Michael Jordan
Inria, University of California Berkeley, University of California, San Diego, Massachusetts Institute of Technology
0000-0001-8935-817X
About
Michael I. Jordan is a Senior Researcher at Inria Paris, and is the Pehong Chen Distinguished Professor Emeritus in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive, biological and social sciences. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering, a member of the American Academy of Arts and Sciences, and a Foreign Member of the Royal Society. He is a Fellow of the American Association for the Advancement of Science. He was awarded the BBVA Foundation Frontiers of Knowledge Award in Information and Communication Technologies in 2025. He was the inaugural winner of the World Laureates Association (WLA) Prize in 2022. He was a Plenary Lecturer at the International Congress of Mathematicians in 2018. He received the Ulf Grenander Prize from the American Mathematical Society in 2021, the IEEE John von Neumann Medal in 2020, the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015, and the ACM/AAAI Allen Newell Award in 2009. He gave the Inaugural IMS Grace Wahba Lecture in 2022, the IMS Neyman Lecture in 2011, and an IMS Medallion Lecture in 2004. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
Employment
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Inria Senior Researcher2024 - Present
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University of California Berkeley Professor1998 - Present
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Massachusetts Institute of Technology Professor1988 - 1998
Education
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University of California, San Diego PhD1980 - 1985
Projects & Funding
Projects & funding information is unavailable.
Publications (336)
- A Collectivist, Economic Vision for AI Save
- Breaking Feedback Loops in Recommender Systems with Causal Inference Save
- Mitigating Bias in Spatial Transcriptomic Pipelines via Human Feedback Save
- Deep generative modeling of sample-level heterogeneity in single-cell genomics Save
- Safety versus performance: How multi-objective learning reduces barriers to market entry Save
- On Finding Local Nash Equilibria (and only Local Nash Equilibria) in Zero-Sum Games Save
- Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback Save
- Relying on the Metrics of Evaluated Agents Save
- Functional protein mining with conformal guarantees Save
- Incentive-Aware Recommender Systems in Two-Sided Markets Save