Michael I. Jordan

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.