Papers
12
Total Citations
135
H-Index
6
About
Jungsik Hwang is a leading researcher at the intersection of cognitive robotics, human-robot interaction, and wearable technology. His primary contributions lie in developing deep learning models—particularly deep dynamic neural networks based on the predictive coding framework—that enable robots to perceive, predict, and generate complex spatio-temporal visuomotor patterns. Hwang’s most cited work (51 citations) demonstrates how robots can learn imitative interactions with humans through iterative mental simulation, while his subsequent studies show seamless integration of cognitive skills like visual recognition, attention, and action generation in humanoid platforms. His research has achieved over 130 total citations, with key papers exploring synergy in cognitive behavior and novel action generation. Beyond robotics, Hwang has extended his expertise to healthcare, designing a soft wearable passive fitness device for upper limb resistance exercise and assessing IMU sensor placement for gait analysis. He has also contributed to socially impactful work, developing robot-mediated imitation training for children with Autism Spectrum Disorders. Hwang’s work bridges foundational cognitive science with practical applications in rehabilitation and assistive technology.
Research Focus
Key Achievements
Top Papers
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- 2Predictive coding-based deep dynamic neural network for visuomotor learning18 citations · 2017
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