Jinglu Zhang

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About

Jinglu Zhang is a leading researcher in surgical data science, with a primary focus on computer vision and machine learning for minimally invasive surgery. Her work centers on developing intelligent systems for automated surgical gesture recognition and skill assessment, aiming to provide real-time, context-aware assistance in the operating room. Zhang’s major contributions include the introduction of **Symmetric Dilated Convolution**, a novel architecture that effectively captures long-range temporal dependencies in surgical video without requiring additional sensors—a key limitation of prior methods. Her most cited paper on this technique (2020) has garnered 31 citations, while her subsequent work, **SD-Net** (2021), which jointly performs gesture recognition and skill assessment, has accumulated 15 citations. By enabling robust analysis from standard video feeds alone, Zhang’s research paves the way for more accessible and scalable surgical AI tools. Her achievements are notable for addressing a critical gap in temporal modeling, directly impacting the development of intelligent surgical assistants and objective performance evaluation in training.