Maryam serves as the Associate Chair for Research in ECE, and holds the Moorthy Family Professorship, as well as adjunct appointments in the departments of Mathematics, Statistics, and the Allen School of Computer Science and Engineering at UW. Her current research interests are Optimization in Machine Learning, Learning and Control, and Online/interactive learning.
Maryam is the director and the lead PI of the Institute for Foundations of Data Science (IFDS), a multi-university research institute aiming to develop theoretical foundations for machine learning and data science, funded by a $12.5 million NSF TRIPODS Phase II grant, launched in September 2020. Previously, she co-directed the Algorithmic Foundations of Data Science Institute (ADSI), a TRIPODS Phase I institute that was a pre-cursor to IFDS.
She is a recipient of the NSF CAREER Award (2009), UWEE Outstanding Teaching Award (2009), and UAI conference Best Student Paper Award (with her student K. Dvijotham, 2014), and coauthored a paper on low-rank matrix estimation selected by ScienceWatch as the “Fast Breaking Paper” in the area of Mathematics (August 2011). Prior to joining UW, she was a postdoctoral scholar at Caltech; and received her PhD in EE from Stanford University where she was advised by Prof. Stephen Boyd. She received her BS in EE from Sharif University of Technology in Iran.
Maryam serves on the Editorial board of the MOS-SIAM Book Series on Optimization, as a Founding Associate Editor of the SIAM Journal on Mathematics of Data Science (SIMODS), as a Steering Committee & Program Committee Member of ICCOPT 2022, and commonly as an Area Chair for Neural Information Processing Systems (NeurIPS).
Awards and Honors
In the News:
- Addressing fundamental challenges in data science: Q&A with Professor Maryam Fazel
- Article on IFDS, The Integrator Magazine, Dec 2021; pp. 28-30
- UW launches Institute for Foundations of Data Science
- Professor Maryam Fazel becomes first recipient of the Moorthy Family Inspiration Career Development Professorship
- Maryam Fazel to lead NSF TRIPODS+X in data science
- Three UW teams receive TRIPODS+X grants for research in data science
- Professors Fazel and Kakade co-lead NSF TRIPODS Award to advance state of the art in data science
- UW EE Wins $7.5 Million MURI Grant to Defend Advanced Cyberattacks
- Fazel Receives NSF 2009 CAREER Award
Honors, Awards, Keynotes:
- Invited as Keynote Speaker (one of three). Conf. on Learning Theory (COLT), July 2022, London, UK.
- NSF TRIPODS Phase II (2020-2025). Director and lead principal investigator of IFDS (Inst. For Foundations of Data Science), a multi-site Institute with partners at Universities of Wisconsin, California at Santa Cruz, and Chicago.
- Moorthy Family Professor in ECE, August 2020-present.
- NSF TRIPODS (2017-2020): Co-director (with S. Kakade) of the Phase I NSF Institute: Algorithmic Foundations for Data Science Institute (ADSI). Co-PIs: Dmitriy Drusvyatskiy, Zaid Harchaoui, Yin Tat Lee. NSF announcement and press, also here.
- Keynote Speaker, Intl. Symposium on Mathematical Programming (ISMP), July 2018, Bordeaux, France.
- Simons Institute Open Lecture (one of three public lectures per semester, organized by the Simons Institute, UC Berkeley), November 2017, Berkeley, CA.
- Plenary Speaker, SIAM Applied Linear Algebra Conference, October 26-30 2015, Atlanta, GA.
- Plenary Speaker, SPARS Conference 2015 (Signal Processing with Adaptive Sparse Representations), July 2015, Cambridge, UK.
- Best Student Paper Award, Uncertainty in Artificial Intelligence (UAI) 2014 (with K. Dvijotham and E. Todorov).
- Plenary Speaker, 2013 International Linear Algebra Society Conference, June 3-7 2013, Providence, RI.
- Coauthored paper selected by ScienceWatch as the “Fast Breaking Paper” in the area of Mathematics, August 2011.
- NSF CAREER Award, National Science Foundation, 2009.
- Outstanding Teaching Award, 2009, University of Washington Electrical Engineering Dept. (Annual department-level teaching award, nominated by students)
- EE Dept. Award for ranking first among all freshmen, 1990, Electrical Engineering Dept., Sharif University of Technology, Tehran, Iran
- Ranked first in the country in the Nationwide Entrance Examination to all Iranian Universities (among ~1 million applicants), 1990, Iran. Presidential Letter of Honor awarded by the Iranian President, 1990, Iran.
