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"All models are wrong, but some are useful." – George Box [Name pronounced either as Saw-rv or Show-oo-rob (Bengali)] I am a Ph.D. candidate at the University of Colorado Boulder, working as a Graduate Research Assistant in the Department of Computer Science. I am advised by Prof. Lijun Chen. My research focuses on the mathematical foundations of sequential decision-making under uncertainty, with a primary emphasis on Multi-Agent Reinforcement Learning (MARL) and Multi-Armed Bandits. I investigate how structural, physical, and strategic constraints shape the limits of learnability, particularly in decentralized coordination, continuous action spaces, and evolving networked structures. Prior to my Ph.D., I earned my Master’s in Computer Science at the University of Colorado Boulder and my bachelor's degree from the Birla Institute of Technology, Mesra. Before moving to the U.S., I worked as a Software Engineer at Flipkart in Bangalore, designing scalable search pipelines and centralized data recovery platforms. Beyond research, I find myself drawn to cinema, literature, and music. Cricket has been a constant since childhood: I played for a local club at school, later for the Flipkart corporate team, and now follow the game closely as a spectator. I am currently on the job market for Fall 2026. Fun Fact. My Erdős Number is 4 and Dijkstra Number is 5.
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Taken in Ouray, Colorado, |
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I develop the mathematical foundations of sequential decision-making to establish how structural, physical, and strategic constraints shape the limits of learnability in uncertain and stochastic environments. My overarching research agenda focuses on three core areas: (i) Scalable Multi-Agent Reinforcement Learning, where I develop theoretical frameworks to overcome the curse of dimensionality by exploiting policy-dependent locality; (ii) Graph-Constrained & Dynamic Bandits, characterizing the limits of online learning when an agent's actions are restricted by evolving network topologies; and (iii) Continuous & Incentivized Learning, designing sequential decision-making algorithms for Lipschitz-structured action spaces and environments with biased feedback. |
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Sourav Chakraborty*, Amit Kiran Rege*, Claire Monteleoni, Lijun Chen Accepted at International Conference of Artificial Intelligence and Statistics (AISTATS) 2026 in Tangier, Morocco. arXiv (preprint)
Abstract (short): We introduce a communication-free, modular protocol for decentralized multi-player bandits over continuous, Lipschitz-structured action spaces. Our approach decouples agent coordination from exploration, achieving near-optimal regret without horizon-dependent coordination costs.
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Sourav Chakraborty*, Amit Kiran Rege*, Claire Monteleoni, Lijun Chen Accepted at Learning for Dynamics & Control Conference (L4DC) 2026 in Los Angeles, California, United States. (Selected as an Oral Presentation; ~Top 10%) arXiv (preprint)
Abstract (short): We formalize a novel bandit framework where arm availability is constrained by local moves on evolving random graphs (Erdős–Rényi and Edge-Markovian). We design a two-phase algorithm utilizing lazy random walks to achieve near-optimal, sublinear regret under these non-stationary physical constraints.
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Sourav Chakraborty*, Amit Kiran Rege*, Claire Monteleoni, Lijun Chen Accepted at Learning for Dynamics & Control Conference (L4DC) 2026 in Los Angeles, California, United States. arXiv (preprint)
Abstract (short): We establish that locality in decentralized MARL is inherently policy-dependent, introducing a novel spectral condition that decouples environmental coupling from policy smoothness. This framework enables policy improvement that circumvents the curse of dimensionality.
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Sourav Chakraborty*, Amit Kiran Rege*, Claire Monteleoni, Lijun Chen Accepted at ARLET Workshop at Neural Information Processing Systems (NeurIPS) 2025 in San Diego, California, United States. arXiv
Abstract (short): We study online learning where an agent is constrained to local movements on a dynamic graph sequence.
We formalizing sufficient structural conditions for learnability and design near-optimal, exploration policies.
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Sourav Chakraborty*, Amit Kiran Rege*, Claire Monteleoni, Lijun Chen Accepted at IEEE Conference on Decision and Control (CDC) 2025 in Rio de Janeiro, Brazil. IEEE / arXiv
Abstract (short): We develop incentivized exploration schemes in continuous metric spaces where the system compensates myopic agents to explore, despite receiving biased feedback. We develop near-optimal uniform discretization-based algorithms.
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Sourav Chakraborty and Lijun Chen. Accepted at IEEE American Control Conference (ACC) 2024 in Toronto, Canada. Master's Thesis, Committee: Lijun Chen, Raf Frongillo, Bo Waggoner. IEEE version / arXiv / thesis
Abstract (short): We address the challenge of incentivizing myopic agents to explore in both abruptly-changing and continuously-drifting bandit environments with drifted feedback and propose near-optimal algorithms.
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I worked on production systems at Flipkart across storage/reliability infrastructure and search relevance, with a focus on product discovery, ranking, and data reliability. |
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Software Engineer at Flipkart
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My teaching contributions span undergraduate computer science instruction and department-level instructional support, including course operations, student-facing instruction, assessment, staff training, and mentorship. |
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Last updated: Feb 2026
Thanks Jon Barron!