报告人：Jian Ding
报告地点：惟真楼523
报告时间：2025年3月21日星期五16:00-17:00
邀请人：郑术蓉
报告摘要：
Abstract: I will present some recent results on detecting correlation in stochastic block models, with emphasis on efficient algorithms and complexity lower bound. Along the way, I will compare results with detection for Erdos-Renyi models. Based on joint work with Guanyi Chen, Shuyang Gong and Zhangsong Li.
主讲人简介：
Professor Jian Ding received his B.S. from Peking University in 2006 and his Ph.D. from The University of California, Berkeley, in 2011. He has been a postdoc at Stanford and a faculty member at the University of Chicago and the University of Pennsylvania. Jian Ding is currently a chair professor at Peking University. Ding works on probability theory, emphasizing its interactions with statistical physics, theoretical computer science, and statistical learning theory. He is also interested in probability questions arising from "application-oriented" problems. With various coauthors, he has contributed to topics including random constraint satisfaction problems, random planar geometry, random field Ising models, random walks in random environments, and random Schrödinger operators. Ding has received a few recognitions, including the ICM invited lecture (2022), the Xplorer Prize (2023), the Rollo Davidson prize (2017), the ICCM gold medal (2022), and the Loève Prize (2023).
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