Mohammad Fattahi Sani
University of Tabriz, Bristol Robotics Laboratory, University of the West of England
Papers
7
Total Citations
39
H-Index
4
About
Mohammad Fattahi Sani is a pioneering researcher at the intersection of neuroscience, robotics, and surgical technology. His work primarily focuses on bio-inspired robotic control, surgical skill analysis, and human-robot interaction. Sani’s most impactful contribution is his experimental study of reinforcement learning in mobile robots through a spiking neural network architecture modeled on the mammalian brain’s thalamo-cortico-thalamic circuitry (14 citations). This work, along with his research on unsupervised learning via Spike Timing Dependent Plasticity, demonstrates his commitment to translating neural mechanisms into robotic learning systems. In the surgical domain, Sani has made notable advances in tracking surgeons’ hand and finger movements during microsurgery and mapping them to tool motion using machine learning (6 citations). He has also contributed to the development of novel master controllers for the da Vinci surgical robot and evaluated force feedback for teleoperated systems. His work on real-time optimal trajectory planning using Generalized Regression Neural Networks further showcases his versatility. With a growing citation record, Sani is establishing himself as a key figure in neuromorphic robotics and intelligent surgical systems.
Research Focus
Key Achievements
Top Papers
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- 2Towards Finger Motion Tracking and Analyses for Cardiac Surgery8 citations · 2019
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