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CLASS:PUBLIC
CREATED:20260917T030421Z
DESCRIPTION:Thesis Title: Personalized Machine Learning for Affective Health: Advancing Mobile Sensing and Digital Phenotyping\n\n--------------------------------------------------\n\nAbstract: The rise of wearable devices and smartphones has enabled continuous, real-world monitoring of human physiology and behavior — opening the door to proactive, personalized healthcare. Yet, one major challenge remains: people’s physiological and emotional responses to affective states vary widely, making one-size-fits-all models unreliable. This dissertation introduces a personalized multimodal AI framework that integrates biosignals, behavioral patterns, and contextual data to predict affective and physiological outcomes in daily life. Using three complementary studies — Emognition (emotion recognition), BanAware (substance-use craving prediction), and CardioMate (stress-induced blood pressure spike prediction) — the work demonstrates how personalization improves both accuracy and interpretability across controlled and real-world environments. By combining self-supervised representation learning, attention-based temporal modeling, and interpretable feature analyses, the thesis bridges behavioral and physiological domains, showing how personalized digital phenotyping can transform mental and cardiovascular health monitoring. The findings advance the path toward adaptive, transparent, and person-specific digital medicine.\n
DTEND;TZID=Pacific/Honolulu:20251111T023000Z
DTSTAMP:20260917T030421Z
DTSTART;TZID=Pacific/Honolulu:20251111T000000Z
LAST-MODIFIED:20260917T030421Z
LOCATION:https://ucsf.zoom.us/my/pwashing
PRIORITY:5
SEQUENCE:0
SUMMARY;LANGUAGE=en-us:PhD Defense: Ali Kargarandehkordi
TRANSP:OPAQUE
UID:178965026144799web-support-l@lists.hawaii.edu
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