William Gould Dow Distinguished Lecture Series|
Learning and Inference for Graphical and Hierarchical Models: A Personal Journey
Alan S. Willsky
Edwin Sibley Webster Professor of EECS
Massachusetts Institute of Technology
Tuesday, January 15, 2013|
4:30pm - 5:30pm
1670 Beyster Building
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|Reception Following the Lecture|
About the Event
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.
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.
Contact: Stacie Printon
Sponsor(s): Electrical and Computer Engineering
Open to: Public