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About

Nithya Bhasker is a leading researcher at the intersection of surgical data science and machine learning, with a primary focus on advancing robot-assisted minimally invasive surgery. Her key research areas include surgomics—the extraction of surgical process characteristics from multimodal intraoperative data—and active learning methodologies for medical annotation. Bhasker’s major contribution lies in developing scalable annotation frameworks that reduce the burden on clinical experts while maintaining high-quality data for training predictive models. Her most-cited work, the 2023 prospective annotation study on active learning for robot-assisted minimally invasive esophagectomy (16 citations), demonstrates how intelligent sampling strategies can efficiently extract surgomic features, enabling personalized prediction of patient outcomes. This work addresses a critical bottleneck in surgical AI: the need for expert-labeled data. Bhasker’s research has significant implications for improving surgical precision and postoperative care, bridging the gap between raw intraoperative data and actionable clinical insights. Her innovative approach to combining active learning with surgical process modeling positions her as a key figure in the emerging field of data-driven surgical optimization.