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About

Beatrice van Amsterdam is a computational researcher specializing in surgical data science, computer-assisted interventions, and medical robotics, with a particular focus on applying machine learning to the challenges of robotic surgery. Her work sits at the intersection of gesture recognition, surgical skill assessment, and medical image analysis — fields critical to advancing next-generation surgical automation and training systems. Van Amsterdam's most influential contribution is her comprehensive 2021 review of gesture recognition in robotic surgery (152 citations), which has become an essential reference for researchers navigating data-driven approaches to surgical activity recognition. Complementing this, her earlier work on weakly supervised gesture recognition (2019, 31 citations) demonstrated innovative methods for extracting meaningful action units from kinematic data with minimal labeling — a significant practical advancement given the scarcity of annotated surgical datasets. Her research further extends into surgical instrument segmentation, notably through simulation-supervised image synthesis techniques that reduce dependence on large labeled datasets. Her involvement in high-profile community challenges, including MICCAI's SurgVisDom and SAR-RARP50, underscores her collaborative engagement with the broader surgical AI community. Collectively, her contributions are helping lay the groundwork for safer, more intelligent computer-assisted surgical systems.