Asst Professor, Electrical and Computer Engineering
Digital and analog Very large-scale integration (VLSI) circuits and architecture for machine learning and signal processing algorithms by leveraging emerging computing paradigms including in-memory / in-sensor / neuromorphic computing
Kang researches vertically integrated VLSI information processing for machine learning and signal processing algorithms. His research focuses on energy- and latency-efficient integrated circuits, architectures, and systems by leveraging novel computing paradigms including in-memory, in-sensor, and neuromorphic computing with both CMOS and emerging devices.
Kang joined the Jacobs School of Engineering in 2020 after conducting research at the IBM Thomas J. Watson Research Center in New York, where he designed machine learning accelerator architecture. He received a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana–Champaign in 2017, and B.S. and M.S. degrees in Electrical and Electronic Engineering from Yonsei University, Seoul, South Korea, in 2007 and 2009, respectively. From 2009 to 2012, he was with the Memory Division, Samsung Electronics, Hwaseong, South Korea, where he was involved in the circuit and architecture design of phase change memory (PRAM).