Health and AI Lab
Translating Data Into Actionable Knowledge
Principal Investigator: Associate Professor Samantha Kleinberg
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 aim to make use of observational data to gain insight into human health, and better prevent and treat disease. Our core research areas are thus methods for causal inference and explanation from complex data, discovery from observational biomedical data, and automated dietary monitoring.
Automated dietary monitoring
We are grateful for the support of multiple sponsors, including NSF, NIH, and the James S. McDonnell Foundation.