Multi-layer Networks: Sparsity, Heterogeneity and Dependency

发布时间：2024-05-16 作者：浏览次数：

Speaker:	王军辉	DateTime:	2024年5月18日（周六）上午9：30 - 10：30
Brief Introduction to Speaker: 王军辉教授现为香港中文大学统计系教授。他本科毕业于北京大学，研究生毕业于美国明尼苏达大学并获得统计学博士学位。他的研究方向包括统计机器学习及其在生物医学，经济，金融，和信息技术上的应用。他的研究成果广泛发表于JASA, Biometrika, JMLR和NeurIPS等统计及机器学习的顶级期刊和会议，并担任JASA，AoAS, Statistica Sinica等主流期刊的副主编。		Place:	6号楼二楼报告厅
Abstract: Network data has attracted increasing research interests across various scientific communities. In this talk, I will talk about some of our recent projects on multi-layer networks, including change point detection and interlayer dependency. Particularly, I will introduce a new subspace tracking method to detect network subspace changes so as to assure homogeneous network layers between adjacent change points, and also a new stochastic block Ising model (SBIM) to accommodate intet-layer dependency among the neighboring homogeneous network layers. The developed methods are supported by their asymptotic properties as well as a variety of real applications. If time permits, I will also briefly discuss about the compromise between privacy protection and estimation accuracy in multi-layer networks.