Health and AI