Max Robotics — Global Service Robot Directory + US Certification

About

Michael Peven is a researcher at the intersection of computer vision, surgical robotics, and multi-modal machine learning. His work focuses on enabling intelligent systems to perceive and understand complex physical interactions, particularly in medical and surgical contexts. Peven’s most cited contribution, “Learning to See Forces: Surgical Force Prediction with RGB-Point Cloud Temporal Convolutional Networks” (2018, 26 citations), pioneered a novel approach to predicting forces during surgery by fusing RGB video with 3D point cloud data and temporal convolutional networks—a key step toward safer, more autonomous robotic surgery. He also played a leading role in the PEg TRAnsfer Workflow Recognition Challenge, authoring two reports (2022, 12 citations; 2023, 5 citations) that systematically evaluated how multi-modal data—such as video, motion, and force signals—can improve surgical workflow recognition. These contributions have helped shape the field of surgical data science, demonstrating that combining diverse sensor modalities significantly boosts model robustness. Peven’s work is widely cited by researchers developing next-generation surgical assistants and has practical implications for improving patient outcomes through enhanced intraoperative feedback and automation.