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Aritra Mitra

Assistant Professor
Department of Electrical and Computer Engineering
North Carolina State University

Previously, I was a Postdoctoral Researcher in the Department of Electrical and Systems Engineering, University of Pennsylvania from August 2020 to December 2022, where I worked with Professor George Pappas and Professor Hamed Hassani. I received my Ph.D. in 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.

I am looking for motivated PhD students to work with me on theoretical problems related to control, optimization, learning, 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 postdoctoral work focused 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

Recent Updates

  • April 2024: Invited Talk at the Control Seminar, University of Southern California (USC).
  • March 2024: Our paper on Compressed Temporal Difference Learning is accepted to the Transactions on Machine Learning Research (TMLR).
  • February 2024: Invited Talk at the Statistics Seminar, Department of Statistics, NC State.
  • January 2024: Couple of papers accepted to the American Control Conference (ACC), 2024.
  • January 2024: Paper on Stochastic Approximation under Delays accepted to AISTATS 2024.
  • January 2024: Paper on Federated On-Policy Reinforcement Learning accepted to ICLR 2024.
  • December 2023: Invited (Virtual) Talk in the Network Seminar Series, CNI, Indian Institute of Science (IISc), Bangalore. Video Link
  • September 2023: Invited Talk at the ECE Distinguished Speaker Colloquium, NC State.
  • June 2023: Our paper on security of networked control systems is accepted to Automatica.
  • June 2023: Paper on Federated Reinforcement Learning over Noisy Channels accepted to IEEE Control Systems Letters.
  • February 2023: Organizing a session on Multi-Agent Learning and Decision-Making at ITA 2023, UC San Diego with AbolFazl Hashemi and Arya Mazumdar. Also gave a talk at this session.