Ph.D - Applied Math
UCLA - 2010

M.S. - Applied Math
UCLA - 2007

B.S. - Applied Math
Peking University - 2005

A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford--Shah Color and Multiphase Image Segmentation 2021 - Journal Article

A Novel Regularization Based on the Error Function for Sparse Recovery 2021 - Journal Article

Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization 2021 - Journal Article

Iteratively Reweighted Group Lasso Based on Log-Composite Regularization 2021 - Journal Article

Limited-Angle CT Reconstruction via the $L_1/L_2$ Minimization 2021 - Journal Article

Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors 2021 - Journal Article

An image sharpening operator combined with framelet for image deblurring 2020 - Journal Article

An Image Sharpening Operator Combined with Framelet for Image Deblurring 2020 - Journal Article

NSF CAREER - NSF [2019]

Associate Professor
University of Texas Dallas [2020–Present]

Assistant Professor
University of Texas Dallas [2014–2020]

Postdoc
University of California Irvine [2012–2014]

Postdoc
Georgia Institute of Technology [2011–2012]

Mathematician Focuses on Getting the Most from Small Data

A University of Texas at Dallas mathematician has received a five-year grant from the National Science Foundation (NSF) in support of her work on doing more with less data.
Dr. Yifei Lou, assistant professor of mathematical sciencesin the School of Natural Sciences and Mathematics, was awarded an NSF Faculty Early Career Development (CAREER) Award of more than $400,000 for her work on “Mathematical Modeling for Data to Insights and Beyond,” a project that seeks analytical tools to provide guidance on acquiring data more efficiently. In an age when so much focus is on big data, she calls her work “small data.”

“If one is able to collect only a certain amount of data, what method will allow him or her to get the most out of it?” Lou asked. “For example, if you can see only 25 percent of the pixels in an image, what way of picking one out of every four pixels would let us best identify the pictured object?”

CAREER: Mathematical Modeling from Data to Insights and Beyond

$400,211 - NSF [2019/06–2024/05]