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, diabetes, and nutrition.
Samantha was invited to participate in the Dagstuhl Seminar "AI x Philosophy: Bridging Minds and Machines"
Our study finds that existing research may be underpowered due to insufficient days of dietary data collection.
We received a $778k grant from the John Templeton Foundation to unite computer science, philosophy, and psychology to address the fundamental question of token causality.
Our randomized pilot study found that text messages linking actions to outcomes led to greater increases in physical activity compared to non-causal messages.
If you feel judged by your doctor, our work shows you may be right. More coverage in Medical Economics. STAT Op-ed on why doctors should not expect patients to be medical experts.
Op-ed on why we should not ask AI to make life or death decisions. Reprinted in Salon.
Samantha became the Farber Chair Professor of Computer Science!
Op-ed on our obsession with health and fitness tracking and why it may be time for a data diet.
We are grateful for the support of multiple sponsors, including NSF, NIH and the John Templeton Foundation.