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About

Liaohai Chen is a researcher working at the intersection of robotic surgery, machine learning, and surgical data science, with contributions spanning both clinical evaluation and computational innovation. His work addresses some of the most pressing challenges in minimally invasive surgery, from assessing the learning curves of complex robotic procedures to enabling autonomous surgical systems. Chen's most cited work examines the adoption of robotic-assisted Roux-en-Y gastric bypass, offering a rigorous cumulative sum analysis of surgical learning curves — a study that has garnered 20 citations and remains relevant to bariatric surgery training programs. Building on this clinical foundation, he has pivoted toward data-driven approaches to surgical intelligence. His development of the Comprehensive Robotic Cholecystectomy Dataset (CRCD), which integrates kinematics, pedal signals, and endoscopic video, provides the research community with a rich multimodal resource for training machine learning models. Complementing this, his framework for automated tissue dissection and his novel sensory glove-based surgeon interface point toward a future of more accessible and intelligent robotic platforms. His work on recurrent and spiking neural networks for modeling sparse surgical kinematics further demonstrates his commitment to advancing computational tools for surgical skill assessment and automation.