Professor Zhang is interested in decision models in the context of Operations Research, optimization, machine, and statistical learning. His research activities span various aspects of modeling, algorithm design, theory and applications of the above-mentioned areas. 

In recent years, Professor Zhang received the following research grants: 

• Co-Investigator, EAGER: New Approach: Early Diagnosis of Alzheimer’s Disease Based on Magnetic Resonance Imaging (MRI) via High-Dimensional Image Feature Identification, NSF (FAIN-1723529), (co-PI: Xiuzhen Huang). Duration: August 2017 – July 2019. Total amount: $241,785.
• Principal Investigator, Gradient Methods for Solving Big Data (Tensor) Optimization Problems, NSF (CMMI-1462408). Duration: September 2015 – August 2018. Total amount: $299,999.
• Principal Investigator, Polynomial Optimization: Solution Methods and Applications, NSF (CMMI-1161242). Duration: June 2012 – May 2015. Total amount: $359,859.

Research Groups

Shuzhong Zhang is Professor and founding Department Head of Department of Industrial and System Engineering, University of Minnesota. He received a B.Sc. degree in Applied Mathematics from Fudan University in 1984, and a Ph.D degree in Operations Research and Econometrics from the Tinbergen Institute, Erasmus University, in 1991. He had held faculty positions at Department of Econometrics, University of Groningen (1991-1993), and Econometric Institute, Erasmus University (1993-1999), and Department of Systems Engineering & Engineering Management, The Chinese University of Hong Kong (1999-2010). He received the Erasmus University Research Prize in 1999, the CUHK Vice-Chancellor Exemplary Teaching Award in 2001, the SIAM Outstanding Paper Prize in 2003, the IEEE Signal Processing Society Best Paper Award in 2010, and the 2015 SPS Signal Processing Magazine Best Paper Award. Dr. Zhang was an elected Council Member at Large of the MPS (Mathematical Programming Society) (2006-2009), and served as Vice-President of the Operations Research Society of China (ORSC) (2008-2012). He serves on the Editorial Board of several academic journals, including Operations Research, and Management Science.

More information can be found on Zhang's personal website.

Education

B.S. in Applied Mathematics, Fudan University, Shanghai, China, 1984.
Ph.D. in Operations Research, Erasmus University, The Netherlands, 1991.

Professional Background

• Assistant Professor, Department of Econometrics, University of Groningen, The Netherlands, 1991-1993.
• Assistant Professor, Econometric Institute, Erasmus University, The Netherlands, 1993-1999.
• Associate Professor, Department of Systems Engineering & Engineering Management, The Chinese University of Hong Kong, 1999 - 2002.
• Professor, Department of Systems Engineering & Engineering Management, The Chinese University of Hong Kong, 2002 - 2010.  
• Professor, Department of Industrial & Systems Engineering, University of Minnesota, 2011 till now (founding Department Head: 2012 - 2018).

 

Scientific & Professional Societies

Honors and Awards

• The 2015 SPS Signal Processing Magazine Best Paper Award;
• IEEE Signal Processing Society Best Paper Award, 2009.
• The SIAM Outstanding Paper Prize, 2003.
• The Young Researcher Award, The Chinese University of Hong Kong, 2003.
• The Vice-Chancellor’s Exemplary Teaching Award, The Chinese University of Hong Kong, 2001.
• Erasmus University Research Prize, 1999.

Selected Publications

• K. Huang, J. Zhang and S. Zhang, Cubic Regularized Newton Method for Saddle Point Models: a Global and Local Convergence Analysis. Journal of Scientific Computing, 91 (2), 1 – 31, 2022.
• X. Chen, B. Jiang, T. Lin and S. Zhang, Accelerating Adaptive Cubic Regularization of Newton’s Method via Random Sampling. Journal of Machine Learning Research, 23 (90), 1 – 38, 2022.
• K. Huang and S. Zhang, New First-Order Algorithms for Stochastic Variational Inequalities. To appear in SIAM Journal on Optimization.
• J. Zhang, M. Hong, and S. Zhang, On Lower Iteration Complexity Bounds for the Saddle Point Problems, forthcoming in Mathematical Programming.
• J. Zhang, L. Xiao, and S. Zhang, Adaptive Stochastic Variance Reduction for Subsampled Newton Method with Cubic Regularization, forthcoming in INFORMS Journal on Optimization.
• B. Jiang, H. Wang, and S. Zhang, An Optimal High-Order Tensor Method for Convex Optimization, forthcoming in Mathematics of Operations Research.
• T. Lin, S. Ma, Y. Ye, and S. Zhang, An ADMM-Based Interior-Point Method for Large-Scale Linear Programming, Optimization Methods and Software, 36 (2-3), 389 – 424, 2021.
• B. Jiang, T. Lin, and S. Zhang, A Unified Scheme to Accelerate Adaptive Cubic Regularization and Gradient Methods for Convex Optimization, SIAM Journal on Optimization, 30 (4), 2897–2926, 2020.
• J. Zhang, S. Ma, and S. Zhang, Primal-Dual Optimization Algorithms over Riemannian Manifolds: an Iteration Complexity Analysis. To appear in Mathematical Programming.
• X. Gao, Y. Xu, and S. Zhang, Randomized Primal-Dual Proximal Block Coordinate Updates, Journal of the Operations Research Society of China, 7 (2), 205 – 250, 2019.
• C. Lu, Y.F. Liu, W.Q. Zhang, and S. Zhang, Tightness of a New and Enhanced Semidefinite Relaxation for MIMO Detection, SIAM Journal on Optimization, 29 (1), 719 – 742, 2019.
• B. Jiang, T. Lin, S. Ma, and S. Zhang, Structured Nonconvex and Nonsmooth Optimization: Algorithms and Iteration Complexity Analysis, Computational Optimization and Applications, 72, 15 - 157, 2019.
• B. Jiang, S. Ma and S. Zhang, Low-M-Rank Tensor Completion and Robust Tensor PCA, IEEE Journal of Selected Topics in Signal Processing,12 (6), 1390 – 1404, 2018.
• J. Zhang, H. Liu, Z. Wen, and S. Zhang, A Sparse Completely Positive Relaxation of the Modularity Maximization for Community Detection, SIAM Journal of Scientific Computing, 40 (5), A3091-A3120, 2018.
• T. Lin, S. Ma, and S. Zhang, Global Convergence of Unmodified 3-Block ADMM for a Class of Convex Minimization Problems. SIAM Journal on Scientific Computing, 76, 69 - 88, 2018.
• X. Gao, B. Jiang and S. Zhang, On the Information-Adaptive Variants of the ADMM: an Iteration Complexity Perspective, Journal of Scientific Computing, 76 (1), 327 – 363, 2018.
• Y. Xu and S. Zhang, Accelerated Primal-Dual Proximal Block Coordinate Updating Methods for Constrained Convex Optimization, Computational Optimization and Applications, 70 (1), 91 - 128, 2018.
• B. Jiang, F. Yang, and S. Zhang, Tensor and Its Tucker Core: the Invariance Relationships. Numerical Linear Algebra with Applications, 24 (3), 2017. DOI: 10.1002/nla.2086.
• 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.
• B. Jiang, Z. Li, and S. Zhang, On Cones of Nonnegative Quartic Forms, Foundations of Computational Mathematics, 17, 161 – 197, 2017.
• T. Lin, S. Ma and S. Zhang, An Extragradient-Based Alternating Direction Method for Convex Minimization, Foundations of Computational Mathematics, 17, 35 – 59, 2017.