Max Robotics — Global Service Robot Directory + US Certification

About

Elif Ayvali’s research sits at the intersection of robotics, medical sensing, and autonomous decision-making, with a focus on improving surgical navigation and tissue characterization. Her most impactful work introduces Bayesian optimization to guide robotic palpation, enabling simultaneous registration of preoperative models with intraoperative data and stiffness mapping of flexible environments—a critical advance for computer-aided surgery. This paper has garnered 38 citations. She further extended this line of inquiry with utility-guided palpation (28 citations), which directs autonomous robots to efficiently locate tissue abnormalities such as tumors or hidden arteries, reducing the need for exhaustive probing. Ayvali has also made notable contributions to multi-agent systems, developing ergodic coverage algorithms that allow robot teams to uniformly explore domains while avoiding obstacles (20 citations), and extending these methods to constrained environments using stochastic trajectory optimization (7 citations). Her recent work applies deep learning to detect clinical operations during robot-assisted percutaneous renal access, demonstrating her ongoing commitment to translating robotics into practical surgical tools. Through these contributions, Ayvali has advanced both the theory and application of autonomous robotic exploration and medical palpation.