Ling Liang

Ling Liang

Assistant Professor

About me

My research focuses on the development, analysis, and implementation of efficient, robust, and scalable algorithmic frameworks for solving large-scale optimization problems and their applications in Machine Learning, Scientific Computing, and Engineering, aiming to bridge theoretical advancements with practical solutions. Currently, I am interested in

  • Semidefinite Programming
  • Computational Optimal Transport
  • Machine Learning Algorithms
  • Inexact Optimization Algorithms

Experience

  • Assistant Professor (August 2025 - present)
    • University of Tennessee, Knoxville
  • Postdoctoral Associate (August 2023 - July 2025)
  • Visiting Researcher (March 2023 - June 2023)
    • Weierstrass Institute for Applied Analysis and Stochastics
    • Host: Dr. Jia-Jie Zhu
  • Research Fellow (January 2022 - July 2023)

Education

  • Ph.D. in Mathematics (August 2017 - November 2021)
  • B.S. in Mathematics (September 2013 - June 2017)
    • University of Science and Technology of China

Papers

Preprints

  1. Lei Yang, Jiayi Zhu, Ling Liang, Kim-Chuan Toh. Convergence of a relative-type inexact proximal ALM for convex nonlinear programming, 2025. arXiv
  2. Raghav Thind, Youran Sun, Ling Liang, Haizhao Yang. OptimAI: Optimization from natural language using LLM-powered AI agents, 2025. arXiv
  3. Rohan Bhatnagar, Ling Liang, Krish Patel, Haizhao Yang. From equations to insights: Unraveling symbolic structures in PDEs with LLMs, 2025. arXiv
  4. Di Wu, Ling Liang, Haizhao Yang. PINS: Proximal iterations with sparse Newton and Sinkhorn for optimal transport, 2025. arXiv
  5. Jiayi Zhu, Ling Liang, Lei Yang, and Kim-Chuan Toh. ripALM: A relative-type inexact proximal augmented Lagrangian method with applications to quadratically regularized optimal transport, 2024. arXiv
  6. Ling Liang, Haizhao Yang. PNOD: An efficient projected Newton framework for exact optimal experimental designs, 2024. arXiv, CODE
  7. Ling Liang, Cameron Austin, and Haizhao Yang. Accelerating multi-block constrained optimization through learning to optimize, 2024. arXiv

