barrier bandwidth may improve flow
40:53 Conclusion
47:32 Predictor-Corrector for Coll. Avoidance

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Stanford Seminar - Neural Networks on Chip Design from the User Perspective

Wed, 14 Oct 2026 01:32:00 -0000

Yu Wang
Tsinghua University

October 9, 2019
To apply neural networks to different applications, various customized hardware architectures are proposed in the past a few years to boost the energy efficiency of deep learning inference processing. Meanwhile, the possibilities of adopting emerging NVM (Non-Volatile Memory) technology for efficient learning systems, i.e., in-memory-computing, are also attractive for both academia and industry. We will briefly review our past effort on Deep learning Processing Unit (DPU) design on FPGA in Tsinghua and Deephi, and then talk about some features, i.e. interrupt and virtualization, we are trying to introduce into the accelerators from the userâ€™s perspective. Furthermore, we will also talk about the challenges for reliability and security issues in NN accelerators on both FPGA and NVM, and some preliminary solutions for now.

Uploaded 17-10-19

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMWw6rRoeSpkiseTHzWj6vu

0:00 Introduction
1:18 Deep Learning for Everything
3:37 The New Era is Waiting for the Next Rising Star
7:46 Why? Power Consumption and Latency Are Crucial
8:25 Development of Energy-Efficient Computing Chips
9:13 Our Previous Work: Software Hardware Co-design for Energy Efficient NN Inference System
12:00 NN Compression: Quantization
12:51 NN Compression: Pruning
13:50 Hardware Architecture - Utilization
19:11 Academic NN Accelerators (Performance vs Power)
22:05 Survey on FPGA based Inference Accelerators
24:14 Application Scenarios: Cloud, Edge, Terminal
26:02 Growing of Computation Power
26:41 Brief Summary
27:28 CNN Greatly Benefits Basic Functions in Robotic Applications
28:12 Accelerator Interrupt for Hardware Conflicts
29:10 Interrupt Respond Latency and Extra Cost
29:56 How to Interrupt?
30:53 Virtual Instruction-Based Interrupt
33:36 DNN Inference Tasks in the Cloud
33:57 How to Support Multiple Tasks in the Cloud?
35:49 How to Support Dynamic Workload in the Cloud?
37:09 Low-overhead Reconfiguration of ISA-based Accelerator
38:02 Design Techniques
38:28 Experiments
40:07 Analysis for NN Fault Problems
42:06 Fault Model in Network Architecture Search (NAS)
43:47 Fault Tolerant Training - NAS Framework
44:24 Discovered Architecture
45:54 Bottleneck of Energy Efficiency Improvement
48:08 Conventional Encryption Incurs Massive Write Operations
48:16 Orders of differences in Write endurance and Write Latency
49:06 SFGE: Sparse Fast Gradient Encryption
49:54 Accuracy Drop vs Encryption Num and Intensity
50:51 Select Encryption Configuration for Different NNS

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Stanford Seminar - The Future is Not What it Used to Be: Fun Stuff in Technical HCI is Ahead of Us

Tue, 13 Oct 2026 21:32:00 -0000

The Future is Not What it Used to Be: Some Thoughts on Why the Fun Stuff in Technical HCI is All Ahead of Us

Scott Hudson of Carnegie Mellon University
January 13, 2023

In this talk I will consider how the future of HCI, and in particular technical HCI, is different than it used to be - what has changed, what has stayed the same, and mostly what should we do about it. Although it seems mundane, when we consider change in any sort of computing technology, we must consider "the elephant in the room" of Moore's law. I will present two quick thought experiments in this talk to try to convince you that you really don't understand the implications of Moore's law, that this really does matter, and that you should perhaps be thinking a little differently about your work as a result. (Spoiler alert: you are dramatically underestimating how much change in computing power is ahead of you, and probably under-utilizing it's potential for HCI advances.) Based on this, the core of my talk will consider what we might be missing in terms of how we go about our work, and talk about several exemplars of where a different view of a "new future" might lead in terms of specific research directions. With these exemplars as motivation, I will consider some more general thoughts about the methodologies we use in our work, and suggest a few ways we might consider thinking differently about how we go about our work.

