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Stanford AI Lab

The Stanford Artificial Intelligence Laboratory (SAIL) has been a center of excellence for Artificial Intelligence research, teaching, theory, and practice since its founding in 1963.

Latest News

Congratulations to stanford ai lab phd student dora zhao for an icml 2024 best paper award.

Congratulations to Stanford AI Lab PhD student Dora Zhao for an ICML 2024 Best Paper Award for a paper from her work at Sony AI on: Measure Dataset Diversity, Don’t Just Claim It

Congratulations to Aaron Lou, Chenlin Meng, and Stefano Ermon for an ICML 2024 Best Paper Award!

Congratulations to Aaron Lou, Chenlin Meng, and Stefano Ermon for an ICML 2024 Best Paper Award: Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution

Congratulations to Marco Pavone for a Robotics: Science and Systems Conference Best Paper Award!

Congratulations to Marco Pavone for winning the best paper award at the Robotics: Science and Systems Conference on AI Safety for autonomous systems.

Congratulations to Carlos Guestrin for being elected to the NAE!

Carlos Guestrin has been elected to the National Academic of Engineering “for scalable systems and algorithms enabling the broad application of machine learning in science and industry.”

Congratulations to Chris Manning on being awarded 2024 IEEE John von Neumann Medal!

Chris Manning has been awarded the 2024 IEEE John von Neumann Medal “for advances in computational representation and analysis of natural language.” This is one of IEEE’s top awards in computing, given with very broad scope “for outstanding achievements in computer-related science and technology.”

SAIL Faculty and Students Win NeurIPS Outstanding Paper Awards

Congratulations to Sanmi Koyejo and his students for winning the NeurIPS Outstanding Paper Award, and congradulations to Chris Manning, Stefano Ermon, Chelsea Finn, and their students for winning Outstanding Paper Runner Up at NeurIPS!

Prof. Fei Fei Li featured in CBS Mornings the Age of AI

Follow Prof. Li's interview with CBS Mornings and on being named the "Godmother of AI"

Latest Tweets

It was a great pleasure to learn about the research work @sunfanyun from @StanfordAILab is doing. Looking forward to a future where AI generates 3D words for RL agents and #Robotics training. Watch the lecture on the BuzzRobot channel https://youtu.be/TD6OILO51Vo?si=KeQ2vkZVgPo31Wyt #MachineLearning…

How do you choose the right data mixture when pretraining generalist policies? Curating the pretraining dataset is particularly important in settings that don't have the luxury of access to internet scale data . ReMix curates robotics datasets via robust optimization🧵👇

As imitation learning policies continue to scale, deciding how to weigh different robot datasets will become even more difficult. To address this problem we introduce ReMix, a method for automatically curating large RT-X scale imitation learning datasets. 🧵(1/5)

To scale up robotics, we need to borrow from NLP's generalization toolkit. But we shouldn't take for granted that these tools will work out of the box. Great work from Joey et al. to adapt DRO to robotics datasets:

Code is out for COAST - a new probabilistically-complete, sampling-based TAMP algorithm that combines the stream-based motion planning with an efficient constrained task planning strategy. https://branvu.github.io/coast.github.io/

So excited to share our new preprint, “LLMs generate structurally realistic social networks but overestimate political homophily”! Joint work co-led by @a_chaszczewicz, along with Emma Wang, Maya Josifovska, @2plus2make5, and @jure Paper: https://arxiv.org/abs/2408.16629

We Are Pleased to Welcome New Members of Our Faculty

Diyi Yang who focuses on Computational Social Science and Natural Language Processing

Sanmi Koyejo who focuses on Trustworthy Machine Learning for Healthcare and Neuroscience

See the Entire Faculty

Affiliates Program

Stanford AI Lab faculty and students enjoy chances to understand and solve the not-yet-doable pain points of industry. Get a chance to support and interact with SAIL’s brightest minds.

Email forwarding for @cs.stanford.edu is changing. Updates and details here .

