Speaker
Wen Zhou 周文
New York University
Time
9:50 - 11:25
Dec. 23 - 27, 2024
Venue
Mon., Dec. 23, Shuangqing B627
Tues., Dec. 24, Shuangqing B627
Wed., Dec. 25, Shuangqing B627
Thur., Dec. 26, Shuangqing B627
Fri., Dec. 27, Shuangqing C654
Registration / 报名链接
https://www.wjx.top/vm/PAcCGSF.aspx#
Description
Over the last two decades, we have seen an unprecedented increase in the volume of available information across various fields. This explosion encompasses data that is ultra-high-dimensional, complexly structured or unstructured, dynamic, and even derived from heterogeneous sources. These data are being produced, collected, stored, and made increasingly accessible to a wide range of stakeholders, including industrial institutions, academic researchers, investors, and individuals. However, learning from this vast trove of information and making accurate predictions poses significant challenges for both algorithm-driven machine learning methods and traditional statistical approaches. Key among these challenges is learning from data heterogeneity and implementing effective dimension reduction techniques without compromising the integrity of the data. This short course aims to lay the groundwork for understanding cluster analysis—an essential unsupervised statistical learning technique for uncovering data heterogeneity. It also covers Principal Component Analysis (PCA) and its variants, which are among the most prevalent tools for dimension reduction.
Prerequisite: Basic machine learning, linear regression, probability
Reference: Using lecture notes
Target Audience: Graduate students
Teaching Language: English
About the Speaker
Wen Zhou is an Associate Professor in the Department of Biostatistics at the School of Global Public Health. He received his Ph.D.s in Statistics and Applied Mathematics from the Iowa State University. His research focuses on developing theories and methods for network data analysis, high-dimensional statistics, multiple testing problems, machine learning, and causal inference. He is particularly interested in applications within genomics, genetics, bioinformatics, protein structure modeling, social science, epidemiology, and health policy. Wen serves on the editorial boards of the Statistica Sinica, Journal of Multivariate Analysis, Biometrics, as well as serves as the Editor-in-Chief of Journal of Biopharmaceutical Statistics. He is an elected member of the International Statistical Institute and has been elected as the WNAR program coordinator in 2024.
