Wael Farag

American University of the Middle East, Gulf University for Science & Technology

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Total Citations

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About

Wael Farag is a researcher working at the intersection of artificial intelligence, reinforcement learning, and robotics, with a growing focus on AI evaluation methodologies and fuzzy logic systems. His most recognized contribution lies in applying Deep Deterministic Policy Gradient (DDPG) algorithms to robotic arm navigation, demonstrating how reinforcement learning can enable autonomous agents to dynamically reach continuously changing target locations — work that has garnered 9 citations and been revisited across multiple publications, underscoring its significance to the robotics and autonomous systems community. This line of research positions Farag as a practical contributor to policy gradient methods in real-world robotic control scenarios. More recently, Farag has expanded his scope to address the rapidly evolving landscape of generative AI, co-authoring a 2025 study that evaluates ChatGPT-generated content using bipolar generalized fuzzy hypergraphs — a novel approach that tackles uncertainty and vagueness inherent in large language model outputs. This work reflects his versatility in bridging advanced mathematical frameworks with contemporary AI challenges. With a cumulative citation profile reflecting steady scholarly engagement, Farag represents an emerging voice in intelligent systems research, making meaningful contributions to both autonomous robotics and AI trustworthiness evaluation.