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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.

 

Contact:

Email: fattahi@umich.edu
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.


News:

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 gave a talk on “learning and control of linear dynamical systems in high dimensions” at the University of Michigan Controls Seminar. The presentation is available online.

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: Here is a (very) short overview of my research, presented at MIDAS Faculty Research Pitch.

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.