Sanjeev Khudanpur
Papers
7
Total Citations
675
H-Index
7
About
Sanjeev Khudanpur is a leading figure in the intersection of machine learning, speech processing, and surgical data science. His pioneering work focuses on developing computational models for analyzing high-dimensional time-series data, with a major emphasis on automated surgical skill assessment and gesture recognition. Khudanpur’s contributions include the creation of the JIGSAWS dataset, a benchmark that has garnered over 288 citations and become a standard resource for robotic surgery analysis. He introduced sparse hidden Markov models for classifying surgical gestures and evaluating skill levels, achieving 156 citations, and developed data-derived segmentation models cited over 107 times. His research also extends to string motif-based descriptions of tool motion and task-level metrics for skill evaluation, each receiving significant attention. Beyond surgery, Khudanpur has advanced learning and inference algorithms for dynamical systems modeling of dexterous motion, with applications spanning video and speech. His work on unsupervised data alignment for automatic activity annotation further demonstrates his impact in enabling scalable, objective surgical education tools. With over 675 cumulative citations across his most-cited papers, Khudanpur’s innovations are foundational to modern surgical data analysis and human activity modeling.
Research Focus
Key Achievements
Top Papers
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