I am an assistant professor in the Department of Industrial and Operations Engineering at the University of Michigan. I am also affiliated with Michigan Institute for Computational Discovery and Engineering (MICDE), and Michigan Institute for Data Science (MIDAS).
My research focuses on developing large-scale computational and statistical methods for societal problems. In particular, I develop new techniques in optimization, machine learning, and control to solve massive-scale problems that stem from real-life applications, with a special focus on networked and interconnected systems.
I received my M.Sc. and Ph.D. degrees in Industrial Engineering and Operations Research from the University of California, Berkeley. I also received a M.Sc. degree in Electrical Engineering from the Columbia University, and a B.Sc. degree in Electrical Engineering from the Sharif University of Technology.
Phone: (734) 763-9744
Prospective PhD students: I will be recruiting PhD students to join my research group in Fall 2021. If interested, please send me an email with your CV, and apply to our PhD program.
March 2021: Our paper Learning Partially Observed Linear Dynamical Systems from Logarithmic Number of Samples to appear in 3rd Annual Learning for Dynamics & Control Conference.
February 2021: New paper on robust matrix recovery: Implicit Regularization of Sub-gradient Method in Robust Matrix Recovery: Don’t be Afraid of Outliers. Joint work with my student Jianhao Ma.
February 2021: New paper on the inference of time-varying Markov Random Fields: Scalable Inference of Sparsely-changing Markov Random Fields with Strong Statistical Guarantees. Joint work with Andres Gomez.
November 2020: Our paper Smoothing Property of Load Variation Promotes Finding Global Solutions of Time-Varying Optimal Power Flow has been conditionally accepted to appear in IEEE Transactions on Control of Network Systems.
November 2020: A revised version of our paper On the Absence of Spurious Local Trajectories in Time-varying Nonconvex Optimization is available online.
October 2020: New paper on the efficient learning of partially-observed linear dynamical systems: Learning Partially Observed Linear Dynamical Systems from Logarithmic Number of Samples.
October 2020: I will be organizing a session on “Recent Advances in Learning, Optimization, and Control” at INFORMS Annual Meeting. Check out the schedule here!
September 2020: Our paper Smoothing Property of Load Variation Promotes Finding Global Solutions of Time-Varying Optimal Power Flow has received the 2020 INFORMS ENRE Best Student Paper Award.
August 2020: Our paper Absence of Spurious Local Trajectories in Time-Varying Optimization: A Control-Theoretic Perspective has received an Outstanding Student Paper Award of the IEEE Conference on Control Technology and Applications (CCTA).
July 2020: Our paper ‘‘Efficient Learning of Distributed Linear-Quadratic Controllers’’ to appear in SIAM Journal on Control and Optimization.
June 2020: Our paper ‘‘Load Variation Enables Escaping Poor Solutions of Time-Varying Optimal Power Flow’’ has received the Best-of-the-Best Conference Paper Award of the 2020 Power & Energy Society General Meeting.
April 2020: Interview with UC Berkeley IEOR.
March 2020: Our paper ‘‘Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysis’’ to appear in Journal of Machine Learning Research (JMLR), 2020.