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SILO Seminar Series: Han Liu

Event Details

When: September 7, 2016, 12:30 PM

Location: 3rd Floor Orchard View Room , Discovery Building

Contact: 608-316-4401, hstampfli@wisc.edu

Han Liu

Han Liu
Asst. Professor
Princeton University

Combinatorial Inference

Video: https://vimeo.com/183315365

We propose the combinatorial inference to explore the global topological structures of graphical models. In particular, we conduct hypothesis tests on many combinatorial graph properties including connectivity, hub detection, perfect matching, etc. Our methods can be applied to any graph property which is invariant under the deletion of edges. On the other side, we also develop a generic minimax lower bound which shows the optimality of the proposed method for a large family of graph properties. If time permits, I will also discuss the computational lower bounds of the combinatorial inference problem under the oracle computational model. Our methods are applied to the neuroscience by discovering hub voxels contributing to visual memories (Joint work with Junwei Lu, Matey Neykov, Kean Ming Tan, and Zhaoran Wang).

Bio: Han Liu received a Joint PhD in Machine Learning and Statistics in 2011 at Carnegie Mellon University. His thesis advisors are John Lafferty and Larry Wasserman. As a computer scientist and statistician, he exploits computation and data as a lens to explore science and machine intelligence. He is serving as an Associate Editor of the Electronic Journal of Statistics and as area chairs for NIPS, AISTATS, and ICML


The weekly SILO seminar series is made possible through the generous support of the 3M Company and its Advanced Technology Group
SILO is a lecture series with speakers from the UW faculty, graduate students or invited researchers that discuss mathematical related topics. The seminars are organized by WID’s Optimization research group.
SILO’s purpose is to provide a forum that helps connect and recruit mathematically-minded graduate students. SILO is a lunch-and-listen format, where speakers present interesting math topics while the audience eats lunch.