Jamal Bentahar
Concordia University, Khalifa University of Science and Technology
Papers
5
Total Citations
119
H-Index
4
About
Jamal Bentahar is a leading researcher at the intersection of artificial intelligence, multi-agent systems, and medical robotics. His work primarily focuses on developing intelligent, autonomous systems for target localization and robotic-assisted medical procedures. Bentahar’s major contributions include pioneering the use of Multi-Agent Deep Reinforcement Learning with Proximal Policy Optimization for target localization, as demonstrated in his highly cited 2022 paper (68 citations) and his 2023 work on demonstration cloning (34 citations). These methods enable teams of mobile sensing agents—such as UAVs and robots—to collaboratively and efficiently identify target locations, surpassing traditional stationary sensor approaches. In medical robotics, Bentahar has advanced cardiac ultrasound robotic systems with novel AI-powered robust interaction force control (2024, 10 citations) and deep feature ultrasound image-based visual servoing (2025, 5 citations), enhancing both image quality and patient safety during cardiac examinations. His work on motion planning using reinforcement learning (2024) further extends the capabilities of autonomous robotics. With over 119 citations across his top papers, Bentahar’s research is shaping the future of intelligent, autonomous systems in both defense and healthcare.
Research Focus
Key Achievements
Top Papers
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