Xuemei Chen

Xuemei Chen is an Associate Professor in the Department of Mathematics and Statistics at the University of North Carolina Wilmington (UNCW), where she has been a faculty member since 2020, promoted to Associate Professor in 2023. She previously served as an Assistant Professor at New Mexico State University (2018–2020) and the University of San Francisco (2016–2018), following postdoctoral positions at the University of Missouri Columbia and the University of Maryland College Park.

• Ph.D. in Mathematics, Vanderbilt University
• Research focus: Applied harmonic analysis, compressed sensing, frame theory, and high-dimensional data solvers
• Active Principal Investigator on NSF grants DMS-2307827 and DMS-1908880
• Mentors undergraduate and graduate students in signal and image processing projects

Her research lies at the intersection of mathematical signal processing and data science, with a strong emphasis on theoretical foundations of sparse representations, frame design, and iterative algorithms like the Kaczmarz method. She investigates atomic norm minimization, matrix separation, and structured signal recovery, contributing to both theoretical understanding and practical applications in image inpainting and tensor recovery. Her work frequently appears in top journals such as SIAM Journal on Imaging Sciences and Applied and Computational Harmonic Analysis.

The 15 most recent articles reflect a consistent trajectory in applied harmonic analysis and compressed sensing, with increasing focus on structured recovery, frame optimization, and algorithmic convergence. Keywords span Mathematics, Signal Processing, Numerical Analysis, Optimization, Image Processing, and Data Science, with subfields including Frame Theory, Sparse Recovery, Kaczmarz Algorithms, Atomic Norm Minimization, Low Coherence Frames, and Tensor Recovery. The trend shows deepening theoretical analysis paired with algorithmic innovation for high-dimensional problems.

• NSF DMS-2307827: Recovering structured signals: atoms, matrix separation, and applications (PI, 2023–2026)
• NSF DMS-1908880: Recovering Signals Sparse in a Frame: Theory and Applications (PI, 2019–2023)

Xuemei Chen actively advises students, including undergraduates engaged in research on sparse signal recovery and image processing. She supports students through research assistantships and hourly positions, emphasizing strong analytical and coding skills. Her teaching portfolio includes courses in statistics, regression, numerical analysis, linear algebra, and data science, reflecting her interdisciplinary expertise. She maintains open educational resources on GitHub and encourages student engagement through research and professional development opportunities.

She leads a research group focused on mathematical data science, with projects suitable for master’s and advanced undergraduate students. Her GitHub repositories, such as Undergraduates and Image-Processing, support student training and open collaboration. She fosters a research environment that bridges pure mathematics with real-world applications in data and image analysis.