Michael I. Jordan

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berkeley college

Michael Irwin Jordan is an american scientist, professor in machine learning, statistical science and artificial intelligence at the University of California, and researcher in Berkeley. He is one of the leading figures in machine learning, and Science has reported him as the most important computer scientist in the world in 2016.

In 1978, Jordan received his BS magna cum laude degree in Psychology from Louisiana State University, his MS degree in Mathematics from Arizona State University in 1980 and his PhD in cognitive science from the University of California in San Diego in 1985. Jordan was a student of David Rumelhart and a member of the PDP Group in the 1980s at the University of California, San Diego.

Jordan currently is a full professor, working in the Department of Statistics and the Department of EECS at the University of California, Berkeley. From 1988 to 1998 he was professor in the Brain and Cognitive Sciences Department at MIT.

Jordan began to develop recurrent neural networks as a cognitive model in the 1980s. In recent years, his work has been less driven by a cognitive point of view and more by traditional statistics.

In the machine-learning community, Jordan popularized Bayesian networks and is known for pointing out links between machine learning and statistics. He was also prominent in formalizing variation methods for approximate inference and popularizing the machine learning expectative maximization algorithm.

In 2001, Jordan and others resigned from the Machine Learning editorial board. They advocated less restrictive access in a public letter and committed support to a new open access newspaper, The Journal of Machine Learning Research, created by Leslie Kaelbling to support the development of machine learning.

Jordan has earned numerous awards, including the ACM - AAAI Allen Newell Award, the IEEE Pioneer Award for Neural Networks, and the NSF Young Investigator Award. This is a prize for the best paper award at the International Conference on Machine Learn. In 2010 he was appointed a Fellow for “contributions to the theory and application of machine training” in the Association for Machinery for Computing Machinery. Jordan belongs to the National Academy of Science, to the National Academy of Engineering and to the Academy of Arts and Sciences in the US.

He was named a Neyman lecturer and an Institute of Mathematical Statistics medallion lecturer. In 2015 he was awarded the David E. Rumelhart Prize and in 2009 received the ACM/AAAI Allen Newell Award.

In 2016 Jordan was identified by an analysis of published literature by the Semantic Scholar Project as the “most influential computer scientist.”

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