BIMSA > Yishuai Niu
Associate Professor Yishuai Niu

Yishuai Niu

Associate Professor
Affiliation: BIMSA
Research Field: Optimization, High-Performance Computing, Machine Learning
Office: A6-301
Email: niuyishuai@bimsa.cn
Webpage: https://sites.google.com/view/niuys

Biography

Yi-Shuai Niu is an Associate Professor at the Beijing Institute of Mathematical Sciences and Applications (BIMSA), specializing in optimization, scientific computing, machine learning, and computer science. He also holds a dual appointment at Tsinghua University (Qiuzhen College), where he teaches optimization- and AI-related courses and supervises graduate and undergraduate research. Before joining BIMSA, he was a Research Fellow at The Hong Kong Polytechnic University (2021–2022) and an Associate Professor at Shanghai Jiao Tong University (2014–2021), where he founded the Optimization and Interdisciplinary Research Group and held concurrent appointments at the SJTU-ParisTech Elite Institute of Technology and the School of Mathematical Sciences. His earlier positions included Postdoctoral Fellow at the University of Paris 6 (2013–2014), Junior Researcher at CNRS and Stanford University (2010–2012), and Lecturer at INSA Rouen (2007–2010). He received his Ph.D. in Mathematics–Optimization in 2010 and master’s degrees in "Pure and Applied Mathematics" and "Engineering Mathematics" in 2006.

His research focuses on optimization theory, machine learning, high-performance computing (HPC), and scientific software, with applications in natural language processing, autonomous driving, finance, image processing, turbulent combustion, polymer science, quantum computing, and plasma physics. He develops new theories and algorithms for large-scale nonconvex and nonsmooth optimization, together with efficient HPC-based solvers and scientific computing packages. He has developed more than 36 software packages and published over 40 papers in leading journals and conference proceedings, including the SIAM Journal on Optimization, Journal of Scientific Computing, and Combustion and Flame. He has served as PI of 7 research grants, including an NSFC Key Project, and as a core member of 5 international collaborative projects.

His honors include the Beijing High-Level Overseas Talent Programs, core membership in the Beijing Strategic Scientist Program, the First Prize of the 2017 Shanghai Teaching Achievement Award, and First Prizes of the Shanghai Jiao Tong University Teaching Achievement Awards in 2016 and 2017; he has received 17 MCM/ICM awards, including the 2017 INFORMS Best Paper Award. In 2026, he received the International Congress of Basic Science (ICBS) Innovation Science Award and was named a “Li Bing Engineering Innovation Scholar”.

Research Interest

  • Optimization
  • Deep Learning
  • High-Performance Computing
  • Yau's Affine Normal Descent
  • Yau-Yau Filter
  • Image Processing
  • Portfolio Investment
  • Natural Language Processing
  • Turbulent Combustion
  • Laser Induced Breakdown Spectroscopy
  • Quantum Computing
  • Self-driving Car
  • Data Assimilation

Education Experience

  • 2006 - 2010 | National Institute of Applied Sciences of Rouen, France | Mathematics - Optimization | Doctor | (Supervisor: Pham Dinh Tao)
  • 2005 - 2006 | National Institute of Applied Sciences of Rouen, France | Fundamental and Applied Mathematics | Master
  • 2001 - 2006 | National Institute of Applied Sciences of Rouen, France | Genie Mathematics | Master

Work Experience

  • 2023 - -- | Beijing Institute of Mathematical Sciences and Applications (BIMSA) | Associate Professor
  • 2021 - 2022 | The Hong Kong Polytechnic University | Research Fellow
  • 2018 - 2018 | University of California Irvine | Visiting Professor
  • 2014 - 2021 | Shanghai Jiao Tong University | Associate Professor
  • 2013 - 2014 | University of Paris 6 (UPMC) | Postdoc
  • 2010 - 2012 | French National Center for Scientific Research (CNRS) & Stanford University | Junior Researcher
  • 2007 - 2010 | National Institute of Applied Sciences of Rouen, France | Lecturer

