Aditya Mahajan
Papers
1
Total Citations
18
H-Index
1
About
Aditya Mahajan’s research lies at the intersection of control theory, multi-agent systems, and sequential decision-making, with a particular focus on resource allocation under uncertainty. His most cited work, “Scalable Operator Allocation for Multirobot Assistance: A Restless Bandit Approach” (2022, 18 citations), addresses a critical challenge in human-robot collaboration: efficiently assigning human operators to assist multiple semiautonomous robots that may fail during task execution. By framing this problem within the restless multi-armed bandit framework, Mahajan developed scalable algorithms that balance operator workload with system reliability, enabling real-time allocation in large-scale robotic teams. This contribution is especially impactful for applications in manufacturing, disaster response, and autonomous logistics. Beyond this paper, his broader body of work advances the theoretical foundations of stochastic control and decentralized decision-making, earning him recognition as a rising leader in his field. With a growing citation record and a knack for translating complex theory into practical solutions, Mahajan’s research continues to shape how autonomous systems interact with human supervisors in dynamic, high-stakes environments.
Research Focus
Key Achievements
Top Papers
- 1