Maryam Fazel

Maryam Fazel is the Moorthy Family Inspiration Career Development Professor and Associate Professor of Electrical Engineering at the University of Washington, with adjunct appointments in Computer Science, Mathematics, and Statistics. She earned her PhD and MS in Electrical Engineering from Stanford University and a BS from Sharif University of Technology in Iran, followed by a postdoctoral fellowship at Caltech.

• Research Focus: Mathematical optimization, machine learning, control theory, and data science applications. Her work bridges convex optimization, reinforcement learning, and low-rank matrix methods.
• Honors: NSF CAREER Award (2009), UWEE Outstanding Teaching Award (2009), UAI Best Student Paper Award (2014), ScienceWatch Fast Breaking Paper (2011), and the Moorthy Family Professorship (2020).
• Leadership: Director of the Institute for Foundations of Data Science (IFDS), a multi-university NSF TRIPODS Phase II institute. Former co-director of Algorithmic Foundations of Data Science Institute (ADSI), Phase I.
• Students: Mentored award-winning advisees including Zhihan Xiong (Meta AI Fellowship), Avinandan Bose (Meta AI Fellowship), and K. Dvijotham (UAI Best Student Paper). Former group alumni include Brian Hutchinson (Full Professor at Western Washington University) and Ting Kei Pong (Full Professor at Hong Kong Polytechnic University).
• Publications: Recent work spans optimization in machine learning, control theory for nonlinear systems, and submodular maximization. Key themes include policy optimization, robustness in AI, and low-rank modeling.

She serves on editorial boards for the Journal of Machine Learning Research (JMLR) and the MOS-SIAM Book Series on Optimization, and co-organizes cross-campus seminars like the Distinguished Seminars in Optimization and Data. Her TRIPODS+X grants (2018) focus on neuro/geoscience applications via hack weeks, while her DARPA Lagrange grant (2018) explores control of uncertain dynamical systems.