Fei-Fei Li (Chinese: 李飞飞; pinyin: Lǐ Fēifēi; born July 3, 1976) is a Chinese-born American computer scientist best known for creating ImageNet, the dataset that helped computers improve quickly in understanding visual information during the 2010s. She is a professor of computer science at Stanford University, with research experience in artificial intelligence, machine learning, deep learning, computer vision, and cognitive neuroscience.

Li is a co-director of the Stanford Institute for Human-Centered Artificial Intelligence and a co-director of the Stanford Vision and Learning Lab. She worked as Chief Scientist of AI/ML at Google Cloud and directed the Stanford Artificial Intelligence Laboratory from 2013 to 2018. In 2017, she co-founded AI4ALL, a nonprofit group that works to increase diversity in artificial intelligence. In 2023, Li was named one of the Time 100 AI Most Influential People.

Li received the Intel Lifetime Achievements Innovation Award in 2017 for her work in artificial intelligence. She became a member of the National Academy of Engineering in 2020, the National Academy of Medicine in 2020, and the American Academy of Arts and Sciences in 2021. In 2025, she was named one of the "Architects of AI" for Time’s Person of the Year.

On August 3, 2023, Li was appointed to the United Nations Scientific Advisory Board, created by Secretary-General Antonio Guterres. In 2024, she was listed on the Gold House’s most influential Asian A100 list. That same year, she helped raise $230 million for a startup called World Labs, which she and three colleagues started to develop a "spatial intelligence" AI technology that helps computers understand the three-dimensional physical world. In 2026, World Labs raised $1 billion.

Early life and education

Li was born in Beijing, China, in 1976 and grew up in Chengdu, Sichuan. She attended Sichuan Chengdu No. 7 High School. When she was 12 years old, her father moved to Parsippany, New Jersey. At age 16, Li and her mother joined him in the United States. While attending Parsippany High School, Li worked weekends at her family's dry-cleaning shop. She graduated from Parsippany High School in 1995. In 2017, she was honored in the Hall of Fame at Parsippany High School.

Li studied undergraduate courses at Princeton University, where she earned a Bachelor of Arts degree with a major in physics in 1999. Her senior thesis, titled "Auditory binaural correlogram difference: a new computational model for Huggins dichotic pitch," was completed under the guidance of Bradley Dickinson, a professor of electrical engineering. During her time at Princeton, Li returned home most weekends to help manage her family's dry-cleaning business and worked as a dishwasher to support her family's income.

Li continued her education at the California Institute of Technology, where she earned a Master of Science in electrical engineering in 2001 and a Doctor of Philosophy in electrical engineering in 2005. Her dissertation, "Visual Recognition: Computational Models and Human Psychophysics," was completed under the primary supervision of Pietro Perona and secondary supervision of Christof Koch. Her graduate studies were supported by the National Science Foundation Graduate Research Fellowship and The Paul & Daisy Soros Fellowships for New Americans.

Career and research

From 2005 to 2006, Li worked as an assistant professor in the Electrical and Computer Engineering Department at the University of Illinois Urbana-Champaign. From 2007 to 2009, she was an assistant professor in the Computer Science Department at Princeton University. In 2009, she joined Stanford University as an assistant professor. She became an associate professor with tenure in 2012 and a full professor in 2018. At Stanford, Li led the Stanford Artificial Intelligence Lab (SAIL) from 2013 to 2018. Her research focuses on computer vision, deep learning, and cognitive neuroscience. She has published over 300 peer-reviewed research papers. In 2018, she helped start the Human-Centered AI Institute at Stanford with Dr. John Etchemendy, a former provost at Stanford. The institute works to improve AI research, education, policy, and practice to help people.

While at Princeton in 2007, Li helped create ImageNet, a large visual database designed to improve object recognition in AI. The project used Amazon Mechanical Turk to label over 14 million images across 22,000 categories. ImageNet inspired the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which helped improve deep learning and image classification. ImageNet solved a major problem in computer vision: the lack of large, labeled datasets for training AI systems. Today, ImageNet is considered a key innovation that supports advancements in areas like self-driving cars, facial recognition, and medical imaging.

From January 2017 to fall 2018, Li took a leave of absence from Stanford to work at Google Cloud as its Chief Scientist of AI/ML and Vice President. At Google, her team worked to make AI technology easier to use for businesses and developers. This included creating tools like AutoML.

