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peopleLeadership,Faculty,Senior Fellow

Fei-Fei Li

Denning Co-Director, Stanford HAI | Sequoia Professor of Computer Science, Stanford University

fei fei li headshot
External Bio

Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and Co-Director of Stanford HAI. She served as the Director of Stanford’s AI Lab from 2013 to 2018. And during her sabbatical from Stanford from January 2017 to September 2018, she was Vice President at Google and served as Chief Scientist of AI/ML at Google Cloud. Li obtained her B.A. degree in physics from Princeton in 1999 with High Honors, and her PhD degree in electrical engineering from California Institute of Technology (Caltech) in 2005. She joined Stanford in 2009 as an assistant professor. Prior to that, she was on faculty at Princeton University (2007-2009) and University of Illinois Urbana-Champaign (2005-2006).

Li’s current research interests include cognitively inspired AI, machine learning, deep learning, computer vision and AI+healthcare especially ambient intelligent systems for healthcare delivery. In the past she has also worked on cognitive and computational neuroscience. Li has published more than 200 scientific articles in top-tier journals and conferences, including Nature, PNAS, Journal of Neuroscience, CVPR, ICCV, NIPS, ECCV, ICRA, IROS, RSS, IJCV, IEEE-PAMI, New England Journal of Medicine, Nature Digital Medicine, etc. Li is the inventor of ImageNet and the ImageNet Challenge, a critical large-scale dataset and benchmarking effort that has contributed to the latest developments in deep learning and AI. In addition to her technical contributions, she is a national leading voice for advocating diversity in STEM and AI. She is co-founder and chairperson of the national non-profit AI4ALL aimed at increasing inclusion and diversity in AI education.

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Latest Related to Fei-Fei Li

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Firing Line with Margaret Hoover

PBS
May 23

In this video, HAI Co-Director Fei-Fei Li discusses ethical development of artificial intelligence and the challenge of establishing effective regulations. She addresses government funding of research, diversity in science, and ensuring child safety as AI advances.

media mention
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Fei-Fei Li, ‘Godmother Of AI,’ Points To Risks Of Cuts To US Research Funds, Student Visas

Semafor
May 21

Fei-Fei Li, co-director of Stanford HAI, emphasized the risks of cutting research funding and international student visas to the US as it faces an increasingly competitive global tech race.

media mention
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AI Pioneer Fei-Fei Li Says AI Policy Must Be Based On ‘Science, Not Science Fiction’

TechCrunch
Regulation, Policy, GovernanceFeb 08

Fei-Fei Li, Co-Director of Stanford HAI, outlines “three fundamental principles for the future of AI policymaking” ahead of the AI Action Summit in Paris.

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Now More Than Ever, AI Needs A Governance Framework
Financial Times
Feb 07, 2025
media mention

Fei Fei Li, Co-Director of Stanford HAI, stresses the importance of governance for AI technologies. 

Now More Than Ever, AI Needs A Governance Framework

Financial Times
Feb 07, 2025

Fei Fei Li, Co-Director of Stanford HAI, stresses the importance of governance for AI technologies. 

Regulation, Policy, Governance
media mention
Fei-Fei Li Says Understanding How The World Works Is The Next Step For AI
The Economist
Nov 20, 2024
media mention

Stanford HAI co-director Fei-Fei Li says the next frontier in AI lies in advancing spatial intelligence. In this op-ed, she explains how enabling machines to perceive and interact with the world in 3D can unlock human-centered AI applications for robotics, healthcare, education, and beyond.

Fei-Fei Li Says Understanding How The World Works Is The Next Step For AI

The Economist
Nov 20, 2024

Stanford HAI co-director Fei-Fei Li says the next frontier in AI lies in advancing spatial intelligence. In this op-ed, she explains how enabling machines to perceive and interact with the world in 3D can unlock human-centered AI applications for robotics, healthcare, education, and beyond.

Robotics
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Unlocking Public Sector AI Innovation: Next Steps for the National AI Research Resource
seminarOct 31, 20239:00 AM - 10:00 AM
October
31
2023

On Monday, October 30th 2023, President Biden signed a landmark Executive Order to manage the opportunities and risks of artificial intelligence.

October
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2023

Unlocking Public Sector AI Innovation: Next Steps for the National AI Research Resource

Oct 31, 20239:00 AM - 10:00 AM

On Monday, October 30th 2023, President Biden signed a landmark Executive Order to manage the opportunities and risks of artificial intelligence.

