Michael Chen

Michael Chen
Associate Professor
Department of Mathematics and Statistics
Faculty of Science
York University
4700 Keele Street, TEL Building Room 2034
Toronto, Canada
chensy@mathstat.yorku.ca

Page Index

Education
Research and Teaching Interests
Projects
Selected Publications
Software
Courses
Interesting Work from Students

Education

BSC, Econometrics, Jilin University
MS, Operations Research, University of British Columbia
PhD, Industrial Engineering and Management Science, Northwestern University
PostDoc, Mathematics, IBM T.J. Watson Research Center

Research and Teaching Interests

Deep Learning
Operations Research
Optimization
Professor Chen studies optimization models and algorithms, particularly the optimization under uncertainty (integrated risk and decision modelling, sparse grid scenario generation) , interior point method (barrier functions, warm start strategies, complexity analysis) and large scale optimization of the deep learning neural network (recurrent neural network, Monte Carlo Tree search, tensor analysis).
Professor Chen teaches graduate courses on stochastic programming and nonlinear optimization, as well as undergraduate courses on linear optimization, combinatorial optimization, operations research models, linear algebra and advanced calculus.

Seminars

9:30am - 11:00am, July 10th 2019, N Ross 627, Prof. Michael Chen, "A Collaborative Filtering based Model for Recommending Graduate Schools"

Projects

PI, IRAP Project "Dynamic Routing Algorithms in Building Management"

PI, Co-PI Professor Hongmei Zhu, Mitacs Project "Optimization of Savings and Retirement for Canadians"

PI, Co-PI professor Hongmei Zhu, Mitacs Project "Learning Jungle AI Recommender System for Enhanced Education"

PI on NSERC Discovery Grant "Stochastic Optimization of Renewal Energy"

PI on NSERC Engage Grant "Deep Learning Method for Drinking Water Pollutant Detection"

PI on OCE VIP I Grant "Deep Learning Method for University Timetabling Problem"

PI on IRAP AI Grant "Deep Learning Method for Workplace Safety"

PI on MITACS Grant "Recommendation System for Online Learning"

PI on Environment Canada Project "Temporal and Spacial Correlation of PM2.5"

Member of NSERC Sanofi Industrial Research Chair Jianghong Wu Grant "Math for Infectious Diseases"

Analyst in Teekay Shipping "Operational Risk Mitigation"

Selected Publications
Yousef Akhavan, Shengyua Chen, Dong Liang. Second Order in Time and Space Corrected Explicit-Implicit Domain Decomposition Scheme for Convection-Diffusion Equations, Journal of Computational and Applied Mathematics, accepted, 2019.
Masoud Ataei, Shengyuan Chen, Reza Peyghami. Time homogeneous Top-K Ranking Using Tensor Decomposition, Optimization Method and Software, accepted, 2018.
Sajad Fathi-Hafshejani, Hossein Mansouri, M. Reza Peyghami, Shengyuan Chen. Primal-dual interior-point method for linear optimization based on a kernel function with trigonometric growth term, Optimization, 67:10, 1605-1630, 2018.
M. Reza Peyghami, Kevin Yang, Shengyuan Chen, Zijiang Yang, Masoud Ataei. Accelerated Gradient and Block-Wise Gradient Methods for Big Data Factorization. Canadian AI 2018, LNAI 10832, 2018.
S. Fathi-Hafshejani, Alireza Fakharzadeh Jahromi, Mohammad Reza Peyghami, Shengyuan Chen: Complexity of Interior Point Methods for a Class of Linear Complementarity Problems Using a Kernel Function with Trigonometric Growth Term. J. Optimization Theory and Applications, 178(3): 935-949, 2018.
M. Reza Peyghami , S. Fathi-Hafshejani , Shengyuan Chen. A primal-dual interior-point method for semidefinite optimization based on a class of trigonometric barrier functions. OR Letters, 44(3):319-323, 2016.
M. Zhu, S. Chen, M. Christal. Modeling the impacts of uncertain carbon tax policy on maritime fleet mix strategy and carbon mitigation. Transport, 33(3), 707-717, 2016.
Z. Danling, H. Xi, Shengyuan Chen, and W. Jianhong. The Diffusion of Online Content Based On SEIR Model. Journal of Biomathematics, issue 4, 2016.
Shengyuan Chen, S. Mehrotra, D. Papp. Scenario Generation for Stochastic Optimization Problems via the Sparse Grid Method. Computational Optimization and Applications, 62(3):669-692, 2015.
Fei Yang and Shengyuan Chen. Deep Cutting Plane Inequalities for Stochastic Non-Preemptive Single Machine Scheduling Problem. American Journal of Operations Research, 5:69-76. doi: 10.4236/ajor.2015.52006, 2015.
Y. Akhavan, Shengyuan Chen and D. Liang. Global Optimization of Total Power Generated from Wind Farm, Proceeding of 2014 International Conference on Power System Technology, 2945-2950, 2014.
Shengyuan Chen, X. Qiu, L. Zhang, F. Yang, P. Blanchard. Method Development Estimating Ambient Oxidized Mercury Concentration from Monitored Mercury Wet Deposition. Atmospheric Chemistry and Physics, 13:11287-11293, 2013.
Shengyuan Chen and X. Wang. A Derivative-free Global Optimization Algorithm by Sparse Grid Integration. American Journal of Computational Mathematics, 3(1):16-26, 2013. DOI: 10.4236/ajcm.2013.31003, 2013.
Shengyuan Chen and X. Wang. Approximate Risk Analysis using Numerical Integration On Sparse Grids. Journal of Mathematical and Computational Science,3(4):929-944, 2013.
Shengyuan Chen and S. Mehrotra. Self-concordance and Decomposition Based Interior Point Methods for Two-stage Stochastic Convex Optimization Problem. SIAM Journal of Optimization, 4(21):1667-1687, 2011.
Shengyuan Chen and M. Zhao. Optimal Thermal Generator Portfolio in a Day-ahead Market under Uncertain Carbon Tax Policy. American Journal of Operations Research, 1(4):268-276, 2011.

Software

The following research code are available upon request:

Sparse Grid Generator in ANSI C and AMPL
Linear Programming Solver in ANSI C
Mixed Integer Nonlinear Programming Solver in Python

Current Course Taught

MATH 3171 Linear Optimization
MATH 3172 Combinatorial Optimization
MATH 4171 Nonlinear Optimization
MATH 4172 Operations Research Models
MATH 6901 Stochastic Optimization
MATH 6902 Modern Optimization

Short Courses
Prof. Chen teaches short courses on the following topics:

Optimization models in finance, education, and scheduling
Optimization techniques for deep learning
Introduction to optimization modeling languages and solvers
Introduction to artificial intelligence
Introduction to deep learning
Introduction to data science
Introduction to Python

Interesting Work from Students
2019, Zhao Lian, Ruimeng Yang, Jingyi Liu, Support Vector Machine (SVM), MATH 6902 Modern Optimization
2019, Kosal Chhin, Convexity and Unniqueness, MATH 6902 Modern Optimization

last update 2/19/2019