Ming Yan

Ming Yan

Assistant Professor, Department of Computational Mathematics, Science and Engineering; Department of Mathematics
Room 1514, Engineering Building
  428 S. Shaw Ln.
 (517) 432-0401
 myan@msu.edu

About Me

Education:

B.S., 2005, mathematics, University of Science and Technology of China
M.S., 2008, mathematics, University of Science and Technology of China
Ph.D., 2012, mathematics, University of California, Los Angeles 

 

Bio:

Ming Yan's research lies on the intersection of parallel and distributed algorithms, signal and image processing, and inverse problems. He is particularly interested in developing optimization methods and their applications in sparse recovery and regularized inverse problems, variational methods for image processing, and parallel and distributed algorithms for solving big data problems.

Ming Yan received his Ph.D. degree in mathematics from the University of California, Los Angeles (UCLA). After completing his Ph.D., he was a postdoctoral fellow in the Department of Computational and Applied Mathematics at Rice University from 2012 to 2013, and then moved to UCLA as a postdoctoral scholar and an assistant adjunct professor.

Research Interests

•    Signal Processing

•    Image Processing

•    Convex Optimization

•    Nonconvex Optimization

•    Parallel and Distributed Computing

Publications
[1]
Y. Lou and M. Yan, Fast l1-l2 Minimization via a Proximal Operator, Journal of Scientific Computing, in press. (SN SharedIt Link) (pdf)
 
[2]
M. Yan and W. Yin, Self Equivalence of the Alternating Direction Method of Multipliers, in R. Glowinski, S. Osher and W. Yin (Eds.), Splitting Methods in Communication and Imaging, Science and Engineering (2017), New York, Springer, 165-194. (pdf)
 
[3]
I. Baytas, M. Yan, A. Jain and J. Zhou, Asynchronous Multi-Task Learning, In: Proceedings of IEEE International Conference on Data Mining (ICDM 2016), 11-20.
 
[4]
L. Chen, M. Yan, C. Qian, N. Xi, Z. Zhou, Y. Yang, B. Song and L. Dong, Nonconvex Compressive Video Sensing, Journal of Electronic Imaging, 25 (2016), 063003.
 
[5]
H. Zhang, M. Yan and W. Yin, One Condition for Solution Uniqueness and Robustness of Both l1-Synthesis and l1-Analysis Minimizations, Advances in Computational Mathematics, 42 (2016), 1381–1399. (SN SharedIt Link) (pdf).

...

Software Projects
PD3O: Primal Dual Algorithms with Three Operators
PD3O: Primal Dual Algorithms with Three Operators

Faculty member Ming Yan's Matlab code that reproduce the results for   

M. Yan, A New Primal-Dual Method for Minimizing the Sum of Three Functions with a Linear Operator, arXiv:1611.09805, 2016. (Slides) (Code)

NIDS: Decentralized Algorithm with Network-independent Step Sizes
NIDS: Decentralized Algorithm with Network-independent Step Sizes

Faculty member Ming Yan's Matlab code that reproduce the results for

Z. Li, W. Shi and M. Yan, A Decentralized Proximal-gradient Method with Network Independent Step-sizes and Separated Convergence Rates, arXiv:1704.07807, 2017. (Code)

ProxL1-L2: Proximal operators for L1-L2
ProxL1-L2: Proximal operators for L1-L2

Faculty member Ming Yan's Matlab code that reproduce the results for 

Y. Lou and M. Yan, Fast L1-L2 Minimization via a Proximal Operator, Journal of Scientific Computing, in press. (pdf)

TMAC: A Toolbox that Implements a Set of Modern Methods
TMAC: A Toolbox that Implements a Set of Modern Methods

Faculty member Ming Yan's C code that reproduce the results for 

Z. Peng, Y. Xu, M. Yan and W. Yin, ARock: an algorithmic framework for asynchronous parallel coordinate updates, SIAM Journal on Scientific Computing, 38 (2016), A2851-A2879. (pdf

Teaching

FS-17: CMSE 890 Section 001 Optimization

SS-18: CMSE 202 Computational Modeling Tools and Techniques

Click "Teaching" link to see past courses.