DSI Distinguished Lecture: Han Liu, Princeton University

Hariri Institute for Computing
111 Cummington Mall, Seminar Room

Monday, January 30, 2017
3:00-4:00pm (reception to follow)

Title: Combinatorial Inference

Speaker: Han Liu
Assistant Professor
Dept. of Operations Research & Financial Engineering
Princeton University

Abstract:
We propose a new family of combinatorial inference problems for graphical models. Unlike classical statistical inference where the main interest is point estimation or parameter testing of Euclidean parameters, combinatorial inference aims at testing the global structure of the underlying graph. Examples include testing the graph connectivity, the presence of a cycle of certain size, or the maximum degree of the graph. To begin with, we develop a unified theory for the fundamental limits of a large family of combinatorial inference problems. We propose new structural packing entropies to characterize how the complexity of combinatorial graph structures impacts the corresponding minimax lower bounds. On the other hand, we propose a family of practical structural testing algorithms to match the obtained lower bounds. We use a case study of brain network analysis to illustrate the usefulness of these proposed methods.

Brief Bio:
Han Liu is an Assistant Professor in the Department of Operations Research and Financial Engineering at Princeton University, where he leads the Statistical Machine Learning (SMiLe) Laboratory. He received the joint PhD in Statistics and Machine Learning from the Machine Learning Department at the Carnegie Mellon University (under the co-supervision of Larry Wasserman and John Lafferty). He has broad research interests, ranging from modern data science to artificial general intelligence. Specifically, his theoretical research includes combinatorial inference, statistical optimization, and computational lower bounds. His applied research includes brain science, genomics, and computational finance. Han Liu is the recipient of many prestigious research awards including the Tweedie New Researcher Award (from the Institute of Mathematical Statistics), the Noether Young Scholar Award (from the American Statistical Association), the NSF CAREER Award (from the Division of Mathematical Sciences), and the Howard B Wentz Award (from Princeton SEAS), and has received numerous best paper awards including the Best Paper Prize in Continuous Optimization in the 5th ICCOPT and the Best Overall Paper Award honorable mention in the 26th ICML. He was also invited as a keynote speaker in the 2016 INFORMS Optimization Society Conference.

About the DSI Distinguished Lecture Series:
Launched as part of the Data Science Initiative at the Hariri Institute for Computing, the Distinguished Lecture Series brings prominent scholars to Boston University to share their experiences and perspectives on data science as it is manifested through or enabled by their research. Lectures in this series should be of interest to a broad audience as they are meant to promote the exciting advances in the methodologies that enable big-data-driven research in a multitude of disciplines.