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SUMMARY:Concept-based Interpretable Models for Affective Computing Applica
 tions - Xinyu Li\, University of Glasgow.
DTSTART:20251023T130000Z
DTEND:20251023T134000Z
UID:TALK239728@talks.cam.ac.uk
CONTACT:Hatice Gunes
DESCRIPTION:Talk abstract: In today’s era of intelligent connectivity\, 
 Affective Computing (AC) plays a vital role in enabling AI systems to unde
 rstand and respond to human emotions. However\, a key challenge persists: 
 how can we design models that are both accurate and explainable? This talk
  explores how concept-level interpretability can be integrated into model 
 design to make AC systems not only intelligent but also transparent. We in
 troduce a family of concept-based AC frameworks that advance explainable a
 nd efficient affective AI across a range of applications\, from facial exp
 ression recognition and conversational engagement estimation to video-base
 d mental health assessment. Together\, these works outline a pathway towar
 d interpretable\, trustworthy\, and deployable affective AI for real-world
  impact.\n\nSpeaker bio: Xinyu Li is a final year doctoral student at the 
 Behaviour AI Lab\, at the School of Computing Science\, University of Glas
 gow. His research focuses on Affective Computing\, Explainable Artificial 
 Intelligence(XAI)\, and Multimodal Machine Learning\, with an emphasis on 
 developing interpretable and trustworthy human-centered AI systems.
LOCATION:FW011 - William Gates Building
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