Ahmed Alhomoud
Papers
2
Total Citations
7
H-Index
2
About
Ahmed Alhomoud is a rising researcher at the intersection of trustworthy artificial intelligence and industrial automation, with a focus on visual intelligence and autonomous systems security. His most-cited work, "Optimized trustworthy visual intelligence model for industrial marble surface anomaly detection" (2025, 5 citations), introduces a novel framework that ensures model reliability and transparency in detecting surface defects during manufacturing. This contribution addresses critical challenges in quality control by embedding trustworthiness into visual AI, enabling more robust and explainable anomaly detection for industrial applications. Alhomoud also explores adversarial vulnerabilities in autonomous driving, as demonstrated in his paper "LiDAR point cloud transmission: Adversarial perspectives of spoofing attacks in autonomous driving" (2025, 2 citations), where he investigates how spoofing attacks can compromise LiDAR-based perception systems. His work highlights the importance of securing sensor data pipelines against adversarial manipulation. Though early in his career, Alhomoud’s research bridges practical industrial needs with cutting-edge AI safety, positioning him as a promising voice in trustworthy machine learning and cyber-physical system resilience.
Research Focus
Key Achievements
Top Papers
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