Projects

Using behavioral, physiological, and weather data collected from wearables and mobile phones, we develop robust personalize models to predict future wellbeing (mood, stress, psychiatric conditions and health) with machine learning/ deep learning methods.

We train deep networks to automatically extract physiological and biobehavioral features from raw data collected by wearable/mobile sensors to help understand and improve people’s health and wellbeing.

Estimation of Human Circadian Phase

This project aims to develop mathematical/machine learning models to measure circadian rhythm using non-invasive physiological and behavioral sensors.

Design just-in time stress intervention and delivery timing using machine learning

Multi-modal sensory feedback for regulating alertness and relaxation

The project aims to design and validate multi-sensory stimulation to regulate alertness or relaxation.

Health and Wellbeing Measurement, Prediction, and Recommendation for shift workers

Combining sensing, modeling / data analysis and intervention modules, we design personalized dynamic feedback system for stress, mental health and sleep management and other health applications.

We build machine learning models for predicting schizophrenia patients' symptoms and clinical assessment and relapse events using mobile phone sensors. We also build machine learning models for detecting and predicting craving symptoms in patients with Opioid Use Disorder.

The project aims to measure and increase understanding of the interactions between living environment, residents’ daily behavior, and their health and well-being in daily life and related to adverse events using mobile sensing and crowd source assessment.

This project focuses on understanding, combining, and positively affecting emotional experience and psychological and physiological resilience processes in job seeking.