Associate Dean (Faculty & Staff Affairs), Presidential Chair Professor
Ph.D. Tinbergen Institute, Erasmus University, 1991
B.S. Department of Mathematics, Fudan University, 1984
Professor Zhang Shuzhong is the Presidential Chair Professor at CUHK(SZ). Professor Zhang studied in the Department of Mathematics, Fudan University with a bachelor’s degree and then graduated from the Mathematics Institute of Fudan University with a master’s degree, majoring in operation and control. He obtained his Ph.D. degree in Econometrics and Operations Research at the Tinbergen institute, Erasmus University. In the same year upon graduation, he obtained a teaching position at Groningen University in the Netherlands. He returned to the faculty of the Institute of Econometrics at Erasmus university in 1993. In 1999, he received Award for best research (only one person per year at Erasmus University). In the same year he was ranked 6th among the top 40 economists in the Netherlands. Professor Zhang returned to the Department of Systemetical Engineering and Engineering Management of the Chinese University of Hong Kong in 1999. He received Presidential Exemplary Teaching Award in 2001 and Research Award for Young Scientist in 2003 at CUHK. Professor Zhang taught at the University of Minnesota starting from 2011, when he was the founding director of the Department of Industrial and System Engineering.
Professor Zhang has a long and in-depth research in operational research and optimization theories and methods, as well as a strong interest and extensive experience in the application of operational optimization. His research covers gene expression analysis and disease diagnosis, signal processing and spectrum management, financial investment model and stochastic optimization, risk-return management and robust optimization, equilibrium and efficiency calculation in economic and game theory, and algorithm software design. Professor Zhang has published more than 140 academic papers and monographs, and has been invited to give conference reports at important international academic conferences for many times. In terms of non-convex quadratic programming and non-convex polynomial optimization, Professor Zhang developed the rank-1 matrix decomposition method for solving non-convex quadratic programming problems accurately, and proposed a series of new algorithms for solving non-convex polynomial optimization problems, leading the development of this field. Professor Zhang developed the approximate calculation method of polynomial and tensor optimization model, and also developed the only approximate algorithm that can solve arbitrary degree polynomial optimization model with approximate ratio guarantee. These non-convex optimization models can be applied to many combination problems, graph theory problems, wireless sensor positioning in signal processing and airline revenue management problems. Because of his outstanding research on the theory and algorithm of non-convex polynomial optimization problems, professor Zhang was invited to give a 50-minute presentation at the triennial International Symposium on Mathematical Programming in Chicago in 2009. Jos Sturm, a former doctoral student of Professor Zhang Shuzhong, developed SeDuMi, one of the most famous optimization softwares in the world, based on a series of original dual interior point algorithms for solving semi-positive definite programming problems jointly invented by them. The software has been used more than 5,000 times since its inception. By applying the duality theory of semidefinite programming, Professor Zhang Shuzhong and his collaborators have solved the problem of solving the stochastic linear quadratic optimal control model in theory and practice. Professor Zhang and Professor Luo Zhiquan applied the theory of functional analysis to signal processing and proposed a solution to the problem of dynamic spectrum management in signal processing. The results received the IEEE Signal Processing Society Best Paper Award in 2009. Professor Zhang received the Best Paper Award in the Journal of Signal Processing by the International Association for Signal Processing in 2016. In recent years, Professor Zhang and his students have made breakthroughs on the large-scale optimization model by using the low-order method, especially in solving non-convex optimization model generated in big data analysis (including tensor calculation modeling). In 2016, He was invited to the International conference for continuous optimization held in Tokyo, Japan for 1 hour on this research results. Professor Zhang was the Honorary Dean of the School of Management, Chinese University of Mining and Technology (2003-2006), and has been a visiting professor of Fudan university, Chinese Academy of Sciences, Tsinghua University, Shanghai University, Shanghai University of Finance and Economics. Professor Zhang is on the editorial board of many important international journals, including Operations Research and Management Science, part of the INFORMS society.
1. B. Jiang, F. Yang, and S. Zhang, Tensor and Its Tucker Core: the Invariance Relationships. To appear in Numerical Linear Algebra with Applications.
2. B. Chen, S. He, Z. Li, and S. Zhang, On new classes of nonnegative symmetric tensors, SIAM Journal on Optimization, 27 (1), 292-318, 2017.
3. B. Jiang, Z. Li, and S. Zhang, On Cones of Nonnegative Quartic Forms, Foundations of Computational Mathematics, 17, 161-197, 2017.
4. T. Lin, S. Ma and S. Zhang, An Extragradient-Based Alternating Direction Method for Convex Minimization, Foundations of Computational Mathematics, 17, 35-59, 2017.
5. B. Jiang, S. Ma, M. Hardin, L. Qiao, J. Causey, I. Bitts, D. Johnson, S. Zhang and X. Huang, SparRec: An effective matrix completion framework of missing data imputation for GWAS, Scientific Reports, 6, Article Number: 35534 (2016).
6. X. Gao and S. Zhang, First-Order Algorithms for Convex Optimization with Nonseparate Objective and Coupled Constraints, Journal of Operations Research Society of China, DOI: 10.1007/s40305-016-0131-5, June 2016.
