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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.
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...
Jul 17, 2024 · Stanford University’s artificial intelligence leader Fei-Fei Li has quietly built a billion-dollar start-up in just four months, joining the fierce race across the tech industry to commercialise...
View Fei-Fei Li’s profile on LinkedIn, a professional community of 1 billion members. Experience: World Labs · Location: Stanford · 458 connections on LinkedIn.
Sep 13, 2024 · Fei-Fei Li, a leading artificial intelligence researcher, has raised $230 million for a startup she and three colleagues founded to make AI technology that can understand how the three-dimensional ...
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.
Nov 10, 2023 · From medicine to science to the Hollywood strikes. Today, with computer scientist and AI pioneer Fei-Fei Li, we dig deeper into the history of the field, how machines really learn and how...