This project develops AI methods that transform wearable and physiological signals into reliable digital biomarkers. Research includes PPG quality assessment, heart-rate estimation, circadian patterns, sleep EEG, and interpretable analysis of longitudinal sensor data.

Selected Publications

• Reducing Interdataset Covariate Shift in Sleep EEG of Traumatic Brain Injured Humans and Mice Using Transfer Euclidean Alignment
• Circadian Rhythm of Heart Rate and Heart Rate Variability in Pregnancy
• Multitask Learning Approach for PPG Applications: Case Studies on Signal Quality Assessment and Physiological Parameters Estimation
• An LLM-Powered Agent for Physiological Data Analysis: A Case Study on PPG-Based Heart Rate Estimation
• Personalized Counterfactual Framework: Generating Potential Outcomes from Wearable Data