Current Projects
We aim to understand and prevent dysglycemia by uncovering how lifestyle, behavior, and physiology interact to shape metabolic risk. Prediabetes (PD) and type 2 diabetes (T2D) affect 122 million Americans, yet remain critically underdiagnosed—81% and 23% of those with PD and T2D, respectively, are unaware of their condition.
Our work develops innovative, scalable strategies for early detection and prevention by leveraging digital data from everyday consumer devices—smartphones, smartwatches, and wearables—to identify subtle, real-world signatures of glycemic risk and resilience.
Using wearable sensors and digital biomarkers, we aim to detect pain, stress, and complications earlier—helping clinicians personalize care and improve patient recovery.
We identify and model biological, behavioral, and environmental determinants of disease across large, diverse cohorts. These projects often integrate multi-omic, clinical, and contextual data (e.g., from mobile and wearable devices) to uncover novel predictors of health outcomes and to advance precision prevention strategies.
We develop equitable digital health methods so wearable technologies benefit all populations. Our work builds inclusive datasets and robust algorithms, and studies structural factors—such as access and retention—that shape who is represented in real-world digital health data.
We are developing computational methods, open platforms, and analytic frameworks that enable high-quality biosignal analysis and reproducible digital biomarker research.
These projects build the methodological and infrastructure foundation that supports scalable, trustworthy digital health discovery across diverse data modalities and studies.
