The Neuromorphic Sensing Team pioneers brain-inspired computing architectures that revolutionize how robots process information. By emulating the structure and function of biological neural networks, we develop energy-efficient, adaptive systems capable of real-time learning and decision-making in dynamic environments.
Our research spans spiking neural networks (SNNs), neuromorphic hardware integration, event-based sensing, and bio-inspired learning algorithms. We focus on developing computational models that combine the efficiency of biological brains with the scalability of modern computing, enabling robots to achieve cognitive capabilities while maintaining low power consumption.
Bio-inspired neural computation using spike-based information processing
Ultra-low power consumption for sustainable robotic systems
Real-time adaptation and learning in dynamic environments
Asynchronous data processing inspired by biological vision
Yonghoon Ji received the B.S. degrees in mechanical engineering and in computer engineering from Kyunghee University, Seoul, South Korea, in 2010, the M.S. degree in mechatronics from Korea University, Seoul, in 2012, and the Ph.D. degree in precision engineering from The University of Tokyo, Tokyo, Japan, in 2016.
From 2016 to 2018, he was an overseas Researcher under the Postdoctoral Fellowship of Japan Society for the Promotion of Science with the Department of Precision Engineering, The University of Tokyo. From 2018 to 2020, he was an Assistant Professor with the Department of Precision Mechanics, Chuo University, Tokyo, Japan. He is currently an Associate Professor with the Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, Japan. His research interests include mobile robotics, autonomous vehicles, and marine robotics. Dr. Ji is a Member of JSME, RSJ, SICE, and KROS.
Shibaura Institute of Technology
JAIST
JAIST