Papers
30
Total Citations
560
H-Index
14
About
Mouloud Ourak is a leading researcher at the intersection of robotics, medical imaging, and machine learning, with a focus on advancing flexible surgical and interventional robots. His work addresses critical challenges in minimally invasive procedures, particularly in cardiovascular, ophthalmic, and fetal surgery. Ourak has pioneered the use of deep learning for shape sensing and hysteresis compensation in robotic catheters, enabling more precise and safer navigation within the body. His highly cited paper on "3D Printing of Small‐Scale Soft Robots with Programmable Magnetization" (78 citations) demonstrates his innovative approach to fabricating soft robots with complex locomotion capabilities for confined spaces. Ourak has also made significant contributions to retinal vein cannulation, developing combined OCT distance and FBG force sensing needles that have been validated in vivo. His work on deep learning-based monocular placental pose estimation for collaborative robotics in fetoscopy (28 citations) addresses critical needs in treating twin-to-twin transfusion syndrome. With over 400 total citations across his publications, including a comprehensive review on machine learning in flexible surgical robots (2024), Ourak continues to shape the future of intelligent, adaptive surgical systems that enhance patient outcomes.
Research Focus
Key Achievements
Top Papers
- 13D Printing of Small‐Scale Soft Robots with Programmable Magnetization78 citations · 2023
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- 3Shape Sensing of Flexible Robots Based on Deep Learning45 citations · 2022
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- 9Direct Visual Servoing Using Wavelet Coefficients26 citations · 2019
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