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About

Alistair Weld is a rising researcher at the forefront of surgical robotics, with a focus on advancing autonomous and collaborative systems for minimally invasive procedures. His work centers on three key areas: soft-tissue tracking, robotic ultrasound imaging, and surgical instrument pose estimation. Weld’s major contributions include leading the **SurgT challenge** (2023, 23 citations), which established a benchmark for soft-tissue trackers in robotic surgery, and the **SurgRIPE challenge** (2025, 5 citations), which set a new standard for surgical robot instrument pose estimation—a critical step toward autonomous task execution. In neurosurgery, he introduced a novel framework for safe, collaborative robotic ultrasound tissue scanning (2024, 13 citations), addressing long-standing barriers to intraoperative ultrasound adoption by integrating image interpretation with physical scanning. Weld also developed a deep regression method coupling spatial-frequency features with image synthesis for robot-assisted endomicroscopy (2022, 4 citations), and pioneered a technique to identify visible tissue in intraoperative ultrasound (2023, 3 citations), enhancing visuotactile feedback during scanning. His work is shaping the future of safe, intelligent robotic assistance in surgery.