Michael Jordan, PhD


Michael I. Jordan is the Pehong Chen Distinguished Professor 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.


Prof. Jordan was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive, biological and social sciences. He 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. Prof. Jordan is a Fellow of the American Association for the Advancement of Science. He was the inaugural winner of the World Laureates Association (WLA) Prize in 2022. 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 was a Plenary Lecturer at the International Congress of Mathematicians in 2018.

In 2016, Prof. Jordan was named the “most influential computer scientist” worldwide in an article in Science, based on rankings from the Semantic Scholar search engine.

Professional Summary

  • 1985, Ph.D., Cognitive Science, UC San Diego
  • 1980, M.S., Mathematics, Arizona State University
  • 1978, B.S., Psychology, Louisiana State University
  • M. Jordan, “Full publication list at www.cs.berkeley.edu/~jordan/publications.html,” None, July 2022.
  • B. Shi, S. Du, W. Su, and M. Jordan, “Understanding the acceleration phenomenon via high-resolution differential equations,” Mathematical Programming, vol. 5, pp. 634-648, April 2022.
  • M. Jagadeesan, A. Wei, M. Jordan, and J. Steinhardt, “Learning equilibria in matching markets from bandit feedback,” in Advances in Neural Information Processing Systems, 2021.
  • A. Adhikari, J. DeNero, and M. Jordan, “Interleaving computational and inferential thinking: Data science for undergraduates at Berkeley,” {Harvard Data Science Review, vol. 2, pp. 1-24, Sep. 2021.
  • A. El Alaoui, F. Krzakala, and M. Jordan, “Fundamental limits of detection in the spiked Wigner model,” Annals of Statistics, vol. 48, pp. 863-885, July 2021.

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