Mohamed Chahine Ghanem
London Metropolitan University, Georgia Institute of Technology
Papers
2
Total Citations
12
H-Index
2
About
Mohamed Chahine Ghanem is at the forefront of autonomous robotics and surgical AI, pioneering methods that bridge intelligent navigation and precision medicine. His most impactful work introduces the Adaptive Hybrid PSO–APF algorithm, a breakthrough in path planning for next-generation autonomous robots that combines particle swarm optimization with artificial potential fields to achieve smooth, safe, and efficient navigation without human intervention. This paper has already garnered 10 citations since its 2025 publication, signaling its rapid influence on the field. In surgical robotics, Ghanem addresses the critical bottleneck of data scarcity with SuFIA-BC, a framework that generates high-quality demonstration data for visuomotor policy learning in surgical subtasks. By overcoming challenges like robot calibration errors and the difficulty of obtaining patient data, this work enables more reliable behavior cloning for dexterous manipulation in operating rooms. Ghanem’s dual focus on autonomous navigation and surgical skill acquisition positions him as a rising leader in embodied AI, with research that directly impacts real-world applications from factory floors to hospital suites.
Research Focus
Key Achievements
Top Papers
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- 2