Amit Banerjee
Papers
2
Total Citations
6
H-Index
2
About
Amit Banerjee’s research bridges signal processing and robotics, with a focus on adaptive systems and intelligent behavior. In signal processing, Banerjee has advanced the theory of non-uniform sampling, particularly through his work on cardinality-constrained approaches that balance reconstruction accuracy with sample size—a critical trade-off for time-varying signals. This work, published in 2024, has already garnered 3 citations, reflecting its emerging impact in the field. In robotics, Banerjee explores the computational modeling of deception, introducing an adaptive Markov process to enable robots to engage in deceptive behaviors in interactive games. His 2019 paper on robot deception, also with 3 citations, contributes to the growing literature on intelligent systems that mimic animal and human strategies for advantage. Banerjee’s research is notable for its interdisciplinary nature, applying rigorous mathematical frameworks to both signal reconstruction and autonomous decision-making. His work offers practical implications for efficient data acquisition and more sophisticated human-robot interaction, making him a promising voice in the development of adaptive, intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2An Adaptive Markov Process for Robot Deception.3 citations · 2019