Changyeob Shin
Papers
5
Total Citations
113
H-Index
4
About
Changyeob Shin is a pioneering researcher at the intersection of surgical robotics and autonomous manipulation, with a focus on enhancing precision in minimally invasive procedures. His most impactful work, the "Autonomous Suturing Framework and Quantification Using a Cable-Driven Surgical Robot" (65 citations), addresses one of surgery’s most challenging tasks by introducing a novel needle path planner and accurate pose estimation, enabling robots to perform suturing with limited vision and haptic feedback. Shin also advances ophthalmic surgery through "Semi-Automated Extraction of Lens Fragments Via a Surgical Robot Using Semantic Segmentation of OCT Images" (12 citations), demonstrating deep learning-guided robotic extraction in ex vivo models. His contributions extend to deformable tissue manipulation, where "Toward Synergic Learning for Autonomous Manipulation of Deformable Tissues" (11 citations) employs approximate Q-learning for indirect tissue control—a critical step toward safe robotic surgery. Beyond medicine, Shin’s early work on supernumerary robotic limbs (23 citations) for ceiling construction tasks showcases his versatility in human-robot collaboration. With over 100 total citations, his research bridges automation, computer vision, and control theory, offering practical solutions for high-stakes environments.
Research Focus
Key Achievements
Top Papers
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