Michael I. Jordan

University of California, Berkeley

Election Year: 2010
Primary Section: 32, Applied Mathematical Sciences
Secondary Section: 34, Computer and Information Sciences
Membership Type: Member

Research Interests

I have a longstanding interest in aspects of statistical inference  and decision-making that raise computational and systems problems. One focus of my research has been the area of probabilistic graphical  models, which blends probability theory and graph theory to provide statistical models for interdependent collections of variables and accompanying graph-theoretic algorithms for inference.  I have  developed new graphical model architectures that have had impact in  various applied fields, ncluding bioinformatics, computational vision,  speech, natural language processing and information retrieval, and I have contributed to the development of a novel framework for inference  in graphical models based on variational representations of probability  distributions.  Another area of focus has been nonparametric inference,  including both Bayesian nonparametrics, where I have developed new models  based on the area of stochastic processes known as completely random  measures, and frequentist nonparametrics, where I have focused on kernel  machines, spectral methods, dimension reduction and classification.  I have also been interested in the psychophysics of human learning, specifically as manifested in human motor control.

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