Shamak Dutta
Papers
1
Total Citations
2
H-Index
1
About
Shamak Dutta is a researcher at the forefront of robotics and machine learning, with a primary focus on informative path planning and active regression under resource constraints. His most cited work, "Informative Path Planning for Active Regression With Gaussian Processes via Sparse Optimization" (2025), addresses a critical challenge: how a team of robots can efficiently collect measurements of an unknown function—modeled as a sample from a Gaussian Process—while minimizing the trace of the posterior covariance. This contribution is pivotal for environmental monitoring, autonomous exploration, and sensor deployment, where data quality and energy efficiency are paramount. By introducing sparse optimization techniques, Dutta enables robots to make intelligent, real-time decisions about where to sample next, significantly reducing uncertainty in the learned model. Though his work is early in its citation trajectory, its practical implications for multi-robot systems and active learning are already recognized. Dutta’s research bridges theoretical rigor with real-world robotics, offering scalable solutions for autonomous data collection. His achievements underscore a promising career dedicated to advancing autonomous decision-making in complex, uncertain environments.
Research Focus
Key Achievements
Top Papers
- 1