Fei Fei Li's Testimony Before the Senate Committee on Homeland Security and Governmental Affairs
Fei-Fei Li
Sep 14, 2023
testimony

We have arrived at an inflection point in the world of AI, largely propelled by breakthroughs in generative AI, including increasingly sophisticated language models like GPT-4. These models have revolutionized various sectors from customer service to adaptive learning. However, the scope of intelligence is far broader than linguistic capability alone. In my specialized field of computer vision, we have also witnessed remarkable advancements that empower machines to analyze and act upon visual information—essentially teaching computers to 'see.'

Fei Fei Li's Testimony Before the Senate Committee on Homeland Security and Governmental Affairs

Fei-Fei Li
Sep 14, 2023

We have arrived at an inflection point in the world of AI, largely propelled by breakthroughs in generative AI, including increasingly sophisticated language models like GPT-4. These models have revolutionized various sectors from customer service to adaptive learning. However, the scope of intelligence is far broader than linguistic capability alone. In my specialized field of computer vision, we have also witnessed remarkable advancements that empower machines to analyze and act upon visual information—essentially teaching computers to 'see.'

Government, Public Administration
International Affairs, International Security, International Development
testimony
Generative AI: Perspectives from Stanford HAI
Peter Norvig, Erik Brynjolfsson, James Landay, Michele Elam, Daniel E. Ho, Rob Reich, Surya Ganguli, Christopher Manning, Curtis Langlotz, Vanessa Parli, Percy Liang, Fei-Fei Li, Russ Altman
Deep DiveMar 01, 2023
Research

A diversity of perspectives from Stanford leaders in medicine, science, engineering, humanities, and the social sciences on how generative AI might affect their fields and our world

Generative AI: Perspectives from Stanford HAI

Peter Norvig, Erik Brynjolfsson, James Landay, Michele Elam, Daniel E. Ho, Rob Reich, Surya Ganguli, Christopher Manning, Curtis Langlotz, Vanessa Parli, Percy Liang, Fei-Fei Li, Russ Altman
Deep DiveMar 01, 2023

A diversity of perspectives from Stanford leaders in medicine, science, engineering, humanities, and the social sciences on how generative AI might affect their fields and our world

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Evaluating Facial Recognition Technology: A Protocol for Performance Assessment in New Domains
Maneesh Agrawala, Daniel E. Ho, Fei-Fei Li, Emily Black
Nov 01, 2021
whitepaper

Facial recognition technology (FRT), namely the set of computer vision techniques to identify individuals from images, has proliferated throughout society. Individuals use FRT to unlock smartphones, computer appliances, and cars.3 Retailers use FRT to monitor stores for shoplifters and perform more targeted advertising.Banks use FRT as an identification mechanism at ATMs. Airports and airlines use FRT to identify travelers.

Evaluating Facial Recognition Technology: A Protocol for Performance Assessment in New Domains

Maneesh Agrawala, Daniel E. Ho, Fei-Fei Li, Emily Black
Nov 01, 2021

Facial recognition technology (FRT), namely the set of computer vision techniques to identify individuals from images, has proliferated throughout society. Individuals use FRT to unlock smartphones, computer appliances, and cars.3 Retailers use FRT to monitor stores for shoplifters and perform more targeted advertising.Banks use FRT as an identification mechanism at ATMs. Airports and airlines use FRT to identify travelers.

whitepaper
Assessing the accuracy of automatic speech recognition for psychotherapy
Arnold Milstein, Bruce Arnow, Stewart Agras, Dan Jurafsky, Nigam Shah, Jason Fries, Fei-Fei Li, Adam Miner, Albert Haque, Denise Wilfley, Terence Wilson, Scott Fleming
Dec 28, 2020
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Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings has not been well described. It is unclear which metrics and thresholds are appropriate for different clinical use cases, which may range from population descriptions to individual safety monitoring.

Assessing the accuracy of automatic speech recognition for psychotherapy

Arnold Milstein, Bruce Arnow, Stewart Agras, Dan Jurafsky, Nigam Shah, Jason Fries, Fei-Fei Li, Adam Miner, Albert Haque, Denise Wilfley, Terence Wilson, Scott Fleming
Dec 28, 2020

Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings has not been well described. It is unclear which metrics and thresholds are appropriate for different clinical use cases, which may range from population descriptions to individual safety monitoring.

