林媚霞，2026年5月起任职于中国人民大学统计与大数据研究院。2016年南京大学数学系获学士学位，2020年新加坡国立大学获数学博士学位。主要研究领域包括数值优化、非光滑优化、统计优化等。

详见个人主页： https://linmeixia.github.io/

Selected Publications

Q. Zhang, and M. Lin*, Low-rank categorical matrix completion with its application to quasispecies analysis, Journal of Scientific Computing, 2026, to appear.

M. Lin, and Y. Zhang*, Low rank convex clustering for matrix-valued observations, SIAM Journal on Optimization, 2026, to appear.

H. T. M. Chu, M. Lin*, and K.-C. Toh, Wasserstein distributionally robust optimization and its tractable regularization formulations, Journal of Optimization Theory and Applications, 2026, Vol. 208(2), pp. 69.

Y. Yuan, M. Lin*, D. F. Sun, and K.-C. Toh, Adaptive sieving: A dimension reduction technique for sparse optimization problems, Mathematical Programming Computation, 2025, Vol. 17, pp. 585616.

M. Lin, D. F. Sun, K.-C. Toh, and C. Wang*, Estimation of sparse Gaussian graphical models with hidden clustering structure, Journal of Machine Learning Research, 2024, Vol. 25(256), pp. 1-36.

M. Lin*, D. F. Sun, and K.-C. Toh, An augmented Lagrangian method with constraint generation for shape-constrained convex regression problems, Mathematical Programming Computation, 2022, Vol. 14, pp. 223-270.

M. Lin, Y.-J. Liu*, D. F. Sun, and K.-C. Toh, Efficient sparse semismooth Newton methods for the clustered lasso problem, SIAM Journal on Optimization, 2019, Vol. 29(3), pp. 2026-2052.