Academics | PhD Program

Main navigation.

The PhD degree is intended primarily for students who desire a career in research, advanced development, or teaching. A broad Computer Science, Engineering, Science background, intensive study, and research experience in a specialized area are the necessary requisites.

The degree of Doctor of Philosophy (PhD) is conferred on candidates who have demonstrated to the satisfaction of our Department in the following areas:

• high attainment in a particular field of knowledge, and
• the ability to do independent investigation and present the results of such research.

They must satisfy the general requirements for advanced degrees, and the program requirements specified by our Department.

Program Requirements

On average, the program is completed in five to six years, depending on the student’s research and progress.

Progress Guidelines

Students should consider the progress guidelines to ensure that they are making reasonable progress.

Monitoring Progress

Annual reviews only apply to PhD students in their second year or later; yearly meetings are held for all PhD students.

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AI & Machine Learning

Organizations delivering services through digital technology have the opportunity to use machine learning and artificial intelligence to improve their products and services.

When training and education are provided through digital technology, it is possible to customize the individual’s experience based on their characteristics as well as their prior interaction with the application. Machine learning methods can be applied to estimate what works for whom, and why. The lab specializes in adapting machine learning methods to focus on heterogeneity in the effect of interventions. Planning experiments with the goal of learning about heterogeneity is another area of interest.

Another area of focus is developing algorithms that are used to improve personalization for digital services. Innovations in machine learning in the last decade have led to improvements in algorithms for making recommendations to individuals about content, and these can be applied to, for example, recommend stories to students in an educational application. Artificial intelligence can be used to “get to know” an individual and adapt the information presented based on previous experience; the lab makes use of methods such as multi-armed bandit algorithms and reinforcement learning to accomplish this.

Many commonly used methods in artificial intelligence are designed for large systems with many users, in environments where mistakes have few consequences. The lab works to customize and improve these algorithms for the context in which we apply them, where the user base may be smaller and mistakes more costly, such as educational applications.

Project Abstracts

Read about a few of the research projects the lab is currently working on.

Academic Publications

Policy learning with adaptively collected data, labor-llm: language-based occupational representations with large language models, using wasserstein generative adversarial networks for the design of monte carlo simulations, career: a foundation model for labor sequence data, digital interventions and habit formation in educational technology, machine learning & causal inference.

This course is a series of videos designed for any audience looking to learn more about how machine learning can be used to measure the effects of interventions, understand the heterogeneous impact of interventions, and design targeted treatment assignment policies.

Thought Leadership

Susan athey awarded the msri prize in innovative quantitative applications.

A fireside chat with GSB Professor Susan Athey, the first woman to receive the CME Group-MSRI Prize in Innovative Quantitative Applications. She was interviewed by GSB Professor Paul Milgrom.

How Does Digitization & AI Impact Our Economy? Keynote Address

In her keynote address, Susan Athey described the ways in which COVID-19 affects the ongoing structural trend towards digitalization and automation, especially in advanced economies.

Congress Probes How AI Will Impact U.S. Economic Recovery

Helping workers find job or skill training will be an important part of addressing the instability and job loss brought on by AI, said Stanford [Professor] Susan Athey, but the United States hasn’t historically done a great job of helping displaced workers impacted by automation.

Computational Social Science: Obstacles and Opportunities

The field of computational social science has exploded in prominence over the past decade but the field has also fallen short in important ways. We suggest opportunities to address these issues, especially in improving the alignment between the organization of the 20th-century university and the intellectual requirements of the field.

A New Measure of Segregation

As attention on race relations increases across the nation, Stanford economists Susan Athey and Matthew Gentzkow have used a new way of measuring segregation that can provide clues to different ways for combatting its negative effects.

When the Best AI Isn’t Necessarily the Best AI

Why organizations might want to design and train less-than-perfect AI.

Three Big Ideas

What might've seemed absurd just a few months ago is what we need to be trying right now: building useless factories for a good reason. It is economically beneficial to invest in capacity early, at risk.

