Google
University of Minnesota
University of Washington
Sanjay GhemawatCo-founder · Discovery LoopFounder↗
Researcher · Academic · Founder
Jeff Dean
Discovery LoopCo-founder and CEO
🇺🇸 AmericanBorn 1968
Dean helped make large-scale AI practical, combining neural-network research with the engineering needed to train and run models across many computers.[1] Widely regarded as one of the most influential programmers of his generation[1], he also co-created MapReduce and Bigtable with Sanjay Ghemawat, shaping the infrastructure behind Google’s growth.[1]
Career & education
Current roleDiscovery LoopAug 2026-presentCo-founder and CEO
Discovery LoopAug 2026-presentPreviously
Google DeepMindApr 2023-Aug 2026Chief Scientist
Google DeepMindApr 2023-Aug 2026Chief ScientistApr 2023-Aug 2026
Engineer and research leader1999-Apr 2023
Work and education history is primarily focused on AI-relevant roles and may not be comprehensive.
Citation Trend
Total citations to scholarly work, including research papers, technical reports, books and theses, as recorded by Google Scholar. Each point is a dated snapshot.
citations445,724As of Nov 1, '26
citations+8,306Since last snapshot, Oct 1, '26
Updates 2
MoveJeff Dean leaves Google after 27 years to build Discovery Loop
Google’s longtime chief scientist said August 6 would be his final day after a career spanning core infrastructure, Google Brain, and DeepMind.
August 6, 2026LaunchJeff Dean, Oriol Vinyals, Quoc Le, and Sanjay Ghemawat launch Discovery LoopFour longtime Google collaborators founded a public benefit corporation to automate machine learning, science, and engineering.
August 5, 2026Selected contributions
Efficient Estimation of Word Representations in Vector Space
Word2vec efficiently learns relationships between words from large text collections.
PaLM: Scaling Language Modeling with Pathways
With Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham and others ·Presented PaLM, a large language model demonstrating gains in reasoning, translation and coding.
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Expanded network capacity by routing each input to a small set of specialist subnetworks.
References
- University of Minnesota — large-scale computing and AI contributionscse.umn.edu
Dean helped make large-scale AI practical, combining neural-network research with the engineering needed to train and run models across many computers.
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Reviewed August 8, 2026 · Checked for accuracy and new information.
