With the advance in sensors, data become ubiquitous. To make sense of the data we have to solve higher and higher dimensional problems that would seem intractable to solve. However, many high-dimensional problems have solutions that live in low-dimensional spaces. Sparsity is a way to exploit the low-dimensional structure of solutions to obtain feasible solution methods for high-dimensional problems. Here in this talk, I will introduce regularization methods that enforce sparsity in solutions and their applications to several image reconstruction problems including single-molecule localization microscopy and ground-based astronomy.

Biography:

Raymond Chan is a Chair Professor in the Department of Mathematics at City University of Hong Kong. He joined CityU in 2019 as the founding Dean of the College of Science and is now the Vice-President of Student Affairs. He obtained his BSc degree from The Chinese University of Hong Kong and his MSc and Ph.D. degrees from New York University. Chan is the winner of the Feng Kang Prize and Morningside Award and is a SIAM Fellow and an AMS Fellow. He was a SIAM Council member and now serves on its Board of Trustees, and he is currently the Vice-President of the International Consortium of Chinese Mathematicians (ICCM).