“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王亚珍，预祝第九届中国人民大学国际统计论坛取得圆满成功！

王亚珍

Title

Quantum Machine Learning

Abstract

Quantum computation and quantum information have attracted great attention on multiple frontiers of scientific fields ranging from physics to chemistry and engineering, as well as from computer science to mathematics and statistics. As randomness and uncertainty are deeply rooted in quantum computing, statistics can play an important role in quantum computation, which in turn offers great potential to revolutionize computational statistics and data science. This talk will give a brief review on quantum computing and machine learning and then present a statistical learning problem and its quantum solution to illustrate the quantum advantage in statistics.  

Biography

Dr. Yazhen Wang is Professor of Statistics at the University of Wisconsin-Madison and has served as the Statistics Department Chair during 2015-2018 and 2021-present. He obtained his Ph.D. in Statistics from the University of California at Berkeley in 1992. He is the fellows of ASA and IMS. He has served as NSF program director during 2007-2009, various committees of ASA, IMS and ICSA; co-editors of Statistica Sinica and Statistics and Its Interface; associate editors of Annals of Statistics, Annals of Applied Statistics, Journal of the American Statistical Association, Journal of Business & Economic Statistics, Statistica Sinica, and the Econometrics Journal. His research areas include financial econometrics, machine learning, quantum computation, high dimensional statistical inference, nonparametric curve estimation, wavelets and multiscale methods, change points, long-memory processes, and order restricted inference.