Eric Mazumdar

Assistant Professor in Computing and Mathematical Sciences & Economics

I am an Assistant Professor in Computing and Mathematical Sciences and Economics at Caltech. I obtained my Ph.D in Electrical Engineering and Computer Science at UC Berkeley, co-advised by Michael Jordan and Shankar Sastry.

My research interests lie at the intersection of machine learning and economics. I am broadly interested in developing the tools and understanding necessary to confidently deploy machine learning algorithms into societal systems. This requires understanding the theoretical underpinnings of learning algorithms in uncertain, dynamic environments where they interact with other strategic agents, humans, and algorithms. Practically, I apply my work to problems in intelligent infrastructure, online markets, e-commerce, and the delivery of healthcare.

I use tools and ideas from statistical machine learning, optimization, stochastic control, dynamical systems, and game theory. Some of the topics addressed by my recent work include strategic classification, learning behavioral models of human decision-making from data, min-max optimization, learning in games, multi-agent reinforcement learning, distributionally robust learning, and learning for control.

In Spring 2022, I will be a Simons-Berkeley Research Fellow for the program on Learning in Games at the Simons Institute for Theoretical Computer Science. Prior to Berkeley, I received an SB in Electrical Engineering and Computer Science at Massachusetts Institute of Technology (MIT) , where I had the opportunity to work with in the Laboratory for Multiscale Regenerative Technologies as well as in the MIT Computational Biology Group in CSAIL.

Contact

You can contact me by email at: lastname at caltech dot edu

Publications

Preprints/Under review

Who Leads and Who Follows in Strategic Classification?
Tijana Zrnic*, Eric Mazumdar*, S. Shankar Sastry, Michael I. Jordan
Preprint, 2021 [PDF]
(* denotes equal contribution)

Fast Distributionally Robust Learning via Min-Max Optimization
Yaodong Yu*, Tianyi Lin*, Eric Mazumdar*, Michael I. Jordan
Preprint, 2021 [PDF]
(* denotes equal contribution)

Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization
Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, S. Shankar Sastry, Lillian J. Ratliff
Preprint, 2021 [PDF]

Journal Articles

On Finding Nash Equilibria (and only Nash equilibria) in Zero-Sum Continuous Games
Eric Mazumdar, Michael I. Jordan, S. Shankar Sastry
Journal of Machine Learning Research (JMLR), under submission [PDF]

On Gradient-Based Learning in Continuous Games
Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry
SIAM Journal on Mathematics for Data Science (SIMODS), 2020 [PDF]

Inverse Risk-Sensitive Reinforcement Learning via Gradient Methods
Lillian Ratliff, Eric Mazumdar
IEEE Transactions on Automatic Control (TAC), 2020 [PDF]

Mathematical Framework for Activity-Based Cancer Biomarkers
Gabriel Kwong, Jaideep Dudani, Emmanuel Carrodeguas, Eric Mazumdar, Miriam Zekvat, Sangeeta N. Bhatia
Proceeding of the National Academy of Science (PNAS), 2015 [PDF]

Peer-Reviewed Conference Articles

High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems
Eric Mazumdar, Tyler Westenbroek, Michael I. Jordan, S. Shankar Sastry
IEEE Conference on Decision and Control (CDC), 2020 [PDF]

Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning
Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry
IEEE Conference on Decision and Control (CDC), 2020 [PDF]

Expert Selection in High Dimensional Markov Decision Processes
Vicenc Rubies Royo, Eric Mazumdar, Roy Dong, Claire J. Tomlin, S. Shankar Sastry
IEEE Conference on Decision and Control (CDC), 2020 [PDF]

On Thompson Sampling with Langevin Algorithms
Eric Mazumdar*, Aldo Pacchiano*, Yi-an Ma*, Peter L. Bartlett, Michael I. Jordan
International Conference on Machine Learning (ICML), 2020 [PDF]
(* denotes equal contribution)

Feedback Linearization for Unknown Systems via Reinforcement Learning
Tyler Westenbroek*,David Fridovitch-Keil*, Eric Mazumdar*, Shreyas Arora, Valmik Prabhu, Claire Tomlin, S. Shankar Sastry
International Conference on Robotics and Automation (ICRA), 2020 [PDF]
(* denotes equal contribution)

Policy Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games
Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2020 [PDF]

Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in ‘Almost All’ Zero-Sum Continuous Games
Eric Mazumdar and Lillian Ratliff
IEEE Conference on Decision and Control (CDC), 2019 [PDF]

Convergence Analysis of Gradient-Based Learning in Continuous Games
Benjamin, Chasnov, Lillian Ratliff, Eric Mazumdar, Samuel A. Burden
Conference on Uncertainty in Artificial Intelligence (UAI), 2019 [PDF]

On the Analysis of Cyclic Drug Schedules for Cancer Treatment using Switched Dynamical Systems
Margaret Chapman, Eric Mazumdar, Ellen Langer, Rosalie Sears, Claire Tomlin
IEEE Conference on Decision and Control (CDC), 2018 [PDF]

