“中国人民大学国际统计论坛”创办于2004年，致力于搭建统计学界高层次的学术交流平台，已成为中国最有影响力的统计学论坛之一。

2023年7月14日至15日，本届论坛将邀请5位主题报告人和6位特邀报告人，本次介绍特邀报告人袁明，预祝第九届中国人民大学国际统计论坛取得圆满成功！

袁明

Title

Spectral Learning for High Dimensional Tensors

Abstract

Matrix perturbation bounds developed by Weyl, Davis, Kahan and Wedin and others play a central role in many statistical and machine learning problems. I shall discuss some of the recent progresses in developing similar bounds for higher order tensors. I will highlight the intriguing differences from matrices, and explore their implications in spectral learning problems.

Biography

Ming Yuan is a Professor of Statistics and an Associate Director of the Data Science Institute at Columbia University. He was previously a Senior Investigator in Virology at Morgridge Institute for Research and a Professor of Statistics at University of Wisconsin at Madison, and prior to that Coca-Cola Junior Professor of Industrial and Systems Engineering at Georgia Institute of Technology. His research and teaching interests lie broadly in statistics and its interface with other quantitative and computational fields such as optimization, machine learning, computational biology, and financial engineering. He has served as the program secretary of the Institute for Mathematical Statistics (IMS), and a member of the advisory board for the Quality, Statistics and Reliability section of the Institute for Operations Research and the Management Sciences (INFORMS). He was also a co-Editor of The Annals of Statistics and has served on numerous editorial boards. He was named a Senior Fellow of the Institute for Theoretical Studies at ETH Zurich (2019), a Medallion Lecturer of IMS (2018), and a recipient of the Leo Breiman Junior Researcher Award (2017; American Statistical Association), the Guy Medal in Bronze (2014; Royal Statistical Society), and CAREER Award (2009; US National Science Foundation).