Tensymp 2020


Wing-Kin (Ken) Man, PhD

Professor, Department of Electronic Engineering, The Chinese University of Hong Kong, HK
IEEE SPS Regional Director-at-Large of Region 10
Fellow of IEEE

Short Bio:

Wing-Kin (Ken) Ma is a Professor with the Department of Electronic Engineering, The Chinese University of Hong Kong. His research interests lie in signal processing, optimization and communications. His most recent research focuses on two distinct topics, namely, structured matrix factorization for data science and remote sensing, and MIMO transceiver design and optimization. Dr. Ma is active in the Signal Processing Society. He served as editors of several journals, e.g., Senior Area Editor of IEEE Transactions on Signal Processing, and Lead Guest Editor of a special issue in IEEE Signal Processing Magazine. He is currently a member of the Signal Processing for Communications and Networking (SPCOM) Technical Committee. He received Research Excellence Award 2013–2014 by CUHK, the 2015 IEEE Signal Processing Magazine Best Paper Award, the 2016 IEEE Signal Processing Letters Best Paper Award, and the 2018 IEEE Signal Processing Best Paper Award. He is an IEEE Fellow and was an 2018-19 IEEE SPS Distinguished Lecturer. He is currently the IEEE SPS Regional Director-at-Large of Region 10.


Hyperspectral Unmixing: Insights and Beyond


Hyperspectral unmixing (HU) is a key topic in hyperspectral remote sensing. The problem is to leverage on the high spectral resolution of hyperspectral images to identify the materials and their corresponding compositions in the scene. Early HU research is based on smart intuitions from remote sensing, and recent involvements from other fields—such as signal processing, optimization and machine learning—have enriched the HU techniques substantially. In this talk we will use the signal processing lens to reveal the fundamental insights of HU, namely, those arising from convex geometry. We will see how such insights establish a unique branch of provably powerful simplex-structured matrix factorization techniques. We will also examine the connections between HU and a number of problems in blind source separation, machine learning, data science, computer vision and biomedical imaging.