Optimization and Machine Learning
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 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 and machine learning, with applications in complex networks.
- P. Liu, S. Fattahi, A. Gomez, and S. Küçükyavuz, “A Graph-based Decomposition Method for Convex Quadratic Optimization with Indicators”, Mathematical Programming, 2022
- S. Fattahi and A. Gomez, “Scalable Inference of Sparsely-changing Gaussian Markov Random Fields”, Conference on Neural Information Processing Systems (NeurIPS), 2021
- S. Fattahi, Learning Partially Observed Linear Dynamical Systems from Logarithmic Number of Samples, Learning for Dynamics & Control Conference, 2021
- G. Zhang, S. Fattahi, R.Y. Zhang, “Preconditioned Gradient Descent for Over-parameterized Nonconvex Matrix Factorization”, Conference on Neural Information Processing Systems (NeurIPS), 2021,
- 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.