Uploaded 23-01-23

About the speaker: Scott Hudson is a Professor of Human-Computer Interaction at Carnegie Mellon and previously held positions at the University of Arizona and Georgia Tech. He has published extensively in technical HCI. He recently received the ACM SIGCHI Lifetime Research Award. Previously he received the ACM SIGCHI Lifetime Service Award, was elected to the CHI Academy, and received the Allen Newell Award for Research Excellence at CMU. His research interests within HCI are wide ranging, but tend to focus on technical aspects of HCI. Much of his recent work has been considering advanced fabrication technologies such as new machines, processes, and materials for 3D printing, as well as computational knitting and weaving, and applications of mechanical meta-materials.

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Stanford Seminar - Resilience is Everything

Tue, 13 Oct 2026 17:32:00 -0000

Jennifer Tejada
PagerDuty

October 9, 2019
Not long after landing at PagerDuty in 2016, Jennifer Tejada embarked on that harrowing rite of passage for CEOs of fortunate young startups: the pursuit of an IPO. Tejada raised a $90 million Series D round in late 2018, and saw PagerDuty go public on April 11, 2019. Her path to that point, she observes, was anything but linear. She tells the story of how a very “average” University of Michigan grad ended up becoming the CEO of a public SaaS company, and describes how gritty perseverance, some fortunate early leadership opportunities, and a passion for understanding and embracing different perspectives drove her career forward. She offers strategies that aspiring leaders can employ to challenge themselves and build tenacity while creating diverse, high-performing teams.

Uploaded 21-10-19

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Seminar - Fireside Chat between David Farber and Dennis Allison

Tue, 13 Oct 2026 13:32:00 -0000

David Farber - Keio University.

Dennis Allison - Stanford University.

December 5, 2022.
David Farber is a distinguished professor from Keio University. David shares his perspectives on the internet, ex-pat life in Japan, and the future in a fireside talk with Dennis Allison, professor from Stanford University.

Uploaded 18-01-23

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Stanford Seminar - Modeling and Control for Robotic Assistants

Tue, 13 Oct 2026 09:32:00 -0000

Monroe Kennedy is an Assistant Professor of Mechanical Engineering at Stanford University.

Uploaded 21-10-19

This talk was given on October 4, 2019.

As advances are made in robotic hardware, the capacity of the complexity of tasks they are capable of performing also increases. One goal of modern robotics is to introduce robotic platforms that require very little augmentation of their environments to be effective and robust. Therefore the challenge for the Roboticist is to develop algorithms and control strategies that leverage the knowledge of the task while retaining the ability to be adaptive, adjusting to perturbations in the environment and task assumptions. These strategies will be discussed in the context of a wet-lab robotic assistant. Motivated by collaborations with a local pharmaceutical company, we will explore two relevant tasks. First, we will discuss a robot-assisted rapid experiment preparation system for research and development scientists. Second, we will discuss ongoing work for intelligent human-robot cooperative transport with limited communication. These tasks are the beginning of a suite of abilities for an assisting robotic platform that can be transferred to similar applications useful to a diverse set of end-users.

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD

Learn about our Robotics and Autonomous Systems Graduate Program: https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-program

0:00 Introduction
0:30 Outline
1:56 The Three A's of Robotics
4:35 Current PhD Students
4:41 Human-Robot Cooperative Transport
5:53 Improving Dexterous Manipulation
6:56 Intelligent Prosthetic Arm
8:10 Modeling Considerations
11:11 Wet-Lab Robotic Assistant
11:44 Executing Complex Tasks
13:32 Why is the pouring problem challenging?
14:00 Additional Wet-Lab Pouring Task Challenges
14:15 Pouring Problem Solutions
16:24 Problem Formulation
18:05 Precision Pouring for Experiment Preparation Precision Pouring With Known Container Geometries
23:34 Fluid Detection Methods
41:37 Obtaining Pourer Geometry
43:18 Precision Pouring Summary
43:46 Wet Lab Robotic Assistant
43:54 Transport Problem
48:28 Human Robot Collaboration Estimating Human intent for Cooperative Transport of Large/Heavy Objects
54:41 Cooperative Transport Summary
55:44 Acknowledgements
55:49 Questions?