Research
Representation Learning with Statistical Independence to Mitigate Bias
Juan Carlos Niebles, Ehsan Adeli, Kilian Pohl, Fei-Fei Li, Adolf Pfefferbaum, Edith Sullivan, Qingyu Zhao
Dec 03, 2020
Research

Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years. Such challenges range from spurious associations between variables in medical studies to the bias of race in gender or face recognition systems. Controlling for all types of biases in the dataset curation stage is cumbersome and sometimes impossible. The alternative is to use the available data and build models incorporating fair representation learning. In this paper, we propose such a model based on adversarial training with two competing objectives to learn features that have (1) maximum discriminative power with respect to the task and (2) minimal statistical mean dependence with the protected (bias) variable(s). Our approach does so by incorporating a new adversarial loss function that encourages a vanished correlation between the bias and the learned features. We apply our method to synthetic data, medical images (containing task bias), and a dataset for gender classification (containing dataset bias). Our results show that the learned features by our method not only result in superior prediction performance but also are unbiased.

Representation Learning with Statistical Independence to Mitigate Bias

Juan Carlos Niebles, Ehsan Adeli, Kilian Pohl, Fei-Fei Li, Adolf Pfefferbaum, Edith Sullivan, Qingyu Zhao
Dec 03, 2020

Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years. Such challenges range from spurious associations between variables in medical studies to the bias of race in gender or face recognition systems. Controlling for all types of biases in the dataset curation stage is cumbersome and sometimes impossible. The alternative is to use the available data and build models incorporating fair representation learning. In this paper, we propose such a model based on adversarial training with two competing objectives to learn features that have (1) maximum discriminative power with respect to the task and (2) minimal statistical mean dependence with the protected (bias) variable(s). Our approach does so by incorporating a new adversarial loss function that encourages a vanished correlation between the bias and the learned features. We apply our method to synthetic data, medical images (containing task bias), and a dataset for gender classification (containing dataset bias). Our results show that the learned features by our method not only result in superior prediction performance but also are unbiased.

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Vision-based Estimation of MDS-UPDRS Gait Scoresfor Assessing Parkinson’s Disease Motor Severity
Juan Carlos Niebles, Ehsan Adeli, Fei-Fei Li, Kathleen Poston, Mandy Lu, Kilian M. Pohl, Adolf Pfefferbaum, Edith Sullivan
Nov 18, 2020
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Parkinson’s disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an F1-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments.

Vision-based Estimation of MDS-UPDRS Gait Scoresfor Assessing Parkinson’s Disease Motor Severity

Juan Carlos Niebles, Ehsan Adeli, Fei-Fei Li, Kathleen Poston, Mandy Lu, Kilian M. Pohl, Adolf Pfefferbaum, Edith Sullivan
Nov 18, 2020

Parkinson’s disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an F1-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments.

Healthcare
Research
Domain Shift and Emerging Questions in Facial Recognition Technology
Maneesh Agrawala, Daniel E. Ho, Fei-Fei Li, Emily Black
Nov 01, 2020
policy brief

Improving AI Software for Healthcare Diagnostics Facial recognition technologies have grown in sophistication and adoption throughout American society. Significant anxieties around the technology have emerged—including privacy concerns, worries about surveillance in both public and private settings, and the perpetuation of racial bias.

Domain Shift and Emerging Questions in Facial Recognition Technology

Maneesh Agrawala, Daniel E. Ho, Fei-Fei Li, Emily Black
Nov 01, 2020

Improving AI Software for Healthcare Diagnostics Facial recognition technologies have grown in sophistication and adoption throughout American society. Significant anxieties around the technology have emerged—including privacy concerns, worries about surveillance in both public and private settings, and the perpetuation of racial bias.

Healthcare
policy brief
National AI Research Resource: Ensuring the Continuation of American Innovation
John Etchemendy, Fei-Fei Li
Mar 28, 2020
announcement
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National AI Research Resource: Ensuring the Continuation of American Innovation

John Etchemendy, Fei-Fei Li
Mar 28, 2020
Industry, Innovation
Government, Public Administration
Regulation, Policy, Governance
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announcement
Ideas for the 2020s
Fei-Fei Li
Feb 03, 2019
news
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Ideas for the 2020s

Fei-Fei Li
Feb 03, 2019
Economy, Markets
Education, Skills
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news
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