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About

Shruti Kulkarni is a pioneering researcher at the intersection of neuromorphic computing and bio-inspired robotics, with a focus on developing ultra-low-power artificial intelligence for edge applications. Her most influential work, "Evolutionary vs imitation learning for neuromorphic control at the edge" (2021, 21 citations), investigates optimal training strategies for spiking neural networks (SNNs) in resource-constrained environments, directly addressing the challenge of implementing efficient AI in autonomous vehicles and robotics. This contribution is critical for advancing real-time, low-power decision-making systems. Kulkarni also demonstrated innovative bio-mimetic design in her 2015 live demonstration of a spiking neural circuit for autonomous navigation, inspired by the thermotaxis behavior of the nematode *C. elegans*. This work showcased a practical SNN-driven robot that uses light intensity for navigation, highlighting her ability to translate biological neural principles into functional hardware. Additionally, her research extends to healthcare, where she examined how patient distance from a cancer center affects perioperative outcomes after robotic-assisted pulmonary lobectomy (2022), revealing socioeconomic factors in surgical recovery. With a portfolio blending neuromorphic engineering, evolutionary algorithms, and clinical applications, Kulkarni’s work is shaping the future of intelligent, energy-efficient systems at the edge.