BIMSA > 牛一帅
副研究员 牛一帅

团队: 人工智能和机器学习 , 量子对称

办公室: A6-301

邮箱: niuyishuai@bimsa.cn

研究方向: 最优化, 高性能计算, 机器学习

个人主页: https://sites.google.com/view/niuys

个人简介

Yi-Shuai Niu, a tenured Associate Professor of Mathematics at Beijing Institute of Mathematical Sciences and Applications (BIMSA), specialized in Optimization, Scientific Computing, Machine Learning, and Computer Sciences. Before joining BIMSA in October 2023, he was a research fellow at the Hong Kong Polytechnic University (2021-2022); an associate professor at Shanghai Jiao Tong University (2014-2021), where he led the “Optimization and Interdisciplinary Research Group” and double-appointed at the ParisTech Elite Institute of Technology and the School of Mathematical Sciences. His earlier roles include postdoc at the University of Paris 6 (2013-2014) and junior researcher both at the French National Center for Scientific Research (CNRS) and Stanford University (2010-2012). He was also a lecturer at the National Institute of Applied Sciences (INSA) of Rouen (2007-2010) in France, where he earned a Ph.D. in Mathematics-Optimization in 2010 and double Masters in Pure and Applied Mathematics and Genie Mathematics in 2006. His research covers a wide range of applied mathematics, with a spotlight on optimization theory, machine learning, high-performance computing, and software development. His works span various interdisciplinary applications including: machine learning, natural language processing, self-driving car, finance, image processing, turbulent combustion, polymer science, quantum chemistry and computing, and plasma physics. His contributions encompass fundamental research, emphasizing novel algorithms for large-scale nonconvex and nonsmooth problems, and practical implementations, focusing on efficient optimization solvers and scientific computing packages using high-performance computing techniques. He developed more than 33 pieces of software and published about 30 articles in prestigious journals and conferences (including SIAM Journal on Optimization, Journal of Scientific Computing, Combustion and Flames, Applied Mathematics and Computation). He was PI of 5 research grants and members of 5 joint international research projects. He was awarded of shanghai teaching achievement award (First prize) in 2017, two outstanding teaching awards (First prize) at Shanghai Jiao Tong University in 2016 and 2017 respectively, as well as 17 awards in international contests of mathematics MCM/ICM (including the INFORMS best paper award in 2017).

研究兴趣

  • 高性能计算
  • 深度学习
  • 最优化理论和算法
  • 湍流燃烧
  • 激光诱导等离子体击穿光谱
  • 图像处理
  • 量子计算
  • 自然语言处理
  • 投资组合
  • 自动驾驶

教育经历

  • 2006 - 2010      法国应用科学院      数学最优化      博士      (Supervisor: Pham Dinh Tao)
  • 2005 - 2006      法国应用科学院      理论和应用数学      硕士
  • 2001 - 2006      法国应用科学院      工程数学      硕士

工作经历

  • 2023 -      北京雁栖湖应用数学研究院      副教授
  • 2023 - 2023      滑铁卢大学      访问教授
  • 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

荣誉与奖项

  • 2017      2017年上海市教学成果奖（一等奖）
  • 2017      MCM/ICM 2017 INFORMS 最佳论文特等奖
  • 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

出版物

  • [1] 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(2024), 2, 1852-1878
  • [2] 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
  • [3] Y.S. Niu, An Accelerated DC Programming Approach With Exact Line Search for the Symmetric Eigenvalue Complementarity Problem, arXiv:2301.09098(2024)
  • [4] Y.S. Niu, Hybrid Accelerated DC Algorithms for the Asymmetric Eigenvalue Complementarity Problem, arXiv:2305.12076(2024)
  • [5] Yu You, and Yi-Shuai Niu, A Refined Inertial DC Algorithm for DC Programming, Optimization and Engineering, 24(2023), 65-91
  • [6] Y. You, Y.S. Niu, A Variable Metric and Nesterov Extrapolated Proximal DCA With Backtracking for A Composite DC Program, Journal of Industrial and Management Optimization, 19(2023), 10, 7716-7734
  • [7] 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:1906.01509(2023)
  • [8] Y.S. Niu, Y. You, M.F. Benammour, Y.J. Wang, Parallel DCCUT Algorithms for Mixed-Binary Linear Programs, arXiv:2103.00717(2023)
  • [9] Y.S. Niu, On the Convergence Analysis of DCA, arXiv:2211.10942(2023)
  • [10] Y.S. Niu, R. Glowinski, Discrete Dynamical System Approaches for Boolean Polynomial Optimization, Journal of Scientific Computing, 92(2022), 2, 1-39
  • [11] Y.S. Niu, H. Zhang, Power-product Matrix: Nonsingularity, Sparsity and Determinant, Linear and Multilinear Algebra(2022), 1-18
  • [12] Y.S. Niu, H.J. Ji, Optimisation Théorie et Algorithmes, Shanghai Jiao Tong University Press(2022)
  • [13] Yi-Shuai Niu, Wentao Ding, Junpeng Hu, Wenxu Xu, and Stephane Canu, Spatio-Temporal Neural Network for Fitting and Forecasting COVID-19, arXiv:2103.11860 (2021)
  • [14] 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(2021), 7, 2407-2432
  • [15] 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(2019), 11363
  • [16] 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
  • [17] 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(2019), 341-351
  • [18] Y.S. Niu, J.J. Judice, H.A. Lethi, D.T. Pham, Improved DC Programming Approaches for Solving Quadratic Eigenvalue Complementarity Problem, Applied Mathematics and Computation, 353(2019), 95-113
  • [19] 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
  • [20] 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(2015), 203-214
  • [21] 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
  • [22] G. Ribert, L. Vervisch, P. Domingo, Y.S. Niu, Hybrid transported-tabulated strategy to downsize detailed chemistry for numerical simulation of flames, Flow, Turbulence and Combustion, 92(2014), 1-2, 175-200
  • [23] Y.S. Niu, L. Vervisch, D.T. Pham, An optimization-based approach to detailed chemistry tabulation: Automated progress variables definition, Combustion and Flame, 160(2013), 4, 776-785
  • [24] 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(2013), 4, 812-829
  • [25] L. Vervisch, Y.S. Niu, G. Lodier, P. Domingo, Recent developments in turbulent combustion modeling: automated progress variables definition − Ignition combustion regimes after rapid compression, ICHMT DIGITAL LIBRARY ONLINE(2012), 37-47
  • [26] 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(2012), 321-330
  • [27] D.T. Pham, Y.S. Niu, An Efficient DC Programming Approach for Portfolio Decision With Higher Moments, Computational Optimization and Applications, 50(2010), 3, 525-554
  • [28] Y.S. Niu, D.T. Pham, A DC Programming Approach for Mixed-Integer Linear Programs, Communications in Computer and Information Science, 14(2008), 244-253

招聘公告

招聘博士生和博士后。博士后待遇优厚，年薪35-50万人民币，有科研启动经费，具体参阅 https://www.mathjobs.org/jobs/list/21900。有意向的申请人（博士或博士后）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有浓厚兴趣，并希望在机器学习、金融、数据分析、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。
  • 博士后补充要求：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，博士学位获得时间不超过五年，年龄不超过35岁。

其他机会：

  • 博士申请人将有机会参加中国人民大学或中国科学院大学博士联培项目。
  • 博士生和博士后在读期间将有机会参与企业合作项目，包括小米、商飞等。

 

更新时间: 2024-09-12 17:18:14