Some of our projects (also see recent publications):
- Learning in Decision-dependent Games; Multiplayer Performative Prediction
- Learning and Optimal Experimental Design with a Budget (NSF CIF) (2020-2024)
- Institute for Foundations of Data Science (NSF TRIPODS Phase II Institute) (2020-2025)
- Safe Imitation Learning for Robotics. NSF TRIPODS+X. PI: Hachaoui (Stats) Co-PIs: Fazel, Srinivasa (CSE), Kakade (CSE)
- Algorithmic Foundations of Data Science Institute (NSF TRIPODS Phase I) (2017-2020)
- Control and Learning of Dynamical Systems: Optimization, Sampling, and Regret (DARPA Lagrange program) (2018-2019)
- ADAPT: Analytical Framework for Actionable Defense against Advanced Persistent Threats (2016-2021)
Please see my Google Scholar page for a complete list.
- L. Ding, D. Drusvyatskiy, M. Fazel, Flat minima generalize for low-rank matrix recovery, arXiv preprint arXiv:2203.03756.
- A. Narang, E. Faulkner, D. Drusvyatskiy, M. Fazel, L.J. Ratliff, Multiplayer Performative Prediction: Learning in Decision-Dependent Games, arXiv preprint arXiv:2201.03398.
- A. Narang, E. Faulkner, D. Drusvyatskiy, M. Fazel, L.J. Ratliff, Learning in Stochastic Monotone Games with Decision-Dependent Data, AI and Statistics Conference (AISTATS), March 2022.
- M. Ray, L.J. Ratliff, D. Drusvyatskiy, M. Fazel, Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments, AAAI Confenece on Artificial Intelligence, Feb. 2022.
- M. Fazel, Y.T. Lee, S. Padmanabhan, A. Sidford, Computing Lewis Weights to High Precision, Proc. Symp. on Discrete Algorithms (SODA), Jan 2022.
- Y. Sun, M. Fazel, Learning Optimal Controllers by Policy Gradient: Global Optimality via Convex Parameterization, Proc. of IEEE Conf. on Decision and Control (CDC), Dec 2021.
- Y. Sun, A. Narang, H.I. Gulluk, S. Oymak, M. Fazel, Towards Sample-Efficient Overparameterized Meta-Learning, Proc. Neural Information Processing Systems(NeurIPS), Dec 2021.
- R. Camilleri*, Z. Xiong*, M. Fazel, L. Jain, K. Jamieson, Selective Sampling for Online Best Arm Identification, Proc. Neural Information Processing Systems (NeurIPS), Dec. 2021.
- O.Sadeghi, M. Fazel, Differentially Private Monotone Submodular Maximization Under Matroid and Knapsack Constraints, Proc. AI and Statistics Conference (AISTATS), Apr 2021.
- J. Diakonikolas, M. Fazel, L. Orecchia, Fair Packing and Covering on a Relative Scale, J. on Optimization, 2020. J. on Optimization, 2020.SIAM J. on Optimization, 2020.
- O. Sadeghi, P. Raut, M. Fazel, A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints, Proc. Neural Information Processing Systems (NeurIPS), Dec 2020.
- Y. Sun, M. Fazel, Escaping from Saddle Points on Riemannian Manifolds, arxiv:1906.07355. Neural Information Processing Systems (NeurIPS) 2019, Vancouver, BC, Canada.
- R. Eghbali, J. Saunderson, M. Fazel, Competitive Online Algorithms for Resource Allocation over the Positive Semidefinite Cone, Mathematical Programming Series B, Vol. 170, Issue 1, pp 267-292, July 2018.
- M. Fazel, R. Ge, S. Kakade, M. Mesbahi, Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator, Proceedings of Intl. conference on Machine Learning (ICML), Stockholm, Sweden, July 2018.
- D. Drusvyatskiy, M. Fazel, S. Roy, An optimal first order method based on optimal quadratic averaging, arXiv:1604.06543. SIAM J. on Optimization, 28-1 (2018), 251271.
- A. Jalali, M. Fazel, L. Xiao, Variational Gram Functions: Convex Analysis and Optimization, arXiv:1507.04734. SIAM J. on Optimization, 27-4 (2017), pp. 2634-2661.
- A. Jalalli, Q. Han, J. Dumitriu, M. Fazel, Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models, Proc. NIPS 2016 [Arxiv version]
I am fortunate to currently advise or co-advise these great students:
- Yue Sun (ECE)
- Omid Sadeghi (ECE)
- Zhihan Xiong (CSE)
- Adhyyan Narang (ECE) (co-advised with L. Ratliff)
- Evan Faulkner (ECE) (co-advised with L. Ratliff)
As well as fantastic IFDS postdocs:
- Ting Kei Pong, PhD in Mathematics, June 2011. Co-advised with Prof. Paul Tseng. Currently: Associate Professor, Dept. of Applied Mathematics, Hong Kong Polytechnic University.
