北京雁栖湖应用数学研究院

• 2023 -      北京雁栖湖应用数学研究院      副教授
• 2021 - 2022      香港理工大学      研究员
• 2018 - 2018      加州大学尔湾分校      访问教授
• 2014 - 2021      上海交通大学      副教授
• 2013 - 2014      巴黎六大      博士后
• 2010 - 2012      法国国家科研中心      研究员
• 2007 - 2010      法国应用科学院      讲师

• 2025      北京市创新类海外人才
• 2025      北京市高层次留学人才
• 2024      若琳论文奖
• 2024      北京市战略科学家核心团队成员
• 2017      MCM/ICM 2017 INFORMS 最佳论文特等奖
• 2017      2017年上海市教学成果奖（一等奖）
• 2016      2016年上海交通大学优秀教学奖（一等奖）
• 2015      上海交大巴黎高科卓越工程师学院优秀教师奖

• 2025 -      Operations Research Forum      副主编

• [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 Unrestricted 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, Shing-Tung Yau, Polylab: A MATLAB Toolbox for Multivariate Polynomial Modeling, arXiv:2604.06575 (2026)
• [7] Yi-Shuai Niu, Continuous-Time Dynamics of the Difference-of-Convex Algorithm, arXiv:2604.06926 (2026)
• [8] 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)
• [9] 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)
• [10] 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)
• [11] 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)
• [12] Y.S. Niu, H. Zhang, Power-product matrix: nonsingularity, sparsity and determinant, Linear and Multilinear Algebra, 72(7), 1170-1187 (2024)
• [13] H. Zhang, Y.S. Niu, A Boosted-DCA with Power-Sum-DC Decomposition for Linearly Constrained Polynomial Program, Journal of Optimization Theory and Applications, 201(2024), 720-759
• [14] Y.S. Niu, Hybrid Accelerated DC Algorithms for the Asymmetric Eigenvalue Complementarity Problem, arXiv:2305.12076(2024)
• [15] 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)
• [16] YS Niu, Accelerated DC Algorithms for the Asymmetric Eigenvalue Complementarity Problem, arXiv preprint arXiv:2305.12076 (2023)
• [17] Yu You, and Yi-Shuai Niu, A refined inertial DC algorithm for DC programming, Optimization and Engineering, 24(1), 65-91 (2023)
• [18] Y.S. Niu, An Accelerated DC Programming Approach with Exact Line Search for The Symmetric Eigenvalue Complementarity Problem, arXiv preprint arXiv:2301.09098 (2023)
• [19] Y.S. Niu, On the convergence analysis of DCA, arXiv preprint arXiv:2211.10942 (2022)
• [20] Y.S. Niu, R. Glowinski, Discrete Dynamical System Approaches for Boolean Polynomial Optimization, Journal of Scientific Computing, 92(2), 1-39 (2022)
• [21] Y.S. Niu, H.J. Ji, Optimisation Théorie et Algorithmes, Shanghai Jiao Tong University Press(2022)
• [22] 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)
• [23] 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)
• [24] YS Niu, Y You, A Difference-of-Convex Cutting Plane Algorithm for Mixed-Binary Linear Program, arXiv preprint arXiv:2103.00717 (2021)
• [25] 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)
• [26] 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)
• [27] 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)
• [28] 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
• [29] 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)
• [30] 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
• [31] 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)
• [32] 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)
• [33] 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
• [34] 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)
• [35] 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)
• [36] 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)
• [37] 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)
• [38] 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)
• [39] 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)
• [40] YS Niu, Programmation DC & DCA en Optimisation Combinatoire et Optimisation Polynomiale via les Techniques de SDP, INSA de Rouen (2010)
• [41] 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)

招聘公告

招聘最优化方向的助理教授和博士后，待遇优厚，有科研启动经费，具体参阅

 https://www.mathjobs.org/jobs/list/21900 (博士后申请者) 和 https://www.mathjobs.org/jobs/list/27475 (助理教授申请者)。有意向者请邮件和我联系。

理想候选人要求：

• 教育背景：具备扎实的数学和计算机科学背景。
• 研究兴趣：对最优化理论和算法、机器学习、动力系统/非线性滤波、微分几何/代数几何 等相关方向有浓厚兴趣，并希望在机器学习、金融、数据分析、天气预报和数据同化、图像处理、量子计算、高性能计算等一个或多个应用领域深入研究。
• 语言技能：流利地使用英语进行口头和书面交流。
• 编程能力：熟练掌握Matlab或Python编程，熟悉高性能并行计算技术。
• 补充要求：
  • 对于助理教授岗位，优先考虑在优化 +（微分或代数）几何 + 高性能计算，或优化 + 人工智能等方向具有交叉背景的候选人。
  • 博士后申请人应为近期获得或即将获得博士学位的年轻科研人员，对于中国申请者，博士学位获得时间不超过五年，年龄不超过35岁。