7. T. Lin, S. Ma, and S. Zhang, Iteration Complexity Analysis of Multi-Block ADMM for a Family of Convex Minimization without Strong Convexity, Journal of Scientific Computing, 69 (1), 52-81, 2016.
8. S. Tao, D. Boley and S. Zhang, Local Linear Convergence of ISTA and FISTA on the LASSO Problem, SIAM Journal on Optimization, 26 (1), 313-336, 2016.
9. Y. Liu, S. Ma, Y. Dai and S. Zhang, A Smoothing SQP Framework for a Class of Composite Lq Minimization over Polyhedron, Mathematical Programming, 158 (1-2), 467-500, 2016.
10. B. Jiang, Z. Li and S. Zhang, Characterizing Real-Valued Multivariate Complex Polynomials and Their Symmetric Tensor Representations, SIAM Journal on Matrix Analysis and Applications, 37 (1), 381-408, 2016.
11. T. Lin, S. Ma, and S. Zhang, On the Sublinear Convergence Rate of Multi-block ADMM, Journal of Operations Research Society of China, 3 (3), 251-274, 2015.
12. S. Ma, D. Johnson, C. Ashby, D. Xiong, C. L. Cramer, J. H. Moore, S. Zhang, and X. Huang, SPARCoC: a new framework for molecular pattern discovery and cancer gene identification, PLOS ONE, 10 (3): e0117135. DOI: 10.1371/journal.pone.0117135. Published online on March 13, 2015.
13. Z. Li, A. Uschmajew, and S. Zhang, Linear Convergence Analysis of the Maximum Block Improvement Method for Spherically Constrained Optimization, SIAM Journal on Optimization, 25(1), 210-233, 2015.
14. B. Chen, Z. Li, and S. Zhang, On tensor Tucker decomposition: the case for an adjustable core size, Journal of Global Optimization 62 (4), 811-832, 2015.
15. T. Lin, S. Ma, and S. Zhang, On the Global Linear Convergence of the ADMM with Multi-Block Variables, SIAM Journal on Optimization, 25 (3), 1478-1497, 2015.
16. B. Jiang, S. Ma, and S. Zhang, Tensor Principal Component Analysis via Convex Optimization, Mathematical Programming, 150, 423-457, 2015.
17. X. Huang et al., Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm, BioData Mining, 8 (7) DOI 10.1186/s13040-015-0037-5, 2015.
18. B. Jiang, S. Ma, and S. Zhang, Alternating Direction Method of Multipliers for Real and Complex Polynomial Optimization Models. Optimization, 63 (6), 883-898, 2014.
19. B. Jiang, Z. Li, and S. Zhang, Approximation Methods for Complex Polynomial Optimization, Computational Optimization and Applications, 59 (1), 219-248, 2014.
20. S. He, B. Jiang, Z. Li, and S. Zhang, Probability Bounds for Polynomial Functions in Random Variables, Mathematics of Operations Research, 39 (3), 889-907, 2014.
21. S. I. Birbil, J. B.G. Frenk, J. Gromicho, and S. Zhang, A Network Airline Revenue Management Framework Based on Decomposition by Origins and Destinations, Transportation Science, 48 (3), 313-333, 2014.
22. S. He, Z. Li, and S. Zhang, General Constrained Polynomial Optimization: an Approximation Approach, Mathematics of Computation, S 0025-5718(2014)02875-5. Article electronically published on July 24, 2014.
23. S. He, B. Jiang, Z. Li and S. Zhang, Moments Tensors, Hilbert's Identity, and k-wise Uncorrelated Random Variables, Mathematics of Operations Research, 39 (3), 775-788, 2014.
24. M. H. Wong and S. Zhang, On Distributional Robust Probability Functions and Their Computations, European Journal of Operational Research, 233, 23-33, 2014.
25. X. Huang et al., No-boundary thinking in bioinformatics research, BioData Mining, 6 (19), 2013.
26. Y.W. Huang, D. P. Palomar, and S. Zhang, Lorentz-Positive Maps and Quadratic Matrix Inequalities with Applications to Robust MISO Transmit Beamforming, IEEE Transaction on Signal Processing, 61 (5), 1121-1130, 2013.
27. X. Chen, J. Peng and S. Zhang, Existence of Sparse Solutions to the Standard Quadratic Programming with Random Matrices, Mathematical Programming, Ser. A, 141, 273-293, 2013.
28. A. Aubry, A. De Maio, B. Jiang, and S. Zhang, Ambiguity Function Shaping for Cognitive Radar via Complex Quartic Optimization, IEEE Transaction on Signal Processing, 61, 5603-5619, 2013.
29. S. He, X. G. Wang, and S. Zhang, On a Generalized Cournot Oligopolistic Competition Game, Journal of Global Optimization, 56 (40), 1335-1345, 2013.
30. S. He, Z. Li, and S. Zhang, Approximation Algorithms for Discrete Polynomial Optimization, Journal of Operations Research Society of China, 1, 3-36, 2013.
31. M. H. Wong and S. Zhang, Computing Best Bounds for Nonlinear Risk Measures with Partial Information. Insurance: Mathematics and Economics, 52 (2), 204-212, 2013.