Rate My Professor Maryam Fazel

Maryam Fazel is the Moorthy Family Professor of Electrical and Computer Engineering in the Engineering faculty at the University of Washington, holding adjunct appointments in the departments of Mathematics, Statistics, and the Paul G. Allen School of Computer Science and Engineering. She received her BS in Electrical Engineering from Sharif University of Technology in Iran in 1990, where she ranked first among all freshmen and topped the nationwide entrance examination among approximately one million applicants, earning a Presidential Letter of Honor. Fazel obtained her MS and PhD in Electrical Engineering from Stanford University, advised by Professor Stephen Boyd, and completed a postdoctoral fellowship at the California Institute of Technology. She joined the University of Washington faculty following her time at Caltech as a research scientist and has held the Moorthy Family Inspiration Career Development Professorship since August 2020. Additionally, she serves part-time as an Amazon Scholar since October 2025. Fazel leads significant initiatives, including as director and lead principal investigator of the Institute for Foundations of Data Science (IFDS), a multi-university research institute funded by a $12.5 million NSF TRIPODS Phase II grant launched in September 2020. She previously co-directed the Algorithmic Foundations of Data Science Institute (ADSI) under TRIPODS Phase I from 2017 to 2020.

Her research focuses on optimization in machine learning and AI, deep learning theory, learning and control, and reinforcement learning. Fazel has secured major grants as principal investigator or co-principal investigator, including DARPA's Lagrange program for control and learning of uncertain dynamical systems (2018), NSF CIF grants for mathematical foundations of deep reinforcement learning (2022–2026) and learning with budget constraints (2020–2024), and NSF AF grant for machine learning markets (2023–2026). Notable awards include the 2025 Farkas Prize from the INFORMS Optimization Society, NSF CAREER Award (2009), University of Washington Electrical Engineering Outstanding Teaching Award (2009), and UAI Best Student Paper Award (2014). Key publications encompass 'Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback' (arXiv 2025), 'Flat Minima Generalize for Low-Rank Matrix Recovery' (IMA Journal of Information and Inference, 2024), 'Global Convergence of Policy Gradient for Linear Quadratic Regulator' (2018), and 'Hankel Matrix Rank Minimization with Applications to System Identification and Realization' (2011), which was recognized as a Fast Breaking Paper by ScienceWatch. She contributes editorially as Founding Associate Editor of SIAM Journal on Mathematics of Data Science, Program Chair of ICML 2025, Action Editor for Journal of Machine Learning Research, and on the MOS-SIAM Optimization Book Series board. Her influence is evident in mentoring alumni to positions at Google DeepMind, universities, and industry, and organizing cross-campus seminars in optimization and data science.