Qingmei Xiao

Papers

Total Citations

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About

Qingmei Xiao’s research centers on affective computing and human-robot interaction, with a particular focus on modeling dynamic emotional states in dialogue systems. Her most-cited work, “Emotion predicting method based on emotion state change of personae according to the other's utterances” (2014, 4 citations), addresses a fundamental challenge in artificial intelligence: enabling machines to predict human emotional shifts during conversation. By proposing a framework that tracks how a persona’s internal emotional state evolves in response to another’s utterances, Xiao advanced the development of more empathetic and context-aware dialogue agents. This contribution is especially relevant for social robots and virtual assistants, where natural emotional understanding is critical. Although her citation count is modest, her work represents an early and thoughtful step toward bridging the gap between human emotional intuition and machine learning. Xiao’s research underscores the importance of temporal dynamics in emotion modeling, offering a foundation for subsequent studies in affective dialogue systems. Her approach continues to inspire researchers seeking to create AI that can engage in more human-like, emotionally responsive interactions.