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20171228 Kun Chen:Integrate, divide, and conquer: on sparse and low-rank multivariate statistical learning
时间:2017-12-21

题目:Integrate, divide, and conquer: on sparse and low-rank multivariate statistical learning

主讲:Kun Chen

时间:2017年12月28   18:00-19:00

地点:明德主楼    1030会议室

 

摘要: Large-scale multivariate/multi-view data, or the measuring of distinct yet interrelated sets of characteristics pertaining to a single set of subjects, has become increasingly common. An integrative learning of multiple data attributes, model structures or objectives often enables us to gain extraordinary insight of complicated data generation mechanisms through utilizing information from various lenses and angles. In this talk, we present several new integrative sparse and low-rank models, crafted for conducting robust dimension reduction and outlier detection, predictive modeling with multi-view features, data fusion with incomplete and mixed-type outcomes, among others. In particular, we tackle the challenging problem of recovering a co-sparse and low-rank structure in large-scale multivariate regression, in which the outcomes are related to the features through a few latent pathways and each pathway may only involve a subset of outcomes and features. We propose divide-and-conquer strategies to isolate the recovery of each latent association pathway, for which the task simplifies greatly to a co-sparse unit-rank regression problem, permitting consistent estimation and scalable computation. The proposed methods enjoy both computational and statistical performance guarantees, and their efficacy is demonstrated by several applications in genetics, finance and public health.

 

简介:Dr. Kun Chen is an Assistant Professor in the Department of Statistics, University of Connecticut (UConn), and a Research Fellow at the Center for Public Health and Health Policy, UConn Health Center. Chen’s research mainly focuses on integrative multivariate learning, high-dimensional statistics, and healthcare analytics with large-scale heterogeneous data. He has extensive interdisciplinary research experience in a variety of fields, including insurance, ecology, biology, medical imaging, and public health. Chen’s research projects have received funding from the National Institutes of Health (NIH), the Simons Foundation, and the National Science Foundation (NSF). Currently he is funded by NSF for developing an integrative multivariate learning framework and for developing dimension reduction and prediction methods for heterogeneous responses; he is a co-PI in an NIH-funded data-driven suicide prevention study, which aims to improve suicide risk identification by leveraging integrated big data from disparate sources in healthcare system. Since 2017, Chen serves as Secretary of the newly established New England Statistical Society since 2017, and serves as an Associate Editor of Sankhya: The Indian Journal of Statistics since 2016. He has received Recognition for Teaching Excellence at UConn for multiple times since 2013.

Chen received his B.Econ. in Finance and Dual B.S. in Computer Science & Technology from the University of Science and Technology of China in 2003, his M.S. in Statistics from the University of Alaska Fairbanks in 2007, and his PhD in Statistics from the University of Iowa in 2011. Before joining UConn, Chen was on the faculty of the Department of Statistics at Kansas State University from 2011 to 2013.