Amanda Song

Universidad Cristiana Autónoma de Nicaragua

Papers

Total Citations

H-Index

About

Amanda Song’s research lies at the compelling intersection of computer vision, cognitive science, and social perception, where she investigates how machines can learn to interpret faces the way humans do. Her work focuses on bridging the gap between objective facial attributes—such as gender, age, and expression—and the subjective, socially nuanced judgments people make every day, like trustworthiness, attractiveness, and friendliness. In her highly cited 2017 paper, “Learning to see faces like humans: modeling the social dimensions of faces” (12 citations), Song pioneered computational models that capture these complex social inferences, moving beyond traditional face recognition to embrace the rich, multidimensional nature of human perception. Her follow-up work, “Learning to see people like people” (5 citations), further explores how objective and subjective face-processing systems can be integrated, challenging the field to consider the social brain’s full repertoire. Though her citation counts are modest, Song’s contributions are notable for their conceptual ambition: she is helping to shape a new generation of AI that sees people not just as data points, but as social beings. Her research is essential reading for anyone interested in human-centered AI, affective computing, or the psychology of face perception.