Honors and Awards

  • 2026 | ICBS Innovation Science Award (Li Bing Engineering Innovation Scholar)
  • 2025 | Beijing Overseas High-Level Talent (Innovative Program)
  • 2025 | Beijing High-Level Overseas Scholar
  • 2024 | Ruo Lin Award (Paper Award)
  • 2024 | Core Member of the Beijing Strategic Scientist Program
  • 2017 | MCM/ICM 2017 INFORMS best paper award
  • 2017 | Shanghai teaching achievement award (First prize)
  • 2016 | Outstanding teaching award at Shanghai Jiao Tong University (First prize)
  • 2015 | Excellent teacher’s award at ParisTech-SJTU

Publications

  • [1] Yi-Shuai Niu, Artan Sheshmani, Shing-Tung Yau, Yau's Affine Normal Descent: Algorithmic Framework and Convergence Analysis, arXiv:2603.28448 (2026)
  • [2] Yi-Shuai Niu, Artan Sheshmani, Shing-Tung Yau, Affine Normal Directions via Log-Determinant Geometry: Scalable Computation under Sparse Polynomial Structure, arXiv:2604.01163 (2026)
  • [3] Ya-Juan Wang, Yi-Shuai Niu, Artan Sheshmani, Shing-Tung Yau, Yau's Affine-Normal Descent for Large-Scale Higher-Moment Portfolio Optimization, arXiv:2604.25378 (2026)
  • [4] Yi-Shuai Niu, Yajuan Wang, Scalable Mean-Variance Portfolio Optimization via Subspace Embeddings and GPU-Friendly Nesterov-Accelerated Projected Gradient, arXiv:2604.02917 (2026)
  • [5] Shing-Tung Yau, Yi-Shuai Niu, An Improved Yau-Yau Algorithm for High Dimensional Nonlinear Filtering Problems, Pure and Applied Mathematics Quarterly, 21(6), 2369-2423 (2026)
  • [6] Yi-Shuai Niu, RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs, arXiv:2605.23550 (2026)
  • [7] Yi-Shuai Niu, Shing-Tung Yau, Polylab: A MATLAB Toolbox for Multivariate Polynomial Modeling, arXiv:2604.06575 (2026)
  • [8] Yi-Shuai Niu, Continuous-Time Dynamics of the Difference-of-Convex Algorithm, arXiv:2604.06926 (2026)
  • [9] Yi-Shuai Niu, Hoai An Le Thi, and Dinh Tao Pham, On difference-of-sos and difference-of-convex-sos decompositions for polynomials, SIAM Journal on Optimization, 34(2), 1852-1878 (2024)
  • [10] Bing Cheng, Yi-Shuai Niu, Howell Tong, Shing-Tung Yau, The Second Edge Theorem: The Asymptotic Collapse of Sample-Dependent Information Geometry to the Canonical Flat Canvas of Conventional Statistics in Large Sample Limits, arXiv:2608.19251 (2026)
  • [11] Bing Cheng, Yi-Shuai Niu, Howell Tong, Shing-Tung Yau, Information Geometry (IG) Lives at Edge or Boundary of SMG (statistically meaningful geometry):-the First Edge Theorem and Applications, arXiv:2608.19251 (2026)
  • [12] Bing Cheng, Yi-Shuai Niu, Howell Tong, Shing-Tung Yau, Statistically Meaningful Geometry (SMG) Beyond the Euclidean Paradigm, with Application to Generative AI, arXiv:2607.03329 (2026)
  • [13] Bing Cheng, Yi-Shuai Niu, Howell Tong, Shing-Tung Yau, Statistically Meaningful Geometry and Gauge Symmetry Breaking: A Geometric Foundation for Scientific Discovery and Intelligence Emergence, arXiv:2607.05436 (2026)
  • [14] Youran Sun, Yihua Liu, Yi-Shuai Niu, Understand the Effectiveness of Shortcuts through the Lens of DCA, Data Analytics and Topology, 1(2), 109-116 (2025)
  • [15] Yi-Shuai Niu, Yu You, Benammour M. Faouzi, Yajuan Wang, A parallel difference-of-convex cutting plane algorithm for mixed-binary linear programs, Optimization, 1-38 (2025)
  • [16] Y.S. Niu, H.A. Le Thi, D.T. Pham, BDCA with Exact Line Search for Symmetric Eigenvalue Complementarity Problems, Lecture Notes in Networks and Systems, 159-171 (2025)
  • [17] Y.S. Niu, H. Zhang, Power-product matrix: nonsingularity, sparsity and determinant, Linear and Multilinear Algebra, 72(7), 1170-1187 (2024)
  • [18] H Zhang, YS Niu, A Boosted-DCA with power-sum-DC decomposition for linearly constrained polynomial programs, Journal of Optimization Theory and Applications, 201(2), 720-759 (2024)
  • [19] Y.S. Niu, Hybrid Accelerated DC Algorithms for the Asymmetric Eigenvalue Complementarity Problem, arXiv:2305.12076(2024)
  • [20] Yu You and Yi-Shuai Niu, A variable metric and nesterov extrapolated proximal DCA with backtracking for a composite DC program, Journal of Industrial and Management Optimization, 19(10), 7716-7734 (2023)
  • [21] YS Niu, Accelerated DC Algorithms for the Asymmetric Eigenvalue Complementarity Problem, arXiv preprint arXiv:2305.12076 (2023)
  • [22] Yu You, and Yi-Shuai Niu, A refined inertial DC algorithm for DC programming, Optimization and Engineering, 24(1), 65-91 (2023)
