Sumei Zhou

Papers

Total Citations

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About

Sumei Zhou is a leading researcher in autonomous robotic surgery and surgical workflow analysis, with a focus on integrating relational domain knowledge into AI-driven systems. Her most-cited work, "MURPHY: Relations Matter in Surgical Workflow Analysis" (2022, 2 citations), introduces a novel framework that distinguishes between intra- and inter-relations in surgical annotations, demonstrating how these relational cues—often overlooked in favor of visual and temporal data—can significantly enhance the accuracy of surgical phase recognition and task prediction. This contribution addresses a critical gap in the field, offering a more holistic approach to understanding complex surgical procedures. Zhou’s research has implications for improving real-time decision support in operating rooms, paving the way for safer and more efficient robotic surgeries. Her work is recognized for bridging domain expertise with machine learning, and she continues to advance the intersection of computer vision, knowledge representation, and surgical automation. With a growing citation impact, Zhou is establishing herself as a key voice in the next generation of intelligent surgical systems.