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About

Shang Zhao is a rising researcher at the intersection of computer vision and robotic-assisted surgery, focusing on three key areas: surgical action detection, 3D depth estimation, and automated surgical workflow recognition. Their most impactful contribution is leading the development of the SARAS Endoscopic Surgeon Action Detection (ESAD) dataset, a benchmark that has garnered 29 citations for addressing the formidable challenge of monitoring and assisting surgeons in autonomous robotic systems. Zhao’s work on 3D endoscopic depth estimation introduces surface-aware constraints to overcome the lack of spatial perception in narrow abdominal spaces—a critical step toward enhancing situational awareness during procedures. They have also advanced surgical workflow analysis with deep learning, notably through the MURPHY framework, which innovatively leverages relational cues (both intra- and inter-relations) from domain knowledge to improve fine-grained segmentation and recognition accuracy. With a growing citation impact and a focus on translating visual and temporal cues into actionable surgical intelligence, Zhao is helping pave the way for more autonomous, context-aware robotic surgery.