Preventing ‘Cytokine Storm’ May Ease Severe COVID-19 Symptoms

A clinical trial in people with the new coronavirus is testing a drug that may halt an overactive immune response before it ramps up.

Experts’ 7 Best Ideas on How to Beat COVID-19 and Save the Economy

Multiple countries have suppressed the coronavirus and significantly curbed the rate of new infections. America’s leading biological and social scientists say there is plenty the United States can do here, too.

Susan Athey: Bringing an Economist's Perspective to Data Science

Susan Athey, Economics of Technology Professor at the Stanford Graduate School of Business, brings an economist’s expertise and perspective to machine learning and data science. With a prolific career spanning academia and industry, Susan’s research focuses on the economics of digitization, marketplace design, and the intersection of econometrics and machine learning.

AI in 10 Minutes: Susan Athey

How does AI work? How will it impact business, help government become more efficient and be beneficial for social impact?

HAI 2019 Fall Conference: AI and the Economy, with Susan Athey and Erik Brynjolfsson

Stanford Graduate School of Business professor Susan Athey Ph.D. ’95 and MIT Sloan professor Erik Brynjolfsson spoke about the effect of artificial intelligence on the economy.

Machine Learning and Economics

Stanford’s Susan Athey discusses the extraordinary power of machine-learning and AI techniques, allied with economists’ know-how, to answer real-world business and policy problems. With a host of new policy areas to study and an exciting new toolkit, social science research is on the cusp of a golden age. Economics, in particular, will never be the same again.

Susan Athey: Why Business Leaders Shouldn't Have Blind Faith in AI

Telling cats from dogs is easy. It's the what-ifs that get problematic.

Susan Athey: The Economics of Technology Professor

Economics is an area that allows you to approach important issues that have a lot of impact on people. Susan Athey’s pioneering work as a “tech economist” has helped industry and academia alike better understand the constantly shifting digital era.

Beyond Prediction: Using Big Data for Policy Problems

Machine-learning prediction methods have been extremely productive in applications ranging from medicine to allocating fire and health inspectors in cities. However, there are a number of gaps between making a prediction and making a decision, and underlying assumptions need to be understood in order to optimize data-driven decision-making.

The Wonk, Episode 2: Machine Learning for Policy Analysis

Menbere Shiferaw, PhD Student at the NYU Wager Graduate School of Public Service, and Dr. Susan Athey, Professor of Economics and Technology at the Stanford Graduate School of Business, investigate machine learning applications in the context of policy analysis.

What Will the Impact of Machine Learning Be on Economics?

"The short answer is that I think it will have an enormous impact; in the early days, as used “off the shelf,” but in the longer run econometricians will modify the methods and tailor them so that they meet the needs of social scientists primarily interested in conducting inference about causal effects and estimating the impact of counterfactual policies (that is, things that haven’t been tried yet, or what would have happened if a different policy had been used)."

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Stanford Online

Welcome, artificial intelligence.

• AI Courses and Programs
• AI for Engineers and Technical Professionals
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• AI for Professionals in Healthcare

Stanford AI Programs FAQs

These FAQs will be helpful to you in getting answers to common questions for both the AI Graduate Program and the AI Professional Program. To easily compare the programs we’ve put together this helpful video .

Do I need to apply before I can enroll in the program?

AI Professional Program: Yes, here is a link to the short application. The application allows you to share more about your interest in joining, as well as verify that you meet the prerequisite requirements needed to make the most of the experience.

AI Graduate Program: Yes, learn more about the graduate application process.

What can I expect from the course assignments?

AI Professional Program: Written homework and programming assignments are basically the same as the graduate assignments, but they have been adapted to include more guided support and scaffolding – including more in-line programming guidance as well as milestone code checks that enable a learner to incrementally verify if they are working in the right direction and troubleshoot their errors. All final exams, most projects, and some assignments have been removed in order to make the course more manageable for professional audiences.