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Stanford Seminar - Interaction-Centric AI

Tue, 13 Oct 2026 05:32:00 -0000

Juho Kim of KAIST
December 9, 2022

Remarkable model performance makes news headlines and compelling demos, but these advances rarely translate to a lasting impact on real-world users. A common anti-pattern is overlooking the dynamic, complex, and unexpected ways humans interact with AI, which in turn limits the adoption and usage of AI in practical contexts. To address this, I argue that human-AI interaction should be considered a first-class object in designing AI applications.

Uploaded 19-12-22

In this talk, I present a few novel interactive systems that use AI to support complex real-life tasks. I discuss tensions and solutions in designing human-AI interaction, and critically reflect on my own research to share hard-earned design lessons. Factors such as accuracy disparity between user groups, user motivation, social dynamics, and sustainable engagement often play a crucial role in determining the user experience of AI, even more so than model accuracy. My call to action is that we need to establish robust building blocks for Interaction-Centric AI a systematic approach to designing and engineering human-AI interaction that complements and overcomes the limitations of model- and data-centric views.

About the speaker:
Juho Kim [juhokim.com] is an Associate Professor in the School of Computing at KAIST, affiliate faculty in the Kim Jaechul Graduate School of AI at KAIST, and a director of KIXLAB (the KAIST Interaction Lab) [kixlab.org]. His research in human-computer interaction and human-AI interaction focuses on building interactive and intelligent systems that support interaction at scale, with the goal of improving the ways people learn, collaborate, discuss, make decisions, and take action online. He earned his Ph.D. from MIT in 2015, M.S. from Stanford University in 2010, and B.S. from Seoul National University in 2008. In 2015-2016, he was a Visiting Assistant Professor and a Brown Fellow at Stanford University. He is a recipient of KAIST s Songam Distinguished Research Award, Grand Prize in Creative Teaching, and Excellence in Teaching Award, as well as 14 paper awards from ACM CHI, ACM CSCW, ACM Learning at Scale, ACM IUI, ACM DIS, and AAAI HCOMP. He is currently spending his sabbatical year at Ringle Inc., a startup building an online language tutoring platform, to transfer his research on automatically analyzing and diagnosing learners English proficiency into a real product.

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Stanford Seminar - New Chip Technologies for AI and for Asia: the Edge is Where the Action is!

Tue, 13 Oct 2026 01:32:00 -0000

Gary Brown
Intel IOTG/DCG

October 10, 2019
Gary Brown is director of AI marketing at Intel’s IoT Group, and has most recently overseen the product launches of edge AI products such as Intel Movidius VPUs (Vision Processor Units) and other IoT Vision products that include AI technologies. Gary has over 25 years of experience leading applications engineering and technical marketing teams in vision, imaging, and audio. Prior to Intel he was VP of Marketing at Movidius, and he led product marketing for imaging/video products at Tensilica and Cadence. Early in his career, Gary enjoyed leading functional verification for an HD Video processor at Panasonic Semiconductor in Osaka, Japan. Gary holds a Masters in EE from Stanford and a Bachelors in Engineering from UC Berkeley.

Uploaded 23-10-19

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Stanford Seminar - Humanizing Robot Design, Aaron Edsinger of Hello Robot

Mon, 12 Oct 2026 21:32:00 -0000

December 9, 2022
Aaron Edsinger of Hello Robot

We are at the beginning of a transformation where robots and humans cohabitate and collaborate in everyday life. From caring for older adults to supporting workers in service industries, collaborative robots hold incredible potential to improve the quality of life for millions of people. These robots need to be safe, intuitive and simple to use. They need to be affordable enough to allow widespread access and adoption. Ultimately, acceptance of these robots in society will require that the human experience is at the center of their design. In this presentation I will highlight some of my work to humanize robot design over the last two decades. This work includes compliant and safe actuation for humanoids, low-cost collaborative robot arms, and assistive mobile manipulators. Our recent work at Hello Robot has been to commercialize a mobile manipulator named Stretch that can assist older adults and people with disabilities. I’ll detail the human-centered research and development process behind Stretch and present recent work to allow an individual with quadriplegia to control Stretch for everyday tasks. Finally I’ll highlight some of the results by the growing community of researchers working with Stretch.

