Daehan Kwak
Papers
2
Total Citations
81
H-Index
2
About
Daehan Kwak is a researcher at the intersection of social robotics, human-computer interaction, and neural rehabilitation. His work focuses on developing intelligent systems that bridge the gap between human intent and machine response, with particular emphasis on information extraction and brain-computer interfaces. Kwak’s highly cited 2017 paper on merged ontology and SVM-based information extraction for social robots (58 citations) pioneered methods for enabling humanoid robots to interpret voice queries and deliver personalized recommendations, advancing the field of socially assistive robotics. More recently, his 2023 survey on EEG and machine learning methods for neural rehabilitation (23 citations) provides a comprehensive framework for using brain-computer interfaces to restore motor function in patients with neurological impairments. This work highlights his commitment to translating AI and robotics research into practical therapeutic tools. Kwak’s contributions are notable for their interdisciplinary approach, combining machine learning, ontology engineering, and neuroscience to create systems that are both technically robust and clinically relevant. His research continues to influence the development of assistive technologies that enhance human capabilities.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Survey of EEG and Machine Learning-Based Methods for Neural Rehabilitation23 citations · 2023