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About

Yun-Hsuan Su is a prominent researcher at the intersection of surgical robotics, computer vision, and machine learning, with a particular focus on advancing robot-assisted minimally invasive surgery (RMIS). Their work addresses some of the most pressing challenges in surgical automation, including real-time surgical tool segmentation, 3D reconstruction of dynamic surgical cavities, and vision-based force estimation. Su's contributions have meaningfully shaped the field through both benchmark-setting and practical innovation. Their involvement in the 2017 Robotic Instrument Segmentation Challenge (57 citations) helped establish community-wide standards for evaluating surgical vision algorithms, mirroring the transformative role of datasets like ImageNet in mainstream computer vision. Their pioneering work integrating robot kinematics priors into surgical tool segmentation (47 citations) demonstrated how combining physical system knowledge with deep learning yields more robust, real-time performance in the operating room. Beyond perception, Su has contributed to surgical robot software infrastructure through the Collaborative Robotics Toolkit (CRTK, 27 citations) and explored intelligent camera control via deep reinforcement learning. Their research on GAN-driven synthetic image generation further addresses the perennial challenge of limited labeled medical data. Collectively accumulating over 250 citations, Su's body of work represents a rigorous and forward-looking effort to bring greater autonomy, precision, and safety to surgical robotics.