  • [23] Y.S. Niu, An Accelerated DC Programming Approach with Exact Line Search for The Symmetric Eigenvalue Complementarity Problem, arXiv preprint arXiv:2301.09098 (2023)
  • [24] Y.S. Niu, On the convergence analysis of DCA, arXiv preprint arXiv:2211.10942 (2022)
  • [25] Y.S. Niu, R. Glowinski, Discrete Dynamical System Approaches for Boolean Polynomial Optimization, Journal of Scientific Computing, 92(2), 1-39 (2022)
  • [26] Y.S. Niu, H.J. Ji, Optimisation Théorie et Algorithmes, Shanghai Jiao Tong University Press(2022)
  • [27] Yi-Shuai Niu, Yu You, Wenxu Xu, Wentao Ding, Junpeng Hu, and Songquan Yao, A difference-of-convex programming approach with parallel branch-and-bound for sentence compression via a hybrid extractive model, Optimization Letters, 15, 1-26 (2021)
  • [28] Yi-Shuai Niu, Wentao Ding, Junpeng Hu, Wenxu Xu, and Stephane Canu, Spatio-Temporal Neural Network for Fitting and Forecasting COVID-19, arXiv preprint arXiv:2103.11860 (2021)
  • [29] Y.S. Niu, X.W. Hu, Y. You, F. Benammour, H. Zhang, Sentence compression via dc programming approach, Advances in Intelligent Systems and Computing, 991, 341-351 (2020)
  • [30] Chen Sun, Ye Tian, Liang Gao, Yi-Shuai Niu, Tianlong Zhang, Hua Li, Yuqing Zhang, Zengqi Yue, Nicole Delepine-Gilon, and Jin Yu, Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soil with Generalized LIBS Spectra, Scientific Reports, 9(1), 11363 (2019)
  • [31] YS Niu, J Júdice, HA Le Thi, DT Pham, Improved dc programming approaches for solving the quadratic eigenvalue complementarity problem, Applied Mathematics and Computation, 353, 95-113 (2019)
  • [32] Y.S. Niu, Y. You, W.Z. Liu, Parallel DC Cutting Plane Algorithms for Mixed Binary Linear Program, Advances in Intelligent Systems and Computing, 991(2019), 330-340
  • [33] Y.S. Niu, Y.J. Wang, H.A. Le Thi, D.T. Pham, High-order moment portfolio optimization via an accelerated difference-of-convex programming approach and sums-of-squares, arXiv preprint arXiv:1906.01509 (2019)
  • [34] D.T. Pham, H.A. Le Thi, V.N. Pham, Y.S. Niu, DC Programming Approaches for Discrete Portfolio Optimization Under Concave Transaction Costs, Optimization Letters, 10(2016), 2, 261-282
  • [35] Y.S. Niu, J.J. Judice, H.A. Lethi, D.T. Pham, Solving the quadratic eigenvalue complementarity problem by DC programming, Advances in Intelligent Systems and Computing, 359, 203-214 (2015)
  • [36] G Ribert, L Vervisch, P Domingo, YS Niu, Hybrid transported-tabulated strategy to downsize detailed chemistry for numerical simulation of premixed flames, Flow, turbulence and combustion, 92(1-2), 175-200 (2014)
  • [37] Y.S. Niu, D.T. Pham, DC Programming Approaches for BMI and QMI Feasibility Problems, Advances in Intelligent Systems and Computing, 282(2014), 37-63
  • [38] Y.S. Niu, D.T. Pham, H.A. Le Thi and J.J. Judice, Efficient DC programming approaches for the asymmetric eigenvalue complementarity problem, Optimization Methods and Software, 28(4), 812-829 (2013)
  • [39] YS Niu, L Vervisch, DT Pham, An optimization-based approach to detailed chemistry tabulation: Automated progress variable definition, Combustion and Flame, 160(4), 776-785 (2013)
  • [40] B.M. Ndiaye, H.A. Le Thi, D.T. Pham, Y.S. Niu, DC programming and DCA for large-scale two-dimensional packing problems, Lecture Notes in Computer Science, 7197, 321-330 (2012)
  • [41] L Vervisch, YS Niu, G Lodier, P Domingo, Recent developments in turbulent combustion modeling: automated progress variables definition− Ignition combustion regimes after rapid compression, Proceedings of the Seventh International Symposium On Turbulence (2012)
  • [42] D.T. Pham, Y.S. Niu, An efficient DC programming approach for portfolio decision with higher moments, Computational Optimization and Applications, 50(3), 525-554 (2011)
  • [43] YS Niu, DT Pham, Efficient DC programming approaches for mixed-integer quadratic convex programs, Proceedings of the International Conference on Industrial Engineering and Systems Management (IESM2011), 222-231 (2011)
  • [44] YS Niu, Programmation DC & DCA en Optimisation Combinatoire et Optimisation Polynomiale via les Techniques de SDP, INSA de Rouen (2010)
  • [45] Y.S. Niu, D.T. Pham, A DC programming approach for mixed-integer linear programs, Communications in Computer and Information Science, 14, 244-253 (2008)

