Yisen Huang
Papers
16
Total Citations
242
H-Index
9
About
Yisen Huang is a pioneering researcher at the intersection of robotic surgery, computer vision, and intelligent control systems, with a particular focus on autonomous endoscopic technologies for minimally invasive surgery. His work addresses critical challenges in surgical robotics, including instrument tracking, autonomous endoscope manipulation, and surgeon-robot collaboration—problems that directly impact surgical safety and clinical outcomes. Huang's landmark contributions include developing the first deep learning-based instrument tracking system for a magnetic anchored surgical endoscope (39 citations), a breakthrough particularly relevant to video-assisted thoracoscopic surgery. He has further distinguished himself through sophisticated neural network innovations, including noise-resistant adaptive gain zeroing neural networks and predefined-time convergent adaptive neural networks for visual servoing, collectively accumulating over 70 citations. His surgeon preference-guided autonomous tracking framework (32 citations) represents a thoughtful human-centered approach to robotic endoscopy, addressing the often-overlooked challenge of intuitive surgeon-robot cooperation. Beyond flexible endoscopes, Huang has extended his expertise to electromagnetically actuated colonoscopes, brain biopsy robotic systems, and DNA-inspired continuum mechanisms. His body of work, totaling over 220 citations across ten highly focused publications, reflects both technical depth and genuine clinical awareness, making him an increasingly influential voice in the emerging field of autonomous surgical robotics.
Research Focus
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