In 2017, Google signed a contract with the U.S. Department of Defense called Project Maven, which used AI to analyze images from drone cameras. Google told employees that their work on the project was limited to non-offensive purposes. In 2018, Google decided not to renew the contract. In leaked emails, Li expressed support for Google’s role in Project Maven but warned against discussing its AI aspects publicly, noting that military AI is often linked to concerns about autonomous weapons. Li told The New York Times that she believes AI should benefit people and not be used for harmful purposes.

In fall 2018, Li returned to Stanford to continue her professorship. In 2023, she helped launch the RAISE-Health initiative at Stanford, which focuses on using AI responsibly in healthcare. The initiative aims to improve clinical care, biomedical research, and patient safety.

Li has been on partial academic leave from January 2024 to the end of 2025 to work on entrepreneurial projects. In 2024, she noted that private companies invest more in AI than governments or universities and called for more public funding to study AI’s benefits and risks.

Li is also the co-founder and chairperson of AI4ALL, a nonprofit that teaches young people about AI and promotes diversity in the field. AI4ALL was created with help from Melinda French Gates and Jensen Huang. Before AI4ALL, Li and her former student Olga Russakovsky started a program called SAILORS at Stanford, which taught high school girls about AI. In 2017, the program became AI4ALL @Stanford. By 2018, AI4ALL had expanded to five other universities.

Li has been described as a "researcher bringing humanity to AI." She was elected to the National Academy of Engineering, the National Academy of Medicine, and the American Academy of Arts and Sciences. In 2025, she received the Queen Elizabeth Prize for Engineering for her work in deep learning.

Li’s research includes artificial intelligence, machine learning, computer vision, cognitive neuroscience, and computational neuroscience. Her work has been published in journals like Nature, the Proceedings of the National Academy of Sciences, and the Journal of Neuroscience. She is best known for creating ImageNet, which has transformed large-scale visual recognition.

In 2007, Li began developing ImageNet based on an idea from a psychologist who estimated that humans recognize about 30,000 object categories. Some colleagues doubted the project’s scale, but Li continued using Amazon Mechanical Turk to label over 14 million images. She helped organize the ImageNet Large-Scale Visual Recognition Challenge, which has become a major event in AI research.

Publications

• Ford, Martin (2018). "Fei-Fei Li". Architects of Intelligence: The Truth About AI from the People Building It. Birmingham, UK: Packt Publishing. pp. 145–162. ISBN 978-1-78913-126-0. OCLC 1083340727. An interview with Li conducted by Ford.
• Li, Fei Fei (2023). The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI. New York, NY: Moment of Lift Books, Flatiron Books. ISBN 978-1-250-89794-7. OCLC 1404458360.

• Li, Fei Fei; VanRullen, Rufin; Koch, Christof; Perona, Pietro (July 9, 2002). "Rapid natural scene categorization in the near absence of attention". Proceedings of the National Academy of Sciences. 99 (14): 9596–9601. Bibcode: 2002PNAS…99.9596L. doi: 10.1073/pnas.092277599. ISSN 0027-8424. PMC 123186. PMID 12077298.
• Li Fe-Fei; Fergus; Perona (2003). Proceedings Ninth IEEE International Conference on Computer Vision (PDF). IEEE. doi: 10.1109/iccv.2003.1238476.
• Li Fei-Fei; Fergus, R.; Perona, P. (2004). "Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories". 2004 Conference on Computer Vision and Pattern Recognition Workshop. IEEE. p. 178. doi: 10.1109/CVPR.2004.383.
• Fei-Fei Li; Perona, P. (2005). A Bayesian Hierarchical Model for Learning Natural Scene Categories (PDF). Vol. 2. IEEE. doi: 10.1109/CVPR.2005.16. ISBN 978-0-7695-2372-9.
• Li Fei-Fei; Fergus, R.; Perona, P. (2006). "One-shot learning of object categories". IEEE Transactions on Pattern Analysis and Machine Intelligence. 28 (4): 594–611. Bibcode: 2006ITPAM..28..594F. doi: 10.1109/TPAMI.2006.79. ISSN 0162-8828. PMID 16566508. Presented as slides.
• Fei-Fei, Li; Iyer, Asha; Koch, Christof; Perona, Pietro (January 31, 2007). "What do we perceive in a glance of a real-world scene?" Journal of Vision. 7 (1): 10. doi: 10.1167/7.1.10. ISSN 1534-7362. PMID 17461678.