Michael I. Jordan
Michael I. Jordan
Pehong Chen Distinguished Professor at the Department of EECS and Department of Statistics, University of California, Berkeley. Ph.D. University of California, San Diego.
Michael Irwin Jordan is an American scientist, professor at the University of California, Berkeley and researcher in machine learning, statistics, and artificial intelligence. He is one of the leading figures in machine learning, and in 2016 Science reported him as the world’s most influential computer scientist. Jordan received his Ph.D. in Cognitive Science in 1985 from the University of California, San Diego. At the University of California, San Diego, Jordan was a student of David Rumelhart and a member of the PDP Group in the 1980s. Jordan is currently a full professor at the University of California, Berkeley where his appointment is split across the Department of Statistics and the Department of EECS. He was a professor at the Department of Brain and Cognitive Sciences at MIT from 1988 to 1998. In the 1980s Jordan started developing recurrent neural networks as a cognitive model. In recent years, his work is less driven from a cognitive perspective and more from the background of traditional statistics. Jordan popularised Bayesian networks in the machine learning community and is known for pointing out links between machine learning and statistics. He was also prominent in the formalisation of variational methods for approximate inference and the popularisation of the expectation-maximization algorithm in machine learning. In 2001, Jordan and others resigned from the editorial board of the journal Machine Learning. In a public letter, they argued for less restrictive access and pledged support for a new open access journal, the Journal of Machine Learning Research, which was created by Leslie Kaelbling to support the evolution of the field of machine learning. Jordan has received numerous awards, including a best student paper award (with X. Nguyen and M. Wainwright) at the International Conference on Machine Learning (ICML 2004), a best paper award (with R. Jacobs) at the American Control Conference (ACC 1991), the ACM - AAAI Allen Newell Award, the IEEE Neural Networks Pioneer Award, and an NSF Presidential Young Investigator Award. In 2010 he was named a Fellow of the Association for Computing Machinery “for contributions to the theory and application of machine learning.” Jordan is a member of the National Academy of Science, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He also won the 2020 IEEE John von Neumann Medal.