Papers
21
Total Citations
1,852
H-Index
13
About
Mehmet Turan is a pioneering researcher at the intersection of medical robotics, computer vision, and artificial intelligence, with a particular focus on miniaturized robotic systems for biomedical applications. His landmark 2015 survey on untethered mobile milli/microrobots — now boasting an impressive 870 citations — helped define the roadmap for next-generation minimally invasive medical devices, demonstrating how robots scaled to cellular dimensions could revolutionize healthcare by accessing previously unreachable regions of the human body. Turan's work on flexible strain sensors, cited over 317 times, further showcases his versatility across biomedical engineering domains. A significant portion of Turan's research centers on endoscopic capsule robots, where he has made substantial contributions to visual odometry, SLAM-based 3D mapping, deep learning-driven localization, and autonomous navigation within the gastrointestinal tract. His EndoSLAM dataset and associated unsupervised depth estimation framework have become important benchmarks in the field, accumulating over 240 citations since 2021. By integrating recurrent convolutional neural networks, reinforcement learning, and multi-modal sensor fusion into capsule endoscopy systems, Turan has consistently pushed the boundaries of intelligent, self-navigating medical robots — offering the prospect of earlier disease detection and more effective therapeutic interventions.
Research Focus
Key Achievements
Top Papers
- 1Biomedical Applications of Untethered Mobile Milli/Microrobots870 citations · 2015
- 2Parallel Microcracks-based Ultrasensitive and Highly Stretchable Strain Sensors317 citations · 2016
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- 8Learning to Navigate Endoscopic Capsule Robots29 citations · 2019
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