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🌍 Global AI•Researcher / Scientist

Andrej Karpathy

Neural networks educator and builder.

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Academic & Research Impact

Affiliation: Yale University (US)

OpenAlex Profile
h-index

19

Hirsch metric
Citations

56,091

Total cited
Publications

25

Indexed works
i10-index

19

Works ≥ 10 cites

Landmark Publications & High-Impact Papers

ImageNet Large Scale Visual Recognition Challenge

2015•DOI ↗
38,825 citations

Large-Scale Video Classification with Convolutional Neural Networks

2014•DOI ↗
6,225 citations

Deep visual-semantic alignments for generating image descriptions

2015•DOI ↗
4,749 citations

Profile

Updated: Oct 13, 2026 · wikipedia
Andrej Karpathy (born 23 October 1986[3]) is a Slovak-Canadian AI researcher,[4] who co-founded and formerly worked at OpenAI,[5][6][7] where he specialized in deep learning and computer vision.[8][9][2] He also worked as the director of artificial intelligence and Autopilot Vision at Tesla, and in 2024 he founded Eureka Labs, an AI education platform.[10] In 2026 he joined Anthropic as part of the pretraining team.[11] Karpathy was born in Bratislava, Czechoslovakia (now Slovakia),[12][13][14][15] and moved with his family to Toronto when he was 15.[4] He completed his Computer Science and Physics bachelor's degrees at the University of Toronto in 2009[16] and his master's degree at the University of British Columbia in 2011,[16] where he worked on physically simulated figures. In 2006, Karpathy began posting videos on YouTube on his channel, badmephisto. He garnered fame by posting Rubik's cube tutorials which have been used by famous speedcubers such as Feliks Zemdegs.[17] The channel has over 9 million views as of June 2025. Karpathy received a PhD from Stanford University in 2015 under the supervision of Fei-Fei Li, focusing on the intersection of natural language processing and computer vision, and deep learning models suited for this task.[18][19][20] He authored and was the primary instructor of the first deep learning course at Stanford, CS 231n: Convolutional Neural Networks for Visual Recognition.[21] The course became one of the largest classes at Stanford, growing from 150 students in 2015 to 750 in 2017.[22]
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