Uploaded 17-12-22

About the speaker:

Aaron Edsinger, CEO and Cofounder of Hello Robot. Aaron has a passion for building robots and robot companies. He has founded four companies focused on commercializing human collaborative robots. Two of these companies, Meka Robotics and Redwood Robotics, were acquired by Google in 2013. As Robotics Director at Google, Aaron led the business, product, and technical development of two of Google’s central investments in robotics. Aaron received his Ph.D. from MIT CSAIL in 2007. Learn more about Aaron and Hello Robot: https://hello-robot.com/

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Stanford Seminar - Beyond "Bots and Trolls" - Understanding Disinformation as Collaborative Work

Mon, 12 Oct 2026 17:32:00 -0000

Kate Starbird
University of Washington

October 11, 2019
Strategic information operations (e.g. disinformation, political propaganda, and other forms of online manipulation) are a critical concern for democratic societies-as they destabilize the ""common ground"" that we need to stand upon to govern ourselves. In this talk, I argue that defending against strategic information operations will require a more nuanced understanding of the problem. In particular, we will need to move beyond focusing on ""bots"" and ""trolls"" to looking at the collaborative nature of disinformation campaigns that target, infiltrate, shape, and leverage online communities. Drawing from three distinct case studies, I describe how orchestrated campaigns can become deeply entangled within ""organic"" online crowds and I highlight a persistent challenge for researchers, platform designers, and policy makers-distinguishing between orchestrated, explicitly-coordinated information operations and the emergent, organic behaviors of an online crowd.

Uploaded 23-10-19

Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true

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Stanford Seminar - Web3: Real Value vs. Bubble

Mon, 12 Oct 2026 13:32:00 -0000

December 5, 2022
Kun Peng of Stanford University, Aaron Greenblatt of IronMill, Inc, Gil Rosen of Stanford University, Philipp Reineke of Stanford University, and Priyanka Mathikshara Mathialagan of Stanford University.

Uploaded 16-12-22

Stanford CEE246A - Web 3 Entrepreneurship: Future Models of value Creation Connecting the Real and Digital Economy

#web3

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Stanford Seminar - Self-Supervised Pseudo-Lidar Networks

Mon, 12 Oct 2026 09:32:00 -0000

Adrien Gaidon
Toyota Research Institute

October 11, 2019
Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception, especially in safety critical contexts like Automated Driving. Nonetheless, recent progress in combining deep learning and geometry suggests that cameras may become a competitive source of reliable 3D information. In this talk, we will present our latest developments in self-supervised monocular depth and pose estimation for urban environments. Particularly, we show that with the proper network architecture, large-scale training, and computational power it is possible to outperform fully supervised methods while still operating on the much more challenging self-supervised setting, where the only source of input information are video sequences. Furthermore, we discuss how other sources of information (i.e. camera velocity, sparse LiDAR data, and semantic predictions) can be leveraged at training time to further improve pseudo-lidar accuracy and overcome some of the inherent limitations of self-supervised learning.

Uploaded 25-10-19

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD

0:00 Introduction
2:46 Pseudo-LiDAR / Monocular Depth Estimation
6:00 Machine Learning at Scale: Beyond Supervised Learning
9:33 Self-Supervised Monocular Depth
10:36 Self-Supervised Depth Learning Objective
11:24 Photometric Loss ++
13:30 Depth Super-Resolution
14:26 Dense Monocular 3D Reconstruction
23:41 Packing/Unpacking blocks
27:09 Velocity Scaling
37:25 Two Stream Nets for Self-Supervised VO
39:55 Why Semi-Supervised?
40:32 Semi = Self + Reprojected(Fully)
44:21 Semi-Supervised PackNet
46:43 Semantically-Guided Depth Network
49:54 Class-Specific Depth Evaluation
50:21 Two-Stage Training for Infinite Depth

