Health

Current Research Focus

My interest in health is at the intersection of machine learning, behavioral sciences, and medicine. We commonly use modern machine learning methods with a focused effort to improve healthcare outcomes. I founded and lead the Rice Digital Health Initiative.

Rice is located across from the world’s biggest medical center, Texas Medical Center, and it gives us a major advantage in conducting high-impact research. In my Scalable Health Labs, we are developing methods to uncover behavior-biology causal pathways and develop novel speech and vision AI for health. With our clinical partners, we have access to unique retrospective data, and we regularly conduct novel prospective trials.

In addition to ongoing research directions in mental health and diabetes, we have new projects in cardiology, Parkinson’s, and DBS-based interventions for bipolar disorder. Several new positions are available in the broad area of AI for health.

Our lab commonly performs innovations in methods and their novel applications in clinical applications. Some recent examples (full publication list at my Google Scholar page)

Mental Health: We are developing foundations for a trans-diagnostic approach to psychiatry and establishing new bio-behavioral dimensions. Notably, we have developed quantitative methods to measure a person’s in-person socialization behaviors using a wrist-worn audio band and deep learning-based speech analysis pipelines to extract privacy-preserving socialization parameters like in-person network size, conversation behaviors, dyadic versus multi-party conversations, and social ambiance.

Diabetes: We have developed new bio-behavioral markers, new analysis methods and developed unique interventions as part of the NSF ERC PATHS-UP. Following is a snapshot of our 8+ years of research

 

Past Research Directions

Pulmonary Conditions: Pulmonology was our first foray into health. That research led to our first spinoff, Cognita Labs, which has developed two FDA-approved products for the world’s first method to measure inhaler competence and a handheld lung function testing using the forced oscillometry technique. Our research on inhaler use taught us that human behavior should not be treated as a nuisance but as a necessary design requirement.

Retinal Imaging: We spent a few years developing a hand-held retinal imager, which could image the eye without dilation. That led to the second spinoff from the lab, OcuCheck.

Digital Gym:  As an ambitious project, we wanted to track every exercise performed in the Rice Gym, automatically. The project generated many ideas and one of them led to our third spinoff, CherryPick.