朱冀教授
密歇根大学安娜堡分校
6月8日周五下午2:30，一报
Title: Network cross-validation by edge sampling
Abstract: Many models and methods are now available for network analysis, but model selection and tuning remain challenging. Cross-validation is a useful general tool for these tasks in many settings, but is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. Here we propose a new network cross-validation strategy based on splitting edges rather than nodes, which avoids losing information and is applicable to a wide range of network problems. We provide a theoretical justification for our method in a general setting, and in particular show that the method has good asymptotic properties under the stochastic block model Numerical results on simulated networks show that our approach performs well for a number of model selection and parameter tuning tasks. We also analyze a citation network of statisticians, with meaningful research communities emerging from the analysis. This is joint work with Tianxi Li and Elizaveta Levina.
Brief bio: Dr. Zhu received his B.Sc. in Physics from Peking University in China, and his Ph.D. in Statistics from Stanford University in 2003. He is now a Professor in the Department of Statistics at the University of Michigan. Dr. Zhu is recognized as a leading researcher in the areas of statistical machine learning and statistical network analysis. He is also interested in applications in science, health, engineering and business. Dr. Zhu has published more than 100+ research papers, including 90+ journal articles, 5 refereed conference articles and 7 discussions. He received a CAREER award from the National Science Foundation (USA) in 2008, and he was elected as a Fellow of the American Statistical Association in 2013 and a Fellow of the Institute of Mathematical Statistics in 2015; he also served as the Chair-Elect and Chair of the Statistical Learning and Data Mining Section of the American Statistical Association from 2011-2013.
朱教授博士和硕士毕业于美国斯坦福大学，本科毕业于北京大学物理系。现任美国密西根大学终身正教授。长期从事统计学、计算机科学及其与金融相关交叉学科的研究，其指导的很多博士毕业生活跃在美国一流大学（包括哈佛大学、威斯康辛大学、明尼苏达大学等）及国内外知名金融机构（包括美林美银、高盛、Jump Trading等）。在国际著名顶级刊物发表学术论文百余篇，领导并参与了多个美国国家级的重大科研项目。 2008年获得美国国家科学基金的CAREER奖，2008年获得美国国家科学基金的CAREER奖，2013 年当选美国统计学会会士，2015 年当选国际数理统计学会会士，连续四年（2014-2017）入选ThomsonReuters全球高被引用科学家（数学领域全球100 位）。现任Journal of the American Statistical Association（JASA）Assoicate Editor, 曾任 Biometrika 等多个国际一流杂志的 Assoicate Editor