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Stanford CS309A: 7 Life Lessons from Dr. Timothy Chou

Mon, 12 Oct 2026 05:32:00 -0000

Autumn 2022, Stanford University

Dr. Timothy Chou details 7 life lessons:

1. Selling is Teaching
2. Teach Insight, Teach the Not
3. Teach with Story
4. Power of Language
5. Manage Your Career
6. Best Email Address You'll Ever Have
7. Not Work-Life Balance

Uploaded 14-12-22

About the speaker:
Dr. Chou’s impressive background includes a return to Oracle in 1999 to become the first President of Oracle’s cloud computing business. Dr. Chou’s experiences at Oracle led to his landmark book ‘The End of Software’, released in 2004, which predicted the emerging software as a service business model. The book featured four startups, including Salesforce, VMWare, and NetSuite. Next, in 2005 Timothy returned to Stanford and started the first class on cloud computing. Most recently in 2020, he launched a company focused on delivering an edge cloud to transform pediatric healthcare.

Watch Dr. Chou's latest webinar here: https://youtu.be/qrBriiPmZHA

Chapters:
0:00 Introduction
0:17 Selling is Teaching
3:39 Teach Insight, Teach the Not
14:17 Power of Language
19:17 Manage Your Career
23:37 Best Email Address You'll Ever Have
25:57 Not Work-Life Balance

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Stanford Seminar - Tales from the Risks Forum

Mon, 12 Oct 2026 01:32:00 -0000

Peter Neumann
SRI International

October 16, 2019
Peter G. Neumann has moderated the ACM Risks Forum (risks.org) since its inception in 1985. RISKS has reported and discussed a broad spectrum of problems in computer systems, misconceptions, and human failings over the years. In an unusual format for EE380, this session will revisit some of more memorable risks discovered and how they were mitigated.

Uploaded 25-10-19

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMWw6rRoeSpkiseTHzWj6vu

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Stanford Webinar - Digital Transformation: Beyond the Technology

Sun, 11 Oct 2026 21:32:00 -0000

Learn more about the Stanford Digital Transformation Program: https://online.stanford.edu/programs/digital-transformation-program

When you think of digital transformation, flashy new tech might come to mind… cloud computing, the internet of things, or artificial intelligence. But in this webinar, we’ll explore some of the not-so-obvious aspects of digital transformation: company culture, systems thinking, and more.

Uploaded 14-12-22

We’ll sit down with Pamela Hinds, Stanford Professor and Academic Director of the Digital Transformation Program, to learn more about the not-so-technical side of digital transformation and where the future of this hot topic is headed.

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Stanford Seminar - Finding the Great Problems

Sun, 11 Oct 2026 17:32:00 -0000

Barbara Liskov
MIT CSAIL

October 16, 2019
Barbara Liskov was already breaking new ground in 1968, when she became one of the first American women to earn a doctorate in the emerging discipline of computer science. After receiving that PhD at Stanford, she went on to design several influential programming languages, including CLU, an important precursor to Java. More recently, as an Institute Professor at MIT and head of the institute’s Programming Methodology Group, she has undertaken crucial research on distributed systems, information security and complex system failure issues. She is one of fewer than 100 individuals to receive an A.M. Turing Award from the Association of Computing Machinery. In a conversation with host Ann Miura-Ko, a lecturer in Stanford’s Department of Management Science and Engineering and founding partner of the venture capital firm Floodgate, Liskov explores how she discovered the nascent field of computer science, how she recognized and surmounted a number of fundamental computing challenges, and shares her concerns and hopes about how computing will continue to transform our lives.

Uploaded 29-10-19

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

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Stanford Seminar - Can we govern algorithms with science?

Sun, 11 Oct 2026 13:32:00 -0000

J. Nathan Matias of Cornell University

The governance of adaptive algorithms is one of society s most pressing scientific challenges. In public discourse, these algorithms decide what we are allowed to say and rank the information we see. Because they adapt to human behaviors that they also influence, their actions have been hard to predict representing a risk to society and a challenge for anyone who would govern their behavior.

