معرفی
Longxiu Huang is an Assistant Professor at Michigan State University (MSU), holding dual appointments in the Department of Computational Mathematics, Science and Engineering (CMSE) and the Department of Mathematics. Prior to this, she was an Assistant Adjunct Professor at UCLA's Department of Mathematics. Her research focuses on Data/Signal Processing, Applied Harmonic Analysis, Machine Learning, Random Matrix/Tensor Analysis, Dynamical Sampling, and Optimization.
Education:
- Ph.D. in Mathematics, Vanderbilt University (2014-2019)
- M.S. in Mathematics, Fudan University (2012-2014)
- B.S. in Mathematics, Sun Yat-Sen University (2009-2012)
- B.S. in Software Engineering, Sun Yat-Sen University (2008-2009)
Research Interests: Huang's work bridges theoretical mathematics and applied computational methods. She explores signal reconstruction in dynamical systems, tensor decompositions for high-dimensional data, robust matrix/tensor completion, and optimization algorithms for ill-conditioned problems. Her contributions include CUR-based methods for low-rank approximations and sampling strategies for sparse signals on graphs.
Professional Activities: She co-organizes the One World MINDS Seminar, a global initiative in mathematical data science. Her research has been published in top journals like SIAM Journal on Imaging Sciences and Applied and Computational Harmonic Analysis, with a focus on theoretical guarantees and practical applications in imaging and machine learning.
Lab/Teams: Active collaborator in interdisciplinary projects at MSU, particularly in computational mathematics and machine learning applications.
