Max Robotics — Global Service Robot Directory + US Certification

About

Rayan Younis is at the forefront of integrating artificial intelligence with robot-assisted surgery, pioneering cognitive cooperative assistance systems that promise to transform minimally invasive interventions. His research centers on multi-agent reinforcement learning, surgical automation, and the extraction of surgomic features—machine-learning-derived characteristics from intraoperative data that enable personalized surgical outcome prediction. Younis's work on cooperative assistance in robotic surgery through multi-agent reinforcement learning (21 citations) demonstrates how automation can reduce surgeon mental exertion and fatigue, while his influential overview of machine learning in autonomous surgical decision-making (21 citations) articulates the paradigm shift toward high-tech, interconnected operating rooms. He developed LapGym, an open-source framework for reinforcement learning in laparoscopic surgery (19 citations), providing standardized environments essential for advancing surgical AI algorithms. His prospective annotation study on active learning for surgomic feature extraction (16 citations) addresses the critical bottleneck of high-quality medical data annotation. Younis has also contributed to augmented reality-based robot control, weakly supervised semantic segmentation of laparoscopic scenes, and semi-autonomous robotic assistance for gallbladder retraction. Through these innovations, he is systematically building the technological foundation for safer, more efficient, and increasingly autonomous surgical care.