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Sparse signal learning and its applications in data science

Organizer(s):
Name:	Affiliation:	Country:
Xuemei Chen	University of North Carolina Wilmington	USA
Longxiu Huang	Michigan State University	USA
Jing Qin	University of Kentucky	USA

Abstract:

Many signal processing problems utilize sparsity or its low dimensional structure such as compressed sensing, matrix completion, and low rank tensor recovery, all of which have prominent applications such as image processing, social network, and machine learning in general. This special session aims to present recent developments in this area, whether theory or applications, utilizing tools in sampling theory, random matrix/tensor theory, optimization, numerical linear algebra, approximation theory, graph theory, etc.

List of approved abstract
Name	Affiliation	Title
Julia Dobrosotskaya	Case Western Reserve University	Data adaptive multiscale bases inducing joint compressibility
Christopher Dock	Tufts University	Instability of the Infinite Dimensional Operator Recovery Problem
Keaton Hamm	University of Texas at Arlington	Fast algorithms via Matrix Subsampling
Katherine Henneberger	University of Kentucky	Fast Hyperspectral Band Selection Based on Matrix CUR Decomposition
Santhosh Karnik	Michigan State University	Neural Network Approximation of Continuous Functions in High Dimensions with Applications to Inverse Problems
Shuang Li	University of California, Los Angeles	Digital Beamforming Robust to Time-Varying Carrier Frequency Offset
Chunyang Liao	Texas A&M University	Optimal Recovery from inaccurate observations
Jing Qin	University of Kentucky	Dual-Graph Regularized Foreground Background Separation