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About

Eung-Joo Lee is a pioneering researcher at the intersection of surgical data science and assistive robotics, whose work is shaping the future of cognitive surgical assistance and rehabilitation technologies. Lee’s most impactful contribution is the development and validation of machine learning algorithms for surgical workflow and skill analysis, benchmarked through the HeiChole dataset—a landmark study that has garnered 96 citations and established a gold standard for evaluating AI in the operating room. This work addresses a critical need for context-sensitive warnings and semi-autonomous robotic assistance, promising to enhance surgical safety and training. Beyond the operating theater, Lee has explored bionic locomotion, programming gaits for quadruped robots to navigate rugged terrain with stability, and has authored a comprehensive literature review on smart wheelchair systems, synthesizing advances in Brain-Computer Interfaces. With over 100 citations across key publications, Lee’s research bridges cutting-edge AI, robotics, and human-centered design, driving innovations that could revolutionize both surgical practice and mobility assistance for individuals with disabilities.