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About

Neil Getty is a researcher specializing in machine learning applications for robotic-assisted surgery and surgical data science. His work sits at the intersection of artificial intelligence and minimally invasive surgical systems, with a focus on developing computational models that can interpret and analyze complex surgical data. Getty's most notable contribution is his involvement in the Intuitive Surgical SurgToolLoc and SurgVU Challenges (2023), a landmark initiative that brought together the surgical data science community to advance machine learning models for robot-assisted surgery — a paper that has already garnered 6 citations since its publication. His earlier research on recurrent and spiking neural network architectures for modeling sparse surgical kinematics (2020) demonstrates his commitment to translating subjective surgical practices into precise, measurable motion sequences that machine learning systems can meaningfully analyze and evaluate. By applying advanced neural network techniques — including recurrent and neuromorphic spiking models — to robotic surgical kinematics, Getty contributes to a growing body of work aimed at objectively assessing surgeon performance and improving patient outcomes. His research reflects a broader mission to transform surgery from an art into a rigorously data-driven discipline, making him a meaningful contributor to the emerging field of computational surgical intelligence.