Shanlin Yang
Papers
8
Total Citations
210
H-Index
4
About
Shanlin Yang is a leading researcher in surgical robotics and computer-assisted intervention, with a focus on robot-assisted minimally invasive surgery (MIS), autonomous instrument tracking, and 3D perception for virtual reality surgical systems. Yang’s major contributions include pioneering unsupervised-learning-based methods for continuous depth and motion estimation from monocular endoscopy, enabling immersive 3D displays for systems like the Da Vinci surgical robot (95 citations). They developed the Visual Tracking Space Vector method for autonomous multi-instrument tracking, allowing robot-held laparoscopes to collaborate seamlessly with surgeons without manual intervention (52 citations). Yang also proposed a 3D collision avoidance method that prevents damage to robotic systems and patient tissues during surgery (34 citations), and introduced SIRNet for fine-grained surgical interaction recognition to enhance navigation decision support (16 citations). Their work extends to face tracking for patient monitoring in ICUs and edge robotics for COVID-19 monitoring. With over 200 total citations, Yang’s research bridges autonomous algorithms and human–robot shared control, advancing the safety, precision, and autonomy of next-generation surgical robots.
Research Focus
Key Achievements
Top Papers
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- 4SIRNet: Fine-Grained Surgical Interaction Recognition16 citations · 2022
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- 7The Role of Edge Robotics As-a-Service in Monitoring COVID-19 Infection4 citations · 2020
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