Prof. MA, Shiqian 馬 士 謙 教授

Assistant Professor
BS (Peking University)
MSc (Chinese Academy of Sciences)
MPhil, PhD (Columbia University)

Research Interests :
 * Mathematical Programming
 * Scientific Computing
 * Applications of Optimization in Data Sciences,
Machine Learning and Information Sciences

Office :  Room 508, William M.W. Mong
             Engineering Building
Tel      :  (852) 3943-8240
Email  :

=> Prof. Ma's personal home page


Shiqian Ma received his B.S. from Peking University in 2003, M.S. from Chinese Academy of Sciences in 2006 and Ph.D. in Industrial Engineering and Operations Research from Columbia University in 2011. He then spent one and half years in the Institute for Mathematics and Its Applications at University of Minnesota as an NSF postdoctoral fellow. Shiqian Ma joined the Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong in December 2012. His current research interests include theory and algorithms for large-scale optimization and its applications in big data analytics, statistics, machine learning, bioinformatics, signal processing and image processing.

Shiqian Ma received the INFORMS Optimization Society best student paper prize in 2010, honorable mention of INFORMS George Nicholson student paper competition in 2011. He was one of the finalists of the 2011 IBM Herman Goldstine fellowship. He received the Journal of the Operations Research Society of China Excellent Paper Award in 2016. 


Selected Publications

X. Wang, S. Ma, D. Goldfarb and W. Liu. Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization. Accepted in SIAM Journal on Optimization. 2017

B. Jiang, S. Ma, J. Causey, L. Qiao, M. P. Hardin, I. Bitts, D. Johnson, S. Zhang and X. Huang. SparRec: An effective matrix completion framework of missing data imputation for GWAS. Accepted in Scientific Reports. 2016

C. Tan, S. Ma, Y.-H. Dai and Y. Qian. Barzilai-Borwein Step Size for Stochastic Gradient Descent. NIPS. 2016

X. Wang, S. Ma and Y. Yuan. "Penalty Methods with Stochastic Approximation for Stochastic Nonlinear Programming". Mathematics of Computation. to appear, 2016.

Y. Liu, S. Ma, Y. Dai and S. Zhang. A Smoothing SQP Framework for a Class of Composite L_q Minimization over Polyhedron. Mathematical Programming Series A, 158(1): 467-500, 2016.

T. Lin, S. Ma and S. Zhang. An Extragradient-Based Alternating Direction Method for Convex Minimization. Foundations of Computational Mathematics. to appear, 2015.

C. Chen, S. Ma and J. Yang. A general inertial proximal point algorithm for mixed variational inequality problem. SIAM Journal on Optimization. 25 (4): 2120-2142, 2015

C. Chen, R. H. Chan, S. Ma and J. Yang, Inertial Proximal ADMM for Linearly Constrained Separable Convex Optimization. SIAM Journal on Imaging Sciences, 8 (4): 2239-2267, 2015.

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.

Y. Liu, Y. Dai and S. Ma. Joint Power and Admission Control: Non-Convex L_q Approximation and An Effective Polynomial Time Deflation Approach. IEEE Transactions on Signal Processing. 63 (14): 3641-3656, 2015.

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, 2015

B. Jiang, S. Ma and S. Zhang. Tensor Principal Component Analysis via Convex Optimization. Mathematical Programming Series A, 150 (2): 423-457, 2015.

N. S. Aybat, D. Goldfarb and S. Ma. Efficient Algorithms for Robust and Stable Principal Component Pursuit. Computational Optimization and Applications, 58: 1-29, 2014.

D. Goldfarb, S. Ma and K. Scheinberg, “Fast Alternating Linearization Methods for Minimizing the Sum of Two Convex Functions”. Mathematical Programming Series A, 141 (1-2): 349-382, 2013.

B. Huang, S. Ma and D. Goldfarb, “Accelerated Linearized Bregman Method”. Journal of Scientific Computing, 54 (2-3): 428-453, 2013.

L. Xue, S. Ma and H. Zou, “Positive Definite L1 Penalized Estimation of Large Covariance Matrices”. Journal of the American Statistical Association. 107(500): 1480-1491, 2012.

D. Goldfarb and S. Ma, “Fast Multiple Splitting Algorithms for Convex Optimization”. SIAM Journal on Optimization, 22 (2): 533-556, 2012.

S. Ma, D. Goldfarb and L. Chen, “Fixed Point and Bregman Iterative Methods for Matrix Rank Minimization”. Mathematical Programming Series A. 128 (1): 321-353, 2011.

D. Goldfarb and S. Ma, “Convergence of Fixed-Point Continuation Algorithms for Matrix Rank Minimization”. Foundations of Computational Mathematics. 11 (2): 183-210, 2011.

K. Scheinberg, S. Ma and D. Goldfarb, “Sparse Inverse Covariance Selection via Alternating Linearization Methods”. Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS). 2010.

W. Liu, S. Ma, D. Tao, J. Liu and P. Liu, “Semi-Supervised Sparse Metric Learning using Alternating Linearization Optimization”. The Sixteenth ACM SIGKDD International Conference On Knowledge Discovery and Data Mining (SIGKDD). 2010.

S. Ma, W. Yin, Y. Zhang and A. Chakraborty, “An Efficient Algorithm for Compressed MR Imaging Using Total Variation and Wavelets”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2008.