Data Science

Ambulatory Big Data Methodologies

Is the mundane significant? We seek to understand how subtle processes are associated with important outcomes. Small, daily behaviors can have large impacts on functioning and are important points of intervention for improving child and family mental health. In addition to collecting laboratory-based data, we collect data in families’ day-to-day lives to understand how family dynamics naturalistically unfold. Using smartphones and wearable devices, we generate ambulatory big data to track functioning in real time and real life.

Intensive Longitudinal Data Analysis

To study family dynamics, we collect intensive multimodal data and use novel computational methods for measuring, quantifying, and understanding interpersonal relationships. People are connected to each other through intricate and fast-moving patterns of behavior where information is communicated between people through a variety of modalities, including each person’s emotional state, language, tone of voice, physiological arousal, and body language. Longitudinal, cross-person, multimodal data such as these are highly complex. We apply state-of-the-art statistical models and develop new techniques and methodologies to quantify these patterns and processes.

Machine Learning and Mental Health

With the advent of wearable sensors and mobile technologies, vast amounts of data can now be collected relatively easily and inexpensively. However, methods for making sense of these vast amounts of data have lagged behind. Big data and machine learning are increasingly being used to learn patterns in such data to better understand psychological functioning and improve well-being. We apply and develop machine learning methods to automatically and passively detect and even predict interpersonal processes via wearable devices as they occur in daily life.