应用数学| Multiscale Data Analysis: Framelets, Manifolds and Graphs

题目：Multiscale Data Analysis: Framelets, Manifolds and Graphs
时间：12月15日（周日）下午14:30
地点：欧阳楼316
报告人：Xiaosheng Zhuang副教授，香港城市大学

摘要：While Big Data are high-volume, high-dimensional, and high complexity, they are typically concentrated on low-dimensional manifolds or can be represented by graphs, digraphs, etc. Sparsity is the key to the successful analysis of data in various forms. Multiscale representation systems provide efficient and sparse representation of various data sets. In this talk, we will discuss the characterizations, construction, and applications of framelets on manifolds and graphs. We shall demonstrate that tight framelets can be constructed on compact Riemannian manifolds or graphs, and fast algorithmic realizations exist for framelet transforms on manifolds and graphs. Explicit construction of tight framelets on the sphere and graphs as well as numerical examples will be shown.

个人简历：庄晓生分别于2003与2005年在中山大学数学系获得学士及硕士学位.于2010年在加拿大阿尔伯塔大学数学与统计科学系获得博士学位. 2012年在香港城市大学数学系任助理教授, 2018年升任副教授。

联系人：莫群副教授（moqun@zju.edu.cn）