Dr. Yifei Lou from the University of Texas Dallas will present at the Seminar Monday March 26 at 1:15pm in Room 105 of Nedderman Hall (NH). Dr. Lou’s presentation title, abstract, and biographical sketch are below.
Title: Nonconvex Approaches in Data Science
Author: Yifei Lou
Location: Nedderman Hall (NH) Room 105
Date: Monday, March 26
Time: 1:15pm – 2:15pm
Abstract: Although “big data” is ubiquitous in data science, one often faces challenges of “small data,” as the amount of data that can be taken or transmitted is limited by technical or economic constraints. To retrieve useful information from the insufficient amount of data, additional assumptions on the signal of interest are required, e.g. sparsity (having only a few non-zero elements). Conventional methods favor incoherent systems, in which any two measurements are as little correlated as possible. In reality, however, many problems are coherent. I will present a nonconvex approach that works particularly well in the coherent regime. I will address computational aspects in the nonconvex optimization. Various numerical experiments have demonstrated advantages of the proposed method over the state-of-the-art. Applications, ranging from super-resolution to low-rank approximation, will be discussed.
Biographical sketch: Yifei Lou has been an Assistant Professor in the Mathematical Sciences Department, University of Texas Dallas, since 2014. She received her Ph.D. in Applied Math from the University of California Los Angeles (UCLA) in 2010. After graduation, she was a postdoctoral fellow at the School of Electrical and Computer Engineering Georgia Institute of Technology, followed by another postdoc training at the Department of Mathematics, University of California Irvine from 2012-2014. Her research interests include compressive sensing and its applications, image analysis (medical imaging, hyperspectral, imaging through turbulence), and (nonconvex) optimization algorithms.