For contributions to the theory and application of machine learning.
For fundamental advances in machine learning, particularly his groundbreaking work on graphical models and nonparametric Bayesian statistics, the broad application of this work across computer science, statistics, and the biological sciences. Michael I. Jordan has played a seminal role in the development of statistical machine learning. His work, and the work of his former students and postdocs, has served to define this area, which bridges computer science and statistics. Michael played a leading role in the development of the variational approach to inference in graphical models, identifying techniques in statistical physics that could provide efficient approximation algorithms for large-scale Bayesian networks. Michael and his colleagues developed the Latent Dirichlet Allocation model for extracting topics from documents, which has proven very influential not only in text processing but in many other domains. He and his colleagues have also developed several important families of probability models, including the widely used hierarchical mixtures of experts, and the widely applicable hierarchical Dirichlet processes. These models have extraordinarily broad applications from robotics and computer vision, to text and natural language processing, to the detection of bugs in software, to statistical genomics and proteomics. Michael's work is not only highly influential in computer science, with broad applicability within and beyond the field, but also plays a major role in the increasingly computational nature of research in statistics. Michael has also served as a leader in the machine learning community, organizing conferences and workshops, including serving as general chair of the NIPS conference. He is known for working broadly with neuroscientists, statisticians, and biologists, not only in joint research but as an educator and ambassador. Michael has trained an incredible cohort of students and postdocs, distinguished both in size and quality, who now hold faculty positions in over a dozen of the world's top computer science departments and positions in many leading companies.
Department of Electrical Engineering and Computer Science and the
Department of Statistics at the University of California, Berkeley.
He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive and biological sciences. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science.He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He was a Plenary Lecturer at the International Congress of Mathematicians in 2018. He received 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 is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
In 2016, Professor Jordan was named the "most influential computer scientist"worldwide in an article in Science, based on rankings from the Semantic Scholar search engine.