Uploaded 12-12-22

What kinds of knowledge can help us observe and govern these feedback loops between human and machine behavior? And how might this governance challenge require us to rethink how we design the software systems that support this research? This talk will summarize the dilemma of predicting, preventing, and intervening effectively on human-algorithm behavior, and how we might develop the knowledge needed to do so.

About the speaker:
Dr. J. Nathan Matias organizes citizen behavioral science for a safer, fairer, more understanding internet. A Guatemalan-American, Nathan is an assistant professor in the Cornell University Department of Communication and field member in Information Science.

Nathan is a 2022-23 Lenore Annenberg and Wallis Annenberg Fellow in Communication and Siegel Research Fellow at the Center for Advanced Study in the Behavioral Sciences, Stanford University. In the summer of 2022-23, he is also an visiting associate research scholar at the Knight First Amendment Institute at Columbia University.

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Stanford Seminar - Software is Hard Work

Sun, 11 Oct 2026 09:32:00 -0000

Edith Harbaugh
LaunchDarkly

October 23, 2019
LaunchDarkly now helps over 1,000 customers — including major companies like Atlassian and BMW — release code, monitor and manage features, and make data-driven decisions about software functionality. But growth didn’t come overnight, explains CEO and co-founder Edith Harbaugh. She describes the multi-year slog of scaling up a B2B company, and demonstrates how she made the most of a number of less-than-ideal jobs, building a diverse toolkit of skills that ultimately contributed to her success as a founder and CEO. She urges entrepreneurs to draw encouragement from small wins, especially in the early stages, when customers are few and far between.

Uploaded 30-10-19

Learn more about Stanford's eCorner: https://ecorner.stanford.edu
Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs
See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC

#software

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Bayesian Networks 5 - Forward-backward Algorithm | Stanford CS221: AI (Autumn 2021)

Fri, 09 Oct 2026 09:32:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang

Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department and Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh

Uploaded 31-05-22

To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.io/autumn2021/#schedule

0:00 Introduction
0:06 Bayesian networks: forward-backward
0:16 Hidden Markov models for object tracking
2:47 Inference questions
4:16 Lattice representation
8:31 Forward and backward messages
11:42 Putting everything together
15:31 Summary

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Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 7 - Translation, Seq2Seq, Attention

Fri, 09 Oct 2026 05:32:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/3CnshYl

This lecture covers:
1. Introduce a new task: Machine Translation [15 mins] - Machine Translation (MT) is the task of translating a sentence x from one language (the source language) to a sentence y in another language (the target language).
2. A new neural architecture: sequence-to-sequence [45 mins] - Neural Machine Translation (NMT) is a way to do Machine Translation with a single end-to-end neural network. The neural network architecture is called a sequence-to-sequence model (aka seq2seq) and it involves two RNNs.
3. A new neural technique: attention [20 mins] - Attention provides a solution to the bottleneck problem.Core idea: on each step of the decoder, use direct connection to the encoder to focus
on a particular part of the source sequence.

Uploaded 28-10-21

To learn more about this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning
To follow along with the course schedule and syllabus visit: http://web.stanford.edu/class/cs224n/

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

#naturallanguageprocessing #deeplearning

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Bayesian Networks 4 - Probabilistic Inference | Stanford CS221: AI (Autumn 2021)

Fri, 09 Oct 2026 01:32:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang

Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department and Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh

Uploaded 31-05-22

To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.io/autumn2021/#schedule

0:00 Introduction
0:06 Bayesian networks: probabilistic inference
0:12 Review: Bayesian network
1:31 Review: probabilistic inference
2:17 Reduction to Markov networks
4:57 Conditioning on evidence
8:11 Leveraging additional structure: unobserved leaves
11:51 Leveraging additional structure: independence
14:08 Summary

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Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 8 - Final Projects; Practical Tips

Thu, 08 Oct 2026 21:32:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/3CrtdLe

