Aritra Mitra

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Postdoctoral Researcher
Department of Electrical and Systems Engineering
University of Pennsylvania
Philadelphia, PA

amitra20 AT seas [DOT] upenn [DOT] edu

About me

I am a Postdoctoral Researcher in the Department of Electrical and Systems Engineering, University of Pennsylvania, where I am working with Professor George Pappas and Professor Hamed Hassani. I received my Ph.D. degree in August 2020 from the School of Electrical and Computer Engineering, Purdue University, where I was advised by Professor Shreyas Sundaram.

Prior to joining Purdue, I received my M.Tech degree from the Indian Institute of Technology, Kanpur in 2015, and my B.E. degree from Jadavpur University, Kolkata in 2013, both in Electrical Engineering.

[ CV ] [ Google Scholar ]

I will be joining the Department of Electrical and Computer Engineering at North Carolina State University as an Assistant Professor from January 2023. I am looking for motivated PhD students to work on theoretical problems related to control, optimization, and sequential decision-making under uncertainty (e.g., bandits and reinforcement learning), with a particular focus on multi-agent systems. If you are interested in working with me, please feel free to send me an email.

Research Interests

The broad goal of my research is to enable reliable and efficient learning and decision-making in large-scale distributed systems, while contending with modern challenges related to computation, communication, and adversarial robustness. To meet this goal, my research draws on ideas and tools from Control and Optimization Theory, Statistical Signal Processing, Machine Learning, and Network Science. While my work is theoretically grounded, the theory that I develop is motivated by a variety of application domains: multi-robot systems, wireless sensor networks, federated learning, edge-computing, estimation and control in smart cities and power-grids, and learning in social networks.

My recent postdoctoral work focuses on two main themes: (i) Designing fast and communication-efficient algorithms for the emerging paradigm of Federated Learning; and (ii) Investigating the performance bounds of sequential decision-making problems (e.g., bandits and reinforcement learning) in multi-agent settings. Prior to that, my dissertation made fundamental algorithmic and theoretical contributions to the study of state estimation and statistical inference over networks, subject to worst-case adversarial attacks on certain components. A list of keywords that succinctly describe my past and current research interests is as follows.

  • Multi-Agent Reinforcement Learning and Bandits

  • Optimization and Statistical Inference

  • Federated Learning

  • Learning, Control, and Estimation over Networks

  • Resilience and Security

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