Michael I. Jordan is Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California at Berkeley. His research interests focus on topics that bridge artificial intelligence and mathematical statistics, particularly the areas of machine learning and reasoning under uncertainty. He has worked on a number of topics on this interface, including neural networks, mixture models, decision trees, hidden Markov models, Boltzmann machines and Bayesian networks.
Graphical models provide a toolbox that allows for the construction of complex probabilistic models.
They come equipped with a coherent probabilistic semantics, a natural user interface and a set of computational procedures for inference and learning. It is not uncommon to encounter reasonable models for which a single probabilistic inference step would take years to run. In particular, laws of large numbers come to the rescue, and many sums do not need to be performed explicitly because their result is predictable. The goal of our work is to design algorithms that exploit this idea in the general graphical models setting. We have developed a number of such algorithms, using ideas from statistical physics, convex analysis and large deviation theory.