There are some openings for graduate students (MSc, Ph.D.), research assistants, and post-docs in my team. Please contact me if you are interested.

Chao Wang is an Assistant Professor at Southern University of Science and Technology (SUSTech). Before joining SUSTech, Chao was a postdoctoral researcher at the University of California Davis. He worked with Prof. Chen-Nee Chuah. He completed his Ph.D. in Mathematics from The Chinese University of Hong Kong under the direction of Prof. Raymond Chan and worked closely with Prof. Robert Plemmons at Wake Forest University. Previously, he worked with Prof. Yifei Lou at the University of Texas (UT) Dallas and Prof.Xun Jia at UT Southwestern Medical Center.

His research interests include scientific computing, compressed sensing, interdisciplinary mathematical modeling, convex and nonconvex optimization, medical imaging, machine learning, and numerical linear algebra.

Recent News

• Sep. 2024, our work on “OwMatch: Conditional Self-Labeling with Consistency for Open-world Semi-Supervised Learning” was accepted by NeurIPS; our work on “Minimizing Quotient Regularization Model” was accepted by Inverse Problems and Imaging.

• Aug. 2024, our work on Poissonian Image Restoration via the L1/L2-based minimization was accepted by Journal of Scientific Computing.

• Jul. 2024, our work on A nonlinear high-order transformations-based method for high-order tensor completion was published in Signal Processing; our work on Superpixel-informed Implicit Neural Representation for Multi-Dimensional Data was accepted by the European Conference on Computer Vision (ECCV) 2024.

• May 2024, our work on Hyperspectral and multispectral image fusion with arbitrary resolution through self-supervised representations was released in arXiv.

• Feb. 2024, our work on Hyperspectral sparse fusion using adaptive total variation regularization and superpixel-based weighted nuclear norm was accepted by Signal Processing.

• Feb. 2024, our work on Sorted L1/L2 Minimization for Sparse Signal Recovery was accepted by Journal of Scientific Computing.

• Jan. 2024, our work on A scale-invariant relaxation in low-rank tensor recovery with an application to tensor completion was accepted by SIAM Journal on Imaging Sciences.

• Dec. 2023, our work on Nonnegative Matrix Functional Factorization for Hyperspectral Unmixing with Non-uniform Spectral Sampling was accepted by IEEE Transactions on Geoscience and Remote Sensing.

• Nov. 2023, our work on Robust retrieval of material chemical states in X-ray microspectroscopy was accepted by Optics Express.

• Oct. 2023, our work on LocNet: Deep learning-based localization on rotating point spread function with applications to telescope imaging was accepted by Optics Express.

• Sep. 2023, I started this personal website and stopped updating my Google site.

• Aug. 2023, our work on Hyperspectral and multispectral image fusion via superpixel-based weighted nuclear norm minimization was published by IEEE Transactions on Geoscience and Remote Sensing.