Salar Fattahi

Assistant Professor

Location

2753 IOE

Primary Website

http://fattahi.engin.umich.edu/

Research Interests

Optimization, Machine Learning, Control, Power Systems

Biography

Salar Fattahi is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan. Salar Fattahi received his M.Sc. and Ph.D. degrees in Industrial Engineering and Operations Research from the University of California, Berkeley in 2016 and 2020, respectively. He also received a M.Sc. degree in Electrical Engineering from the Columbia University in 2015, and a B.Sc. degree in Electrical Engineering from the Sharif University of Technology in 2014. Salar Fattahi’s research lies at the intersection of optimization, machine learning, and control theory, with applications in large-scale and safety-critical networks.

Publications

• S. Fattahi and S. Sojoudi, “Exact Guarantees on the Absence of Spurious Local Minima for Rank-1 Non-negative Robust Principal Component Analysis”, Journal of Machine Learning Research, 2020.
• S. Fattahi, N. Matni, and S. Sojoudi, “Efficient Learning of Distributed Linear-Quadratic Regulators”, SIAM Journal on Control and Optimization, 2020
• S. Fattahi and S. Sojoudi, “Graphical Lasso and Thresholding: Equivalence and Closed-Form Solutions”, Journal of Machine Learning Research, 2020.
• S. Sojoudi, S. Fattahi and J. Lavaei, “Convexification of Generalized Network Flow Problem”, Mathematical Programming, 2019.
• S. Fattahi, J. Lavaei and A. Atamturk, “A Bound Strengthening Method for Optimal Transmission Switching in Power Systems with Fixed Connected Subgraph”, IEEE Transactions on Power Systems, 2019.
• S. Fattahi, G. Fazelnia, J. Lavaei, M. Arcak, “Transformation of Optimal Centralized Controllers Into Near-Global Static Distributed Controllers”, IEEE Transactions on Automatic Control, 2019
• R. Y. Zhang, S. Fattahi, S. Sojoudi, “Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion”, International Conference on Machine Learning, 2018.