Arpit Shah

Rutgers, The State University of New Jersey, Cleveland State University

Papers

Total Citations

118

H-Index

About

Arpit Shah is a researcher working at the intersection of neuromorphic computing, robotics, and evolutionary algorithms, with contributions spanning both bio-inspired artificial intelligence and distributed optimization systems. His most notable work, "Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM" (2019), has garnered 96 citations and represents a significant advance in robotics and neuromorphic engineering. By drawing on how mammalian brains solve spatial navigation through specialized neurons and event-driven, asynchronous communication, Shah demonstrated that simultaneous localization and mapping (SLAM) — a core challenge for autonomous mobile robots — could be performed with dramatically reduced energy consumption on neuromorphic hardware. This work bridges neuroscience and robotics in a practically impactful way. Earlier in his career, Shah explored distributed evolutionary computation through biogeography-based optimization (BBO), developing and validating hardware implementations for multi-robot learning systems. His series of papers from 2011 to 2013 laid foundational work for decentralized robot control without reliance on centralized computing infrastructure. While these works attracted modest citations, they reflect a consistent research philosophy: taking inspiration from natural systems — whether ecosystems or neural architecture — to build more efficient, scalable intelligent systems. Shah's trajectory highlights a researcher committed to bridging biological principles with real-world robotics applications.