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About

Dr. Alice Segato is a leading researcher at the intersection of robotics, artificial intelligence, and neurosurgery, with a focus on developing autonomous systems for minimally invasive procedures. Her key research areas include inverse reinforcement learning for surgical path planning, steerable needle control, and deformable tissue modeling. Dr. Segato's major contributions include pioneering an intra-operative planning framework for flexible neurosurgical robots that ensures safe keyhole procedures, as evidenced by her highly cited 2021 work (40 citations). She has also advanced Deep Brain Stimulation by automating steerable path planning to safeguard critical brain structures (28 citations), and developed a Position-Based Dynamics simulator for real-time brain deformation modeling (19 citations). Her impact extends beyond neurosurgery to structural cardiology, where she has innovated robotic catheter actuation and control (14 citations), and to autonomous laparoscopic surgery, where her team demonstrated consistent suturing for small bowel anastomosis (10 citations). With a growing citation record and recent work on robust path planning for catheters in deformable environments (2024), Dr. Segato is shaping the future of intelligent, autonomous surgical robotics.