Jason Poon

Welcome! I am an assistant professor in EE at Cal Poly in San Luis Obispo. My research focus is power electronics—specifically, envisioning how power electronics can enable a clean and sustainable future with more renewable energy and electric vehicles. Towards this end, my research draws from a variety of domains—including computing, control, and optimization—with the broad aim of realizing power and energy systems that are more intelligent, efficient, and multifunctional.


Jason Poon is an Assistant Professor in the Department of Electrical Engineering at Cal Poly, San Luis Obispo. He received the M.S. and Ph.D. degree in EECS from UC Berkeley in 2015 and 2019, respectively, and the B.S. degree in ECE from Olin College of Engineering in 2012. He was a postdoc in the EE Department at Stanford from 2019 to 2021. His research interests include power electronics and their applications in next-generation power and energy systems, including renewable energy integration, miniaturized and on-chip power, and electrified transportation. Dr. Poon is the recipient of the 2019 EERE Postdoctoral Research Award and the Best Paper Award at the 2016 IEEE Workshop on Control and Modeling for Power Electronics for his work on distributed fault-tolerant power electronics. Prior to joining Cal Poly, Dr. Poon was a Visiting Scholar and Instructor in the EE Department at Stanford and an affiliate researcher in the Energy Technologies Area at Lawrence Berkeley National Lab.



Invited participant at the DOE Expert Discussion Panels on Miscellaneous Electric Loads in Berkeley, CA.


Co-teaching a new class at Stanford—EE157: Electric Motors for Renewable Energy, Robotics, and Electric Vehicles—with Steven Clark and Aaron Goldin.


Our work on mixed-signal computing for power electronics has been accepted for publication in the IEEE Transactions on Power Electronics and is available as a preprint on TechRxiv. We demonstrate a hardware prototype of a neuromorphic-inspired computing platform for solving complex optimal control problems in real-time.


Presented our work at APEC 2021 on a mixed-signal computing platform for solving online optimization problems for power electronics systems in a fast and energy-efficient manner. See the conference paper for more details.

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