Abhronil Sengupta

Abhronil Sengupta is an Associate Professor in Electrical Engineering, specializing in neuromorphic computing, spiking neural networks, and spintronics. His research explores the intersection of artificial intelligence and hardware design, focusing on energy-efficient systems inspired by biological neural processes.

• Research Interests: Spiking Neural Networks, Neuromorphic Computing, Spintronics, Synaptic Plasticity, Energy-Efficient AI.

His work spans both theoretical and practical domains, including algorithm development, device physics, and hardware implementation. Recent projects involve leveraging ferroelectric FETs for dendritic computing and spintronic devices for stochastic neural systems.

Publications emphasize spatiotemporal pruning, adversarial attack resistance, and quantum material integration in neuromorphic systems. He actively collaborates on NSF-funded initiatives like CAREER and EFRI BRAID grants, aiming to advance brain-inspired computing paradigms.