Aleks Attanasio
Papers
6
Total Citations
507
H-Index
5
About
Aleks Attanasio is a leading researcher at the intersection of surgical robotics and artificial intelligence, whose work is defining the next generation of autonomous minimally invasive surgery. His primary research areas include surgical autonomy, soft continuum robotics, and computer vision for medical applications. Attanasio’s most impactful contribution is his seminal 2020 review on autonomy in surgical robotics, which has amassed 235 citations and provides the foundational framework for categorizing levels of robotic autonomy in the operating room. He has also pioneered novel approaches for soft tentacle robots using magnetic actuation (92 citations) and developed deep learning methods for underwater image enhancement (86 citations). His feasibility study on autonomous tissue retraction (76 citations) demonstrates a practical pathway toward semi-autonomous surgical assistance, while his work on spatio-temporal U-Nets for tissue segmentation advances real-time scene understanding. Attanasio’s open-source motion planning framework further accelerates research by providing accessible tools for the community. Through these contributions, he is systematically addressing the core challenges of perception, planning, and control that stand between current robotic assistants and truly autonomous surgical systems.
Research Focus
Key Achievements
Top Papers
- 1Autonomy in Surgical Robotics235 citations · 2020
- 2
- 3A Deep Learning Approach for Underwater Image Enhancement86 citations · 2017
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- 6