Nikhil Angad Bakshi
Papers
1
Total Citations
3
H-Index
1
About
Nikhil Angad Bakshi is an emerging researcher whose work sits at the intersection of multi-agent systems, probabilistic reasoning, and autonomous robotics. His most notable contribution, "GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search" (2023), addresses one of the most pressing challenges in disaster response robotics — enabling teams of autonomous agents to efficiently and safely search large or hazardous environments without risking human lives. By framing the problem as an asynchronous multi-agent active-search task, Bakshi introduces a generalized uncertainty-aware framework built on Thompson Sampling, a powerful probabilistic decision-making strategy, to coordinate robots in dynamically uncertain environments. This work reflects a broader commitment to developing robust, real-world AI systems that can operate reliably under incomplete information. Though early in his research career, with the work accumulating 3 citations since publication, Bakshi's focus on life-critical applications demonstrates both technical depth and meaningful societal motivation. His research will likely resonate with scholars working in reinforcement learning, probabilistic robotics, and humanitarian technology as the field continues to grow.
Research Focus
Key Achievements
Top Papers
- 1