Research Projects

Our lab focuses on several research directions, primarily representation learning, behavioral machine learning, machine learning for healthcare, and "healthy" machine learning.

We list a selection of representative work here, with a complete listing in Publications.


Representation Learning

Representation learning has prompted great advances in machine learning; for example, the lower dimensional, qualitatively meaningful representations of imaging datasets learned by convolutional neural networks. Healthcare data lacks such obviously natural structures, and investigations into appropriate representations should include multi-source integration, and learning domain appropriate representations.


Behavioral ML

Much of life happens outside a clinical environment. Phenotyping is an important goal in healthcare, and behavioral data provides an ongoing way for devices to collect continuous non-invasive data and provide meaningful classifications or alerts when the patient is in need of clinical attention. We target the development and deployment of machine learning on behavioral data, working to understand the variance of human behavior's impact on health.


Healthcare ML

While healthcare is an inherently data-driven field, most clinicians operate with limited evidence guiding their decisions. Randomized trials estimate average treatment effects for a trial population, but many day-to-day clinical decisions are not based on high-quality randomized control trials. We develop machine learning models to understand the use of important interventions, and the development of outcomes.


Healthy ML

Machine learning models are powered by data, and bias can be encoded by data itself or modeling choices. With the expanding impact of machine learning in sensitive areas like healthcare, we work to identify the potential for bias in data, learning and deployment.