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Fei-Fei Li (Chinese: 李飞飞; pinyin: Lǐ Fēifēi; born July 3, 1976) is a Chinese-American computer scientist, known for establishing ImageNet, the dataset that enabled rapid advances in computer vision in the 2010s.
Dr. Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and Co-Director of Stanford’s Human-Centered AI Institute. She served as the Director of Stanford’s AI Lab from 2013 to 2018.
Dr. Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and Co-Director of Stanford’s Human-Centered AI Institute. She served as the Director of Stanford’s AI Lab from 2013 to 2018.
2016. Articles 1–20. Professor of Computer Science, Stanford University - Cited by 258,191 - Artificial Intelligence - Machine Learning - Computer Vision - Neuroscience.
View Fei-Fei Li’s profile on LinkedIn, a professional community of 1 billion members. Experience: Stanford University · Location: Stanford · 422 connections on LinkedIn.
- Stanford University
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.
Dr. Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and Co-Director of Stanford’s Human-Centered AI Institute. She served as the Director of Stanford’s AI Lab from 2013 to 2018.
Dec 15, 2023 · AI scientist Fei-Fei Li: ‘Maths is pretty clean. Humans are messy’. The China-born technologist on Silicon Valley’s ‘bro’ culture — and her mission to keep AI safe for humanity. © Ciaran...
Director of the Stanford Vision Lab. feifeili@cs.stanford.edu. Biography. I am an Associate Professor at the Computer Science Department at Stanford University. I received my Ph.D. degree from California Institute of Technology, and a B.S. in Physics from Princeton University.
AI pioneer Fei-Fei Li says a similar moment is about to happen for computers and robots. She shows how machines are gaining "spatial intelligence" — the ability to process visual data, make predictions and act upon those predictions — and shares how this could enable AI to interact with humans in the real world.