Papers
2
Total Citations
71
H-Index
2
About
Pablo Mesejo is a leading researcher at the intersection of artificial intelligence, human-robot interaction, and forensic anthropology. His work primarily focuses on developing intelligent systems that enable more natural and adaptive human-robot collaboration, particularly through multimodal perception and reinforcement learning. Mesejo’s most cited paper, “Neural network based reinforcement learning for audio–visual gaze control in human–robot interaction” (2018, 65 citations), introduced a groundbreaking approach that allows robots to learn gaze behaviors from audio and visual cues, significantly enhancing their social engagement capabilities. More recently, he has pioneered the application of AI in forensic anthropology, as highlighted in his 2024 paper “Artificial intelligence in forensic anthropology: State of the art and Skeleton-ID project,” which outlines a novel framework for automated skeletal identification. This work bridges computer vision and forensic science, offering new tools for human identification in legal and humanitarian contexts. With a growing citation impact and a portfolio that spans robotics, machine learning, and forensic AI, Mesejo’s contributions are shaping both the technical and ethical dimensions of intelligent systems.
Research Focus
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Top Papers
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