Aritra Mitra
 |
Postdoctoral Researcher E-mail: |
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.
My CV is available here.
Research Interests
My research lies at the intersection of Control and Optimization Theory, Statistical Signal Processing, Machine Learning, and Network Science. One of the main themes of my work is to design and analyze principled algorithms for distributed information processing that are scalable, communication-efficient, and robust to adversarial perturbations.
My recent work focuses on (i) understanding fundamental convergence rates in the emerging paradigm of Federated Learning, and (ii) investigating the performance bounds of sequential decision-making problems (such as multi-armed bandits) in multi-agent settings.
Recent Updates
October 2021: Selected to receive a NeurIPS 2021 Outstanding Reviewer Award given to the top 8% of reviewers who were judged to be instrumental to the review process!
September 2021: Our paper titled ‘Linear Convergence in Federated Learning:Tackling Client Heterogeneity and Sparse Gradients’ is accepted to NeurIPS 21!
September 2021: I gave an invited talk at Google on the Impacts of Statistical Heterogeneity in Federated Supervised Learning and Best-Arm Identification.
September 2021: Our paper titled ‘On the Computational Complexity of the Secure State-Reconstruction Problem’ is accepted to Automatica.
July 2021: Our papers titled Online Federated Learning and Federated Learning with Incrementally Aggregated Gradients are accepted for presentation at the Decision and Control Conference (CDC), 2021, Austin, Texas, USA.
June 2021: Our paper ‘Distributed Inference with Sparse and Quantized Communication’ is accepted to the IEEE Transactions on Signal Processing.
March 2021: Our paper ‘Near-Optimal Data Source Selection for Bayesian Learning’ is accepted to the 3rd Annual Learning for Dynamics and Control Conference (L4DC) 2021, ETH Zurich, Switzerland.
February 2021: Our paper titled ‘Distributed State Estimation over Time-Varying Graphs: Exploiting the Age-of-Information’ is conditionally accepted to the IEEE Transactions on Automatic Control.
September 2020: Our paper titled ‘A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience’ is accepted to the IEEE Transactions on Automatic Control!
July 2020: Our paper titled ‘Event-Triggered Distributed Inference’ is accepted for presentation at the Decision and Control Conference (CDC) 2020, Jeju Island, Republic of Korea. An extended version of the paper is available here.
July 2020: Gave an invited (virtual) talk on my PhD research at the Indian Institute of Technology, Kharagpur.
June 2020: Successfully defended my PhD thesis!
Jan 2020: Presented our work on Distributed Hypothesis Testing and Non-Bayesian Social Learning at the Robert Bosch Centre for Cyber-Physical Systems, IISC Bangalore. Here is an abstract of the talk.