This lecture covers:
1. Attention [25 mins]
2. Final bit of neural machine translation [10 mins]
3. Final project types and details; assessment revisited [15 mins]
4. Finding research topics; a couple of examples [20 mins]
5. Finding data [10 mins]
6. Care with datasets and in model development [10 mins]

Uploaded 28-10-21

To learn more about this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning
To follow along with the course schedule and syllabus visit: http://web.stanford.edu/class/cs224n/

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

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Bayesian Networks 3 - Probabilistic Programming | Stanford CS221: AI (Autumn 2021)

Thu, 08 Oct 2026 17:32:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang

Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department and Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh

Uploaded 31-05-22

To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.io/autumn2021/#schedule

0:00 Introduction
0:06 Bayesian networks: probabilistic programming
0:19 Probabilistic programs
2:38 Probabilistic program: example
4:35 Probabilistic inference: example
5:16 Application: language modeling Can be used to score sentences for speech recognition or machine translation
6:15 Application: object tracking
7:28 Application: multiple object tracking
8:46 Application: document classification
9:43 Application: topic modeling
11:04 Application: medical diagnosis
12:26 Application: social network analysis
13:49 Summary

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Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 9 - Self- Attention and Transformers

Thu, 08 Oct 2026 13:32:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/3CvTOGY

This lecture covers:
1. Impact of Transformers on NLP (and ML more broadly)
2. From Recurrence (RNNs) to Attention-Based NLP Models
3. Understanding the Transformer Model
4. Drawbacks and Variants of Transformers

Uploaded 28-10-21

To learn more about this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning
To follow along with the course schedule and syllabus visit: http://web.stanford.edu/class/cs224n/

John Hewitt
PhD student in Computer Science at Stanford University

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

#deeplearning #naturallanguageprocessing

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Bayesian Networks 2 - Definition | Stanford CS221: AI (Autumn 2021)

Thu, 08 Oct 2026 09:32:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang

Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department and Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh

Uploaded 31-05-22

To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.io/autumn2021/#schedule

0:00 Introduction
0:06 Bayesian networks: definitions
0:15 Review: probability
5:38 A puzzle
8:00 Bayesian network (alarm)
12:10 Probabilistic inference (alarm)
18:03 Explaining away
19:55 Medical diagnosis
24:54 Bayesian network (definition)
25:58 Probabilistic inference (definition)
27:24 Summary

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Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 10 - Transformers and Pretraining

Thu, 08 Oct 2026 05:32:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/3bDcbOJ

This lecture covers:
1. Quick review of Transformer model
2. Brief note on subword modeling
3. Motivating model pretraining from word embeddings
4. Model pretraining three ways
1. Decoders
2. Encoders
3. Encoder-Decoders
5. Very large models and in-context learning

Uploaded 28-10-21

To learn more about this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning
To follow along with the course schedule and syllabus visit: http://web.stanford.edu/class/cs224n/

John Hewitt
PhD student in Computer Science at Stanford University

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

#naturallanguageprocessing #deeplearning

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Bayesian Networks 1 - Overview | Stanford CS221: AI (Autumn 2021)

Thu, 08 Oct 2026 01:32:00 -0000

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Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 12 - Question Answering

Wed, 07 Oct 2026 21:32:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/2ZytY6G

To learn more about this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning
To follow along with the course schedule and syllabus visit: http://web.stanford.edu/class/cs224n/

Danqi Chen
Assistant Professor, Department of Computer Science
Princeton University

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

Uploaded 28-10-21

#naturallanguageprocessing #deeplearning

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Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 12 - Natural Language Generation

Wed, 07 Oct 2026 13:32:00 -0000

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/3nKbk4p

To learn more about this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning
To follow along with the course schedule and syllabus visit: http://web.stanford.edu/class/cs224n/

Antoine Bosselut
Assistant Professor, School of Computer and Communication Sciences at EPFL

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

Uploaded 28-10-21

#naturallanguageprocessing #deeplearning

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Markov Networks 1 - Overview | Stanford CS221: Artificial Intelligence (Autumn 2021)

Wed, 07 Oct 2026 09:32:00 -0000

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