Han Liu

Han Liu is an assistant professor within the Department of Operations Research and Financial Engineering at Princeton. 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. For more information, visit Princeton's Statistical Machine Learning Group page.

Title: "Combinatorial Inference"

Abstract: 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. 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).