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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. student at the University of Colorado Boulder, working as a Graduate Research Assistant at 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, particularly, Multi-Armed Bandit models and Reinforcement Learning. Prior to my Ph.D., I completed a Master’s in Computer Science at the University of Colorado Boulder. I earned my bachelor's degree from the Birla Institute of Technology, Mesra in Ranchi, India, and worked as a software engineer at Flipkart in Bangalore before moving to the United States. 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 and later for the Flipkart team, and now follow the game closely as a spectator. Fun Fact. My Erdős Number is 4 and Djikstra Number is 5.
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Taken in Ouray, Colorado, |
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I investigate how structural, physical, and strategic constraints shape the limits of learnability in uncertain environments. My work focuses on three core pillars:
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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. |
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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%) |
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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. |
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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 |
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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 |
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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 |
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*Alphabetically |
 
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Tuhina Tripathi, Alexa Reed, Sourav Chakraborty April , 2022 report / code / demo / interface-code Final project for ASEN 5519: Decision Making Under Uncertainty. This project explores the use of Inverse Reinforcement Learning, via Maximum Entropy Formulation, in a Markov Decision Process. The concepts explored in this project were demonstrated using a grid world environment. |
 
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Sourav Chakraborty September, 2020 original paper / code Just playing around with the paper by Liu & Wang et alon Incentivized Exploration for Multi-Armed Bandits under Reward Drift where the players receive compensation for exploring arms other than the greedy choice and may provide biased feedback on reward drift. |
 
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*Amit Baran Roy, Sourav Chakraborty May, 2020 report / code A word embedding model implementation based on the popular skipgram architecture. It involves alterations of the scoring algorithm to give more weightage to the context words that are closer to the target word in a skipgram sliding window. |
 
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Sourav Chakraborty, Nagarajan Shanmuganathan May, 2020 report / code It is known well that Counterfactual regret minimization (CFR) has been used in games which have both terminal states and perfect recall to minimize regret. This project aims to relax those constraints and use a local no-regret algorithm (LONR) by Kash et al, which internally uses a Q-learning like update rule to games which do not have terminal states or perfect recall. |
 
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*Amit Baran Roy, Aparajita Singh, Sourav Chakraborty, Tanmai Gajula Feb-April, 2020 report / code The complete 3D geometry of an object from a single 2.5D depth view was acquired by using deep learning techniques such as generative adversarial networks and 3D convolution neural networks. The resolution of the final 3D voxelized output was improved by transforming the voxel representation into another representation called occupancy networks. |
Last updated: Jan 2026
Thanks Jon Barron!