Sushmita Bhattacharya
Papers
3
Total Citations
14
H-Index
2
About
Sushmita Bhattacharya is a rising star in autonomous systems and multiagent reinforcement learning, with a focus on solving complex decision-making problems under partial observability. Her research bridges theoretical advances in dynamic programming with real-world robotics applications, particularly in autonomous repair and environmental monitoring. Bhattacharya’s most cited work introduces a reinforcement learning framework for whale rendezvous using autonomous sensing robots, addressing the challenge of tracking sperm whales during prolonged dives by integrating multiagent routing with synthetic aperture radar-based VHF signals. She has also made significant contributions to multiagent rollout and policy iteration algorithms for partially observable Markov decision processes (POMDPs), developing methods that simultaneously or sequentially optimize agent controls for tasks like multi-robot repair. Her 2020 papers on partitioned rollout and policy iteration for autonomous sequential repair problems further demonstrate her ability to combine theoretical rigor with practical deployment. With over a dozen citations across her key works, Bhattacharya’s research is gaining traction for its innovative approach to enabling robots to operate reliably in uncertain, real-world environments. Her work stands out for its potential to transform autonomous systems in conservation, industrial maintenance, and beyond.
Research Focus
Key Achievements
Top Papers
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