- Brian Hutchinson, PhD in EE, August 2013. Co-advised with Prof. Mari Ostendorf. Currently: Associate Professor, Department of Computer Science, Western Washington University.
- Dvijotham Krishnamurthy, PhD in Computer Science and Engineering, June 2014. Co-advised with Prof. Emo Todorov. Currently: Research Scientist at Google Brain.
- Palma London, BS in EE and Math, June 2014. Currently: Postdoc at Cornell.
- Karthik Mohan, PhD in EE, December 2014. Currently: Applied Scientist at Facebook/Meta.
- James Saunderson, Postdoc (joint position with Caltech), Sep 2015-June 2016. Currently: Associate Professor, Dept of Electrical and Computer Systems Engineering, Monash Univ, Australia.
- Amin Jalali, PhD in EE, August 2016. Postdoc at Univ. of Wisconsin; Researcher at Technicolor Research.
- Dennis Meng, PhD in EE, May 2017. Kernel Labs Inc.
- Reza Eghbali, PhD in EE, August 2017. Currently: Data Science Health Innovation Fellow at University of California, San Francisco & University of California, Berkeley.
- Tyler Johnson, PhD in EE, October 2018. Co-advised with Prof. Carlos Guestrin. Currently: Machine Learning Research Engineer, Apple Inc.
- Jingjing Bu, PhD in EE, 2020. Co-advised with Prof. Mehran Mesbahi. Currently: Software engineer at TikTok Inc.
- Mitas Ray, MS in EE, 2021. Co-advised with Prof. Lillian Ratliff. Currently: ML Software engineer at FICC.ai
- March 2022: IFDS has partnered with the UW Taskar Center to co-organize AI4All@UW 2022, Aug 8-20, 2022, held virtually.
- Feb 2022: IFDS is co-organizing the PIMS-IFDS-NSF Summer School on Optimal Transport, June 19-July 1st, 2022, University of Washington Campus.
- Sep 2020: We won a NSF TRIPODS Phase II Award and launched the Institute for Foundations of Data Science (IFDS), with partners U Wisconsin, UC Santa Cruz, and U Chicago!
- Aug 2020: I am honored to be named as the inaugural Moorthy Family Inspiration Career Development Professor.
- Aug 2019: I am co-organizing the ADSI Workshop on Algorithmic Foundations of Learning and Control. This event is cosponsored by our TRIPODS partner institute, IFDS at U Wisconsin.
- Aug 2019: I am co-organizing the 2019 ADSI Summer School on Foundations of Data Science. This event is cosponsored by our TRIPODS partner institute, IFDS at U Wisconsin.
- Sep 2018: ADSI recieved three new NSF TRIPODS+X grants! NSF press release
- I am the PI on "TRIPODS+X:EDU: Foundational Training Neuroscience and Geoscience via Hack Weeks," and a co-PI on "TRIPODS+X:RES: Safe Imitation Learning for Robotics" (with PI Zaid Harchaoui).
- UW News press release
- Aug 2018: ADSI co-organized the Workshop on Nonconvex Formulations and Algorithms in Data Science, with our TRIPODS partner institute IFDS at the University of Wisconsin, Madison.Videos of talks are available here.
- July 2018: ADSI co-organized the Summer School on Fundamentals of Data Analysis, with our TRIPODS partner institute IFDS at the University of Wisconsin, Madison. Videos of lectures are available here.
- Apr 2018: I am the PI on a new grant from the DARPA Lagrange program (an exciting recently-established program on optimization) on "Control and Learning of Uncertain Dynamical Systems," with co-PIs Sham Kakade and Mehran Mesbahi.
- Jan 2018: ADSI launched a new blog highlighting research breakthroughs by its members and affiliates, check it out!
- Sep 2017: We founded ADSI: Algorithmic Foundations of Data Science Institute, our NSF TRIPODS Institute. I am the co-director (with Sham Kakade). Other members of the core PI team are: Dmitriy Drusvyatskiy, Zaid Harchaoui, and Yin Tat Lee.
- Aug 2017: We received an NSF TRIPODS Award! This funds the Phase I of an NSF Institute at UW aiming to build a Theoretical Foundation for Data Science by bridging Mathematics, Statistics, and Theoretical Computer Science.
- NSF announcement, UW press here, and here.