Hyuk‐Jun Kwon
Papers
3
Total Citations
33
H-Index
2
About
Hyuk-Jun Kwon is pioneering the intersection of stretchable electronics and energy-efficient machine learning for robotics. His research focuses on two transformative areas: the fabrication of large-area, on-skin stretchable electronic systems and the application of brain-inspired Hyperdimensional Computing (HDC) for robotic control. Kwon’s major contributions include developing a laser ablation technique for ultrafast prototyping of stretchable sensors that acquire electrophysiological signals, enabling controllable robotic arm operations—a breakthrough for wearable robotics and human-machine interaction. His work on HDC introduces lightweight symbolic learning frameworks, such as ReactHD, which dramatically reduce computational demands for sensorimotor control of wheeled robots, and a novel HDC-based federated learning approach that addresses privacy and resource constraints in mobile robot swarms. With his most-cited paper accumulating 26 citations, Kwon’s impact is evident in advancing practical, energy-efficient solutions for real-world robotics. His achievements bridge materials science and AI, offering scalable pathways for next-generation wearable and autonomous systems.
Research Focus
Key Achievements
Top Papers
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