Yangjing ZHANG 张羊晶

I am a tenured associate professor at the Yau Mathematical Sciences Center, Tsinghua University, which I joined in September 2026. Previously, I worked at the Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, where I served as an assistant professor from October 2021 to March 2025 and a tenured associate professor from April 2025 to August 2026.

Before joining the institute, I was a research fellow in the Department of Mathematics at the National University of Singapore, working with Professor Kim-Chuan Toh. I received my Ph.D. in the Department of Mathematics from the National University of Singapore in May 2019, under the supervision of Professor Kim-Chuan Toh and Professor Defeng Sun. I received my B.S. in Mathematics from Tsinghua University in 2014, where I was advised by Professor Wenxun Xing.

My research focuses on large-scale sparse optimization, the design of efficient algorithms for statistical and graphical models, and the analysis of matrix-variate data.

[CV] Updated: Sep 2026

Email: yangjing_zhang [at] tsinghua [dot] edu [dot] cn

zhangyangjing [at] u [dot] nus [dot] edu

GitHub Google Scholar

Preprints

Shengxiang Deng, Xudong Li, Yangjing Zhang, Exact low-dimensional reformulations for regularized spectral approximation. arXiv:2608.27052.
Meixia Lin, Qian Zhang, Yangjing Zhang, Fast joint graph Laplacian learning with nonconvex sparsity control, submitted, 2026.
Meixia Lin, Ziyang Zeng, Yangjing Zhang, Graph-guided fused regularization for single- and multi-task regression on spatiotemporal data. arXiv:2602.14480.
Shengxiang Deng, Xudong Li, Yangjing Zhang, Scalable kernel quantile regression: A preconditioned augmented Lagrangian method. arXiv:2510.07929.
Yuexin Zhou, Chao Ding, Yangjing Zhang, On some perturbation properties of nonsmooth optimization on Riemannian manifolds with applications. arXiv:2308.06793.

Publications

Meixia Lin, Yangjing Zhang, Low rank convex clustering for matrix-valued observations, SIAM Journal on Optimization, 36 (2026), pp. 1446–1475. arXiv:2412.17328.
Shengxiang Deng, Xudong Li, Yangjing Zhang, Alternating minimization for square root principal component pursuit, INFORMS Journal on Computing, online, 2025. arXiv:2501.00471. [code]
Meixia Lin, Ziyang Zeng, Yangjing Zhang, Multiple regression for matrix and vector predictors: model, theory, algorithms, and beyond, Electronic Journal of Statistics, 18 (2024), pp. 5563–5600. arXiv:2410.19264.
Meixia Lin, Yangjing Zhang, DNNLasso: Scalable graph learning for matrix-variate data, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 238 (2024), pp. 316–324. arXiv:2403.02608. [code]
Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh, On efficient and scalable computation of the nonparametric maximum likelihood estimator in mixture models, Journal of Machine Learning Research, 25 (2024), pp. 1–46. arXiv:2208.07514. [code]
Yangjing Zhang, Kim-Chuan Toh, Defeng Sun, Learning graph Laplacian with MCP, Optimization Methods and Software, 39 (2024), pp. 569–600. arXiv:2010.11559.
Shiwei Wang, Chao Ding, Yangjing Zhang, Xinyuan Zhao, Strong variational sufficiency for nonlinear semidefinite programming and its implications, SIAM Journal on Optimization, 33 (2023), pp. 2988–3011. arXiv:2210.04448.
Hong T.M. Chu, Kim-Chuan Toh, Yangjing Zhang, On regularized square-root regression problems: distributionally robust interpretation and fast computations, Journal of Machine Learning Research, 23 (2022), pp. 1–39. arXiv:2109.03632.
Ning Zhang, Yangjing Zhang, Defeng Sun, Kim-Chuan Toh, An efficient linearly convergent regularized proximal point algorithm for fused multiple graphical Lasso problems, SIAM Journal on Mathematics of Data Science, 3 (2021), pp. 524–543. arXiv:1902.06952. [code]
Yangjing Zhang, Ning Zhang, Defeng Sun, Kim-Chuan Toh, A proximal point dual Newton algorithm for solving group graphical Lasso problems, SIAM Journal on Optimization, 30 (2020), pp. 2197–2220. arXiv:1906.04647.
Yangjing Zhang, Ning Zhang, Defeng Sun, Kim-Chuan Toh, An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems, Mathematical Programming, 179 (2020), pp. 223–263. arXiv:1712.05910. [code]