Heath Boyea

Papers

Total Citations

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About

Heath Boyea is a researcher advancing the frontiers of robot-assisted surgery through intelligent haptic guidance and machine learning. His primary research areas include surgical robotics, human-robot interaction, and trajectory prediction for teleoperated systems. Boyea’s major contribution lies in developing a Transformer-based surgeon-side trajectory prediction algorithm that enables long-horizon inference of operator intent during robot-assisted surgical training. This work addresses a critical challenge in teleoperated robotics: providing real-time, context-aware assistance that adapts to the surgeon’s actions over extended procedures. By leveraging the Transformer architecture’s capacity for modeling sequential dependencies, his approach enhances haptic guidance, making surgical training more intuitive and effective. His most-cited paper (2023) has garnered 7 citations, reflecting growing interest in data-driven methods for surgical skill acquisition. Boyea’s work bridges the gap between artificial intelligence and practical surgical education, offering a pathway toward safer, more efficient training paradigms. His research holds promise for reducing errors and improving outcomes in minimally invasive surgery, positioning him as an emerging voice in the integration of predictive models with robotic assistance.