Jason K. Eshraghian

Jason K. Eshraghian is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Cruz, within the Baskin School of Engineering. He holds a Ph.D. in Electrical and Electronic Engineering from The University of Western Australia and completed his postdoctoral training at the University of Michigan from 2019 to 2022.

His research centers on neuromorphic computing, spiking neural networks, and memory circuits, with a strong emphasis on hardware-software co-design for energy-efficient AI. He is the creator of snnTorch, a popular open-source Python library for training and simulating spiking neural networks, widely used in both academia and industry.

His recent work reflects a deep integration of circuit design, machine learning, and neuroscience. The research trends indicate a focus on low-power, event-driven computing paradigms, particularly through VLSI and neuromorphic hardware implementations. His publications span top-tier conferences and journals in circuits, AI hardware, and electronic systems.

Scientific Awards:

• 2023 IEEE Transactions on Circuits and Systems Darlington Best Paper Award
• 2019 IEEE Very Large Scale Integration Systems Best Paper Award
• Best Paper Award at the 2019 IEEE Artificial Intelligence Circuits and Systems Conference
• Best Live Demonstration Award at the 2020 IEEE International Conference on Electronics Circuits and Systems
• Fulbright Fellowship (Australian-American Fulbright Commission)
• Forrest Research Fellowship (Forrest Research Foundation)
• Endeavour Research Fellowship (Australian Government)

He has been recognized with multiple competitive fellowships and best paper/demonstration awards, reflecting both the quality and impact of his research. While specific grants are not listed, his fellowship support indicates substantial research funding. He has advised students in the areas of neuromorphic systems and AI circuits, though no names are publicly listed in the provided text.

He serves professionally as the Secretary of the Neural Systems and Applications Technical Committee and as an Associate Editor for APL Machine Learning, contributing to the advancement of interdisciplinary research in machine learning and physical systems.