Prof. Michael I. Jordan has been a world-leading researcher in the field of statistical machine learning for nearly four decades. His contributions at the interface between computer science and statistics include the variational approach to statistical inference and learning, inference methods based on graphical models and Bayesian non-parametrics, and characterizations of trade offs between statistical risk and computational complexity.

He has also worked at the interface between optimization and machine learning, where he is well known for his development of continuous-time models of gradient-based optimization and sampling, and his work on distributed systems for optimization. He has built bridges between machine learning and control theory, contributing to the theory of reinforcement learning, learning-based model predictive control, and optimality principles for human motor control. 

He has also led the way in bringing microeconomic concepts into contact with machine learning, developing learning methods that incentivize learners to share data, showing how contract theory can be employed for statistical inference, and contributing to the study of learning-based matching markets. He has also pursued numerous high-impact applications of machine learning in domains such as single-molecule imaging, protein modeling, genetic admixture modeling, and natural language processing.

Prof. Jordan's contributions to computer science are also evident in education. He has mentored over 80 PhD students and over 60 postdoctoral researchers, an influential cohort who are now professors at the world's leading academic institutions and leaders in industry.

1978, B.S. in Psychology, Louisiana State University

1980, M.S. in Mathematics (Statistics), Arizona State University

1985, PhD in Cognitive Science, University of California, San Diego

1986-1988, Postdoctoral researcher, Department of Computer and Information Science, University of Massachusetts

1988-1998, Assistant professor, Associate professor, Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology

1998-present, Professor, Department of Electrical Engineering and Computer Sciences, Department of Statistics, University of California, Berkeley

2015-2017, Chair, Department of Statistics, University of California, Berkeley

2017-present, Professor, Department of Industrial Engineering and Operations Research, University of California

2018-present, Honorary Professor, Peking University

2019-present, Honorary Professor, Tsinghua University

2004, Medallion Lecturer, Institute of Mathematical Statistics (IMS)

2009, ACM/AAAI Allen Newell Award (Association for Computing Machinery, ACM; Association for the Advancement of Artificial Intelligence, AAAI )

2010, Member, National Academy of Sciences

2010, Member, National Academy of Engineering

2011, Member, American Academy of Arts and Sciences

2011, Neyman Lecture, Institute of Mathematical Statistics

2015, David E. Rumelhart Prize (Cognitive Science Society, CSS)

2016, IJCAI Award for Research Excellence (International Joint Conference on Artificial Intelligence)

2020, John von Neumann Medal (Institute of Electrical and Electronics Engineers, IEEE)

2021, Mitchell Prize (International Society for Bayesian Analysis, ISBA)

2021, Ulf Grenander Prize in Stochastic Theory and Modeling (American Mathematical Society, AMS)

2022, Inaugural Grace Wahba Lecturer, Institute of Mathematical Statistics (IMS)