We aim to improve human health, through the development of artificial intelligence methods. Most of these problems come back to the question of why things happen or how they change, so we focus on causal inference and time series data. We look at both clinical data as well as data generated outside of hospitals and aim to support both medical providers and patients in their decision making. Key application areas include stroke and diabetes. We are also working on devices that can automatically measure food intake, using body-worn sensors.
We received the Homer R. Warner Award for our work on identifying new indicators for consciousness in NICU patients.
We received an NIH R01, NSF Smart & Connected Health grant, and NSF III grant that will support developing generalizable methods to harness the power of patient generated data, improving shared decision-making by combining computing and cognitive science, and transforming how we evaluate and communicate the output of AI/ML.
New article on our research working toward fully automated dietary monitoring.
We are grateful for the support of multiple sponsors, including NSF, NIH, and the James S. McDonnell Foundation.