AI Graduate Program: Homework assignments come directly from the lectures and are assigned by the professor for the course. The instructor and assignments may vary every quarter.

What can I expect from the lectures?

AI Professional Program: Lecture videos come from the original graduate course and have been edited for brevity – removing classroom logistics, extraneous questions, and some tangential material not associated with the core material. In addition, videos have been segmented by topic for easier reference, viewing, and review. They will be available to view online so you can watch them at your own pace.

AI Graduate Program: Lectures will be live streamed and available to view online. Each lecture takes place on campus and will be recorded for students taking courses remotely.

Is the program in person or online?

AI Professional Program: The AI courses in the professional program are fully online.

AI Graduate Program: The AI courses in the graduate program are recorded live and available to remote students. All students enrolled in courses from this program will be able to watch the lectures with a 20min delay, 24/7 until the end of the quarter.

Will I have the opportunity to meet with the course faculty?

AI Professional Program: This program utilizes pre-recorded lecture videos with Stanford faculty. Throughout the program you will get a chance to interact with Stanford-affiliated Course Facilitators who took the original graduate course and work in the industry. While not guaranteed, we always try to schedule a session with faculty, where you can openly ask your questions about the field.

AI Graduate Program: This program will have online/virtual office hours for remote students. The times and schedules for each course will be announced by the teaching team at the beginning of the quarter.

What do I need to do to get the course certificate?

AI Professional Program: AI Professional courses are pass/fail and in order to earn the digital Certificate of Achievement associated with each course, you must complete the assignments with a total cumulative score of 70% or higher.

AI Graduate Program: All students are automatically enrolled in courses for a letter grade in this program and must have a B or better.

What credential will I receive after completing the program?

AI Professional Program: After successfully completing the program, you will receive a digital Professional Certificate from the Stanford School of Engineering.

AI Graduate Program: After successfully completing the program, you will receive a graduate certificate from the Stanford School of Engineering. You will also receive academic credit for each course you completed and a transcript from Stanford University. Up to 18 credits can be transferred into a Stanford MS program if a student applies and is admitted into the degree.

How long do I have to complete each course in the program?

AI Professional Program: Each course lasts 10-weeks. Assignments will be released and due based on the course schedule.

AI Graduate Program: Depending on which quarter you are enrolled, you will have about 10 weeks to complete each course in the program. Please review the timelines provided for the quarter while you are enrolling.

How long do I have to complete the entire program?

AI Professional Program: There is no deadline to complete the AI Professional Program

AI Graduate Program: You will have 3 years to complete the artificial intelligence graduate certificate program.

Which courses do I need to complete for the program certificate?

AI Professional Program: You may earn a Stanford Professional Certificate in Artificial Intelligence by either:

• Completing any three courses in the AI Professional Program.
• Completing any two courses in the AI Professional Program and one course in the AI Graduate Program. Courses in the AI Graduate Program require a separate application.

AI Graduate Program: Complete four courses including 1-2 required course(s) and 2-3 electives within 3 academic years.

How long will I have access to the materials?

AI Professional Program: Course materials are available for 90 days after the course ends.

AI Graduate Program: Course materials are available for the entire duration of the quarter of which you are enrolled.

When was the content last updated?

AI Professional Program: All courses are up to date. After each graduate course ends, we lead a conversation with faculty to determine if any and what changes are needed for the professional course offering, and make them based on faculty preference.

AI Graduate Program: The content for the course may change based on the professor teaching the course, the quarter it’s being offered.

Do you offer any free courses, discounts, or financial aid?

At this time we do not provide financial aid or discounts to individuals. If you are interested in viewing the lecture videos, you can do so for free by clicking here http://bit.ly/scpd-ai .

You may also view all of our free courses and content and watch graduate lectures on our YouTube Channel.

I still have questions, who can I contact?

AI Professional Program: Get in touch with our team by emailing  [email protected]

AI Graduate Program: Get in touch with our team by emailing  [email protected]

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