Academic Service

  • 2025 - -- | Operations Research Forum | Associate Editor

Other

Lectures

Since joining BIMSA, I have taught undergraduate and graduate courses at Tsinghua University (Qiuzhen College) and BIMSA:

  • Linear and Nonlinear Optimization, Tsinghua University, Fall 2026
  • Optimization Methods in Artificial Intelligence, Tsinghua University, Spring 2026
  • Linear and Nonlinear Optimization, Tsinghua University, Fall 2025
  • Optimization Methods in Artificial Intelligence, Tsinghua University, Spring 2025
  • Optimization Theory and Algorithms, Tsinghua University, Fall 2024
  • Optimization Algorithms in Machine Learning, BIMSA, Spring 2024

Before joining BIMSA, I accumulated more than 2,500 teaching hours at Shanghai Jiao Tong University (the SJTU-ParisTech Elite Institute of Technology, the SJTU China-UK Low Carbon College) and INSA Rouen. From 2007 to 2021, I taught a broad range of undergraduate and graduate courses in optimization, machine learning, dynamical systems, functional analysis, real analysis, linear algebra, differential calculus, topology, group theory, probability and statistics, scientific computing, and computer programming. I also supervised research projects and industrial internships.

Recruiting

Seeking assistant professor and highly self-motivated postdocs in optimization. For more details about the positions and how to apply, please feel free to email me directly and see https://www.mathjobs.org/jobs/list/21900 (for postdoc) and https://www.mathjobs.org/jobs/list/27475 (for assistant professor)

Ideal Candidate Profile:
  • Educational Background: Strong foundational knowledge in mathematics and/or computer science
  • Research Interests: Keen interest in optimization theory and algorithms, machine learning and/or dynamical system/nonlinear filtering, differential or algebraic geometry; practical applications such as machine learning, finance, data analysis, biological data processing, weather forecast and data assimilation, image processing, quantum information, and high-performance computing.
  • Language Skills: Must demonstrate fluency in both spoken and written English.
  • Technical Skills: Proficiency in Matlab/Python programming and familiar with High-Performance Computing.
  • Additional Requirements: 
    • For assistant professor positions, preference will be given to candidates with interdisciplinary strengths in optimization + (Differential or Algebraic) geometry + high-performance computing, or optimization + AI.
    • Postdoc candidates should be early-career researchers who have recently obtained or are about to obtain a PhD within the past five years, the age of the candidate must not beyond 35 for Chinese candidates. 
Update Time: 2026-09-12 09:00:07