Projects

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

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

The project aims to measure craving and provide interventions to patients with opioid use disorders. We combine mobile sensing, non-behavioral sensing, as well as fMRI imaging to characterize craving responses.

This project focuses on measuring and predicting seizure events using non-invasive multimodal sensor data.

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 intervention and delivery timing using machine learning

This project aims to develop systems and machine learning models to measure physical and mental health risks and provide interventions for Alzheimer patient spousal caregivers.

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

We develop and test systems to predict health and wellbeing and provide personalized sleep recommendations 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 analyze how people work using multimodal data and develop an intelligent assistant to help workers engage in their tasks and improve wellbeing.

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.