Prabuddha Chakraborty
Papers
1
Total Citations
2
H-Index
1
About
Prabuddha Chakraborty is a rising researcher at the forefront of efficient artificial intelligence, with a primary focus on neural architecture search (NAS) and multi-task learning. His most notable contribution is the development of **Intelligent Layer Sharing (ILASH)** , a predictive NAS framework designed specifically for multi-task applications. This work addresses a critical challenge in deploying AI across resource-constrained domains such as healthcare, autonomous vehicles, and robotics—enabling multiple analyses on the same data while minimizing computational overhead. By intelligently predicting which neural network layers can be shared across tasks, ILASH reduces redundancy and improves efficiency without sacrificing accuracy. Although his 2025 paper has already garnered early citations, reflecting growing interest in practical, deployable AI, Chakraborty’s work stands out for its direct relevance to real-world systems where power and latency are limited. His research bridges the gap between high-performance multi-task models and the stringent requirements of edge devices, making him a promising voice in the push toward sustainable, scalable artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1