Learning and Inference for Graphical and Hierarchical Models: A Personal Journey

Abstract – This talk will provide an overview of a personal perspective on inference and learning for graphical models, one that began with work on multi-resolution models for signals and images but that has evolved into a more general look at inference and learning especially for graphical models for which these tasks are tractable and scalable to large problems.

Biography – Prof. Willsky is Director of the Laboratory for Information and Decision Systems. His early work on methods for failure detection in dynamic systems is still widely cited and used in practice, and his more recent research on multiresolution methods for large-scale data fusion and assimilation has found application in fields including target tracking, object recognition, oil exploration, oceanographic remote sensing, and groundwater hydrology. His present research interests include estimation and imaging, inference algorithms, statistical image and signal processing, data fusion and estimation for complex systems, image reconstruction, discovery of models for complex interacting phenomena, and computer vision.

Dr. Willsky was a founder and Chief Scientific Consultant of Alphatech, Inc. He has authored more than 200 journal papers and 350 conference papers, as well as two books, including the widely used undergraduate text Signals and Systems. Prof. Willsky has received numerous awards, including the American Automatic Control Council Donald P. Eckman Award, the IEEE Browder J. Thompson Memorial Award, and an honorary doctorate from Université de Rennes. He received a Technical Achievement Award from the IEEE Signal Processing Society and is a member of the National Academy of Engineering.