报告人：Yang Wang(Hong Kong University of Science and Technology)
报告题目：Data Recovery on Manifolds: A Theoretical Framework
报告摘要：Recovering data from compressed number of measurements is ubiquitous in applications today. Among the best know examples are compressed sensing and low rank matrix recovery. To some extend phase retrieval is another example. The general setup is that we would like to recover a data point lying on some manifold having a much lower dimension than the ambient dimension, and we are given a set of linear measurements. The number of measurements is typically much smaller than the ambient dimension. So the questions become: Under what conditions can we recover the data point from these linear measurements? If so, how? The problem has links to classic algebraic geometry as well as some classical problems on the embedding of projective spaces into Euclidean spaces and nonsingular bilinear forms. In this talk I'll give a brief overview and discuss some of the recent progresses.
报告人简介：Professor Yang Wang is Dean of Science and chair professor of mathematics in the Hong Kong University of Science and Technology. An internationally respected mathematician, his research spans both pure and applied mathematics, including applied harmonic analysis, signal processing, fractal geometry, tiling and the application of machine learning to various practical applications. He is on the editorial board of some of the top journals in his fields, such as Applied and Computational Harmonic Analysis, Advances in Computational Math etc.
Professor Wang received his BS in mathematics from the University of Science and Technology of China in 1983 and his PhD in mathematics from Harvard University in 1990. He was a professor at Georgia Tech until 2006, when he became department head in Michigan State University. He has also served as a program director in the US National Science Foundation, and an active member of the mathematical community in promoting outreach and international exchange.
报告时间：2017年12月13日（星期三）下午15：30-16:30
报告地点：科技楼南602