Journal Publications

  1. Ling Liang, Kim-Chuan Toh, and Haizhao Yang. NewVEM: A Newton vertex exchange method for a class of constrained self-concordant minimization problems, Journal of Scientific Computing 105, no. 64 (2025). arXiv, JSC
  2. Ling Liang, Qiyuan Pang, Kim-Chuan Toh, and Haizhao Yang. Nesterov’s accelerated Jacobi-type methods for large-scale symmetric positive semidefinite linear systems, SIAM Journal on Scientific Computing (2025). Accepted. arXiv
  3. Ling Liang, Zusen Xu, Kim-Chuan Toh, and Jia-Jie Zhu. An inexact Halpern iteration with application to distributionally robust optimization, Journal of Optimization Theory and Applications 260, no. 58 (2025): 1-41. JOTA arXiv, CODE
  4. Ching-pei Lee, Ling Liang, Tianyun Tang, and Kim-Chuan Toh. Accelerating nuclear-norm regularized low-rank matrix optimization through Burer-Monteiro decomposition, Journal of Machine Learning Research 25, no. 379 (2024): 1-52. arXiv, JMLR, CODE
  5. Ling Liang, Defeng Sun, and Kim-Chuan Toh. A squared smoothing Newton method for semidefinite programming. Mathematics of Operations Research, 2024. arXiv MOOR
  6. Ling Liang, Haizhao Yang. On the stochastic (variance-reduced) proximal gradient method for regularized expected reward optimization, Transactions on Machine Learning Research, 2024. arXiv, TMLR
  7. Di Hou, Ling Liang, and Kim-Chuan Toh. A sparse smoothing Newton method for solving discrete optimal transport problems, ACM Transactions on Mathematical Software 50, no. 3 (2024): 1–26. arXiv, TOMS
  8. Lei Yang, Ling Liang, Hong T.M. Chu, and Kim-Chuan Toh. A corrected inexact proximal augmented Lagrangian method with a relative error criterion for a class of group-quadratic regularized optimal transport problems. Journal of Scientific Computing 99, no. 79 (2024). arXiv JSC
  9. Hong T.M. Chu, Ling Liang, Kim-Chuan Toh, and Lei Yang. An efficient implementable inexact entropic proximal point algorithm for a class of linear programming problems. Computational Optimization and Applications 85, no. 1 (2023): 107–146. arXiv, COAP, CODE
  10. Heng Yang, Ling Liang, Luca Carlone, and Kim-Chuan Toh. An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization. Mathematical Programming 201, no. 1–2 (2023): 409–472. arXiv, MP, CODE
  11. Ling Liang, Xudong Li, Defeng Sun, and Kim-Chuan Toh. QPPAL: A two-phase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems. ACM Transactions on Mathematical Software 48, no. 3 (2022): 1-27. arXiv, TOMS, CODE
  12. Ying Cui, Ling Liang, Defeng Sun, and Kim-Chuan Toh. On degenerate doubly nonnegative projection problems. Mathematics of Operations Research 47, no. 3 (2022): 2219-2239. arXiv, MOOR
  13. Quoc Tran-Dinh, Ling Liang, and Kim-Chuan Toh. A new homotopy proximal variable-metric framework for composite convex minimization. Mathematics of Operations Research 47, no. 1 (2022): 508-539. arXiv, MOOR
  14. Ling Liang, Defeng Sun, and Kim-Chuan Toh. An inexact augmented Lagrangian method for second-order cone programming with applications. SIAM Journal on Optimization 31, no. 3 (2021): 1748-1773. arXiv, SIOPT

Conference Proceedings

  1. Shucheng Kang, Xiaoyang Xu, Jay Sarva, Ling Liang, and Heng Yang. Fast and certifiable trajectory optimization, Workshop on the Algorithmic Foundations of Robotics, 2024. arXiv, CODE

Invited Talks

  • SIAM New York-New Jersey-Pennsylvania Section Conference, Penns State, November 2025
  • International Conference on Continuous Optimization, USC, Los Angeles, July 2025
  • The 25th International Symposium on Mathematical Programming, Montreal, July 2024
  • 2024 INFORMS Optimization Society Conference, Rice University, March 2024
  • Workshop of Scientific Machine Learning: Theory and Algorithms, UMD, February 2024
  • SIAM Conference on Optimization, The Sheraton Grand Seattle, Seattle, May 2023
  • Hua Luogeng Youth Forum in Applied Mathematics, AMSS, March 2023
  • SIAM Conference on Optimization, Online, July 2021
  • Workshop on Matrix Optimization, Beijing University of Technology, December 2019
  • International Conference on Continuous Optimization, TU Berlin, August 2019

Teaching

  • Fall 2025, MATH519 Mathematical Methods of Machine Learning, Department of Mathematics, UTK
  • Spring 2025, AMSC460 Computational Methods, Department of Mathematics, UMD
  • Fall 2024, MATH241 Calculus III, Department of Mathematics, UMD
  • Spring 2024, MATH401 Applications of Linear Algebra, Department of Mathematics, UMD
  • Fall 2023, AMSC460 Computational Methods, Department of Mathematics, UMD

Services

Review

  • Mathematical Programming
  • SIAM Journal on Optimization
  • Mathematical Programming Computation
  • SIAM Journal on Mathematics of Data Science
  • Computational Optimization and Applications
  • Journal of Scientific Computing
  • INFORMS Journal on Computing
  • Optimization Methods and Software
  • Journal of Industrial and Management Optimization
  • Asia-Pacific Journal of Operational Research
  • Transactions on Machine Learning Research
  • AAAI-26
  • NeurIPS 2025 Workshop ScaleOPT
  • Nonlinear Dynamics

Organization