Papers
20
Total Citations
427
H-Index
12
About
Thomas Low is a pioneering researcher at the intersection of surgical robotics, teleoperation, and autonomous systems. His work spans three primary domains: telesurgical systems for extreme and remote environments, human-robot interaction in medical contexts, and machine learning-driven surgical automation. Low's early contributions include a biologically inspired hexapedal robot utilizing electroactive elastomer artificial muscles (2001, 41 citations), demonstrating his longstanding interest in novel robotic actuation. He gained significant recognition for his investigations into teleoperated surgery in challenging settings, including an undersea environment (2009, 73 citations) and foundational studies on how communication time-delay affects surgical accuracy (2014, 50 citations). More recently, Low has pushed the frontier of surgical automation, developing deep learning-based calibration systems that enable robots to exceed human speed and consistency in peg transfer tasks (2022, 37 citations), and introducing semi-autonomous frameworks to mitigate communication delays during telesurgery (2023). His DESK dataset (2019) has provided the surgical robotics community with essential resources for training machine learning models. With over 350 cumulative citations, Low's career reflects a sustained commitment to making robotic surgery safer, more accessible, and increasingly autonomous.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9A Comparative Study for Telerobotic Surgery Using Free Hand Gestures17 citations · 2016
- 10