Youssef Mohamed

Papers

Total Citations

H-Index

About

Youssef Mohamed is a researcher at the intersection of affective computing, human-robot interaction, and socially-aware AI. His work focuses on enabling machines to perceive and interpret human emotional and social states in real-world settings. Mohamed’s most cited paper, “Automatic Frustration Detection Using Thermal Imaging” (2022, 12 citations), addresses a critical challenge in seamless human-robot interaction: reliably detecting frustration when traditional RGB imaging fails. This work has practical implications for creating more responsive and empathetic robotic systems. More recently, Mohamed introduced the concept of *social embeddings* (2024), a novel approach that leverages large language models to generate compact, semantics-preserving mathematical representations of social situations. This foundational work opens new avenues for machines to understand nuanced social contexts, moving beyond simple emotion detection. Though early in his career, Mohamed’s contributions demonstrate a clear trajectory toward building AI that can navigate the complexities of human social life, with potential applications in assistive robotics, mental health monitoring, and socially intelligent agents.