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About

Alessia Saggese is a prolific researcher at the intersection of computer vision, deep learning, and social robotics, with a particular focus on human-centered AI systems. Her work spans face and voice analysis, action recognition, and the deployment of intelligent perception on edge devices and robotic platforms. Among her most significant contributions is her development of multi-task learning frameworks capable of simultaneously recognizing gender, age, ethnicity, and emotion — from both visual and audio modalities — enabling efficient inference on resource-constrained hardware. Her 2022 paper on edge-based multi-task learning has already garnered 56 citations, reflecting its practical relevance across digital signage, surveillance, and robotics applications. Complementing this, her knowledge distillation approach to age estimation (35 citations) demonstrated how lightweight models can rival heavyweight architectures through intelligent training strategies. Saggese has also made meaningful strides in social robotics, designing architectures for personalized human–robot interaction, museum guide robots, and retail automation systems. Her emotion recognition and skeleton-based action recognition research further underscores her commitment to building machines that genuinely understand human behavior. With over 200 cumulative citations and contributions spanning industrial, domestic, and public environments, her research is shaping the future of intelligent, socially aware robotic systems.