Gradient-based Inverse Risk-Sensitive Reinforcement Learning
Eric Mazumdar, Lillian Ratliff, S. Shankar Sastry
IEEE Conference on Decision and Control (CDC), 2017 [PDF]

To Observe or Not to Observe: Queuing Game Framework for Urban Parking
Lillian Ratliff, Chase Dowling, Eric Mazumdar, Baosen Zhang
IEEE Conference on Decision and Control (CDC), 2016 [PDF]

Understanding the Impact of Parking on Urban Mobility via Routing Games on Queue–Flow Networks
Daniel Calderone, Eric Mazumdar, Lillian Ratliff, S. Shankar Sastry
IEEE Conference on Decision and Control (CDC), 2016 [PDF]

Workshop Publications

Policy Gradient Has No Convergence Guarantees in Linear Quadratic Dynamic Games
Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry
Workshop on Smooth Games and Optimization
NeurIPS, 2019 [PDF]

Learning Feedback Linearization by Model-Free Reinforcement Learning
Tyler Westenbroek*, David Fridovitch-Keil*, Eric Mazumdar*, Claire Tomlin, S. Shankar Sastry
Workshop on Generative Modeling/Model-based Reasoning
International Conference on Machine Learning (ICML), 2019
(* denotes equal contribution)

Students

Thank you for your interest in my group. I will keep this page up-to-date with available positions.

Existing and Incoming Caltech CMS and HSS Graduate Students

If you are an existing or incoming Caltech graduate student in CMS or HSS and you are interested in working with me, please feel free to reach out via email and we can schedule a time to meet.

Prospective Graduate Students

I will be accepting graduate students applying during the 2021/22 academic cycle. If you are interested in my research, you can list me as a faculty of interest in your application to CMS or CDS.

Prospective Postdoctoral Fellows

I will be recruiting Postdoctoral fellows in CMS or HSS to begin in Summer 2022. More details will be posted here in the coming months, however, if there is an exceptionally good research fit with my group, please feel free to send an email with your CV/resume and a short paragraph highlighting research fit.

Teaching

Caltech Courses

TBA



UC Berkeley Courses

DS 102 - Data, Inference, and Decisions

Instructors: Fernando Perez, Michael Jordan

Data Science 102 is a capstone class for the Data Science Major at Berkeley. Its goal is to develop the probabilistic foundations of inference in data science and building a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications.

We covered topics including frequentist and Bayesian decision-making, permutation testing, false discovery rate control, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, multi-armed bandits, Thompson sampling, optimal control, Q-learning, differential privacy, and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.

I participated in the development of this course starting back in Spring 2019, and was a Graduate Student Instructor for the first semester of this course in Fall 2019. This included creating course content (including homeworks, exams, discussions, coding projects, and labs), as well as leading both a discussion and lab section.

IEOR 290 - Data Analytics and the IoT: Machine Learning for Operations with Human Data Sources

Instructors: Roy Dong, Shankar Sastry

IEOR 290 was an introductory graduate-level class in the Industrial Engineering and Operations Research department on machine learning on data from human data sources and reasoning about the human, social, and ethical implications of making decisions based on such analyses. This course covered the theoretical tools for the analysis of data and human agents in cyber-physical systems. Concepts will included optimization, game theory, differential privacy, behavioral methods, statistical estimation, and utility function learning, with a focus on applications in a variety of Internet of Things systems, such as the energy grid, new transportation services, and database privacy. Throughout, we emphasized the underlying mathematical tools required to understand the current research in each of these fields. I was a Graduate Student Instructor for this class and also participated in the development of this course.

Talks

Upcoming

Learning in the Presence of Strategic Agents: Dynamics, Equilibria, and Convergence
Heller Colloquium, October 2021, (invited).
Pasadena, CA

Past

Learning with Strategic Agents: Dynamics, Equilibria, and Convergence
Center for Human-Compatible AI, July 2021, (invited).
Virtual

Approximate Thompson Sampling with Langevin Algorithms
RISE Retreat, May 2020 (invited).
Virtual

Designing Learning Algorithms for Competitive Settings
Center for Human-Compatible AI, March 2020 (invited).
Berkeley, CA

On the Analysis and Design of Gradient-Based Learning Algorithms in Games
ONR MURI Review Workshop, April 2019.
George Mason University, VA

Towards Better Gradient-Based Learning Algorithms in Games
Machine Learning Seminar, November 2018 (invited).
University of Washington, WA

Fundamental Issues with Gradient-Play in Games
ONR MURI Review Workshop, April 2018.
Seattle, WA

Learning Agent Preferences via Inverse Risk-Sensitive Reinforcement Learning
NSF Review Workshop, January 2017.
Arlington, VA

Data Analytics for Parking Management and Congestion Reduction
NSF Review Workshop, November 2015.
Arlington, VA