Rohit Agarwal

National Institute of Technology Durgapur

Papers

Total Citations

H-Index

About

Rohit Agarwal is a rising researcher at the forefront of neuromorphic computing and computer vision, with a particular focus on Spiking Neural Networks (SNNs). His most-cited work, "Spiking Neural Network in Computer Vision: Techniques, Tools and Trends" (2023), has already garnered 15 citations, marking him as an emerging voice in this rapidly evolving field. Agarwal’s major contributions lie in systematically mapping the landscape of SNN architectures—from encoding methods to learning algorithms—and their application to visual recognition tasks, offering a comprehensive toolkit that bridges theoretical neuroscience with practical machine learning. By synthesizing current techniques and identifying key trends, his work provides a critical roadmap for researchers aiming to leverage the energy efficiency and temporal dynamics of SNNs for next-generation vision systems. Beyond this flagship paper, Agarwal’s research continues to explore the intersection of biological plausibility and computational performance, positioning him as a key contributor to the future of low-power, event-driven AI. His clear, accessible synthesis of complex topics makes him an invaluable guide for students and practitioners entering the neuromorphic computing space.