Maryam Fazel

Papers

Total Citations

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About

Maryam Fazel is a leading researcher in optimization, machine learning, and control theory, with a particular focus on low-rank matrix recovery, convex optimization, and large-scale data analysis. Her most influential contributions include pioneering work on the nuclear norm heuristic for rank minimization, which has become a cornerstone in compressed sensing, collaborative filtering, and system identification. This foundational research has garnered thousands of citations, reflecting its profound impact on both theoretical and applied domains. Fazel is also widely recognized for her work on online optimization and network formation algorithms, including her 2016 paper on "Online algorithms for network formation," which addresses strategic decision-making in dynamic graph environments. Beyond her research, she has received prestigious honors such as the NSF CAREER Award and the IEEE Signal Processing Society Best Paper Award. Her clear, rigorous approach to complex problems makes her work essential reading for students and researchers in optimization, signal processing, and machine learning.