Researches

Applying AI/ML and statistical methods for predicting biological age and healthspan using wearable data

Applying SHAP (SHapley Additive exPlanations) method to identify factors influencing biological age using wearable data

Enhancing digital phenotyping through the application of AI/ML and statistical models to demonstrate using wearable, time-series data

Using AI/ML (CNN, LSTM, RF, DT, etc.) and statistical models (cosinor, SSA) to longitudinal, time-series physical activity data to classify different diseases

Development of digital, multi-modal, and non-invasive inflammatory biomarkers to improve early diagnosis and patient-centered disease monitoring for systemic inflammation

Addressing the unmet needs and advancing personalized therapies of cancer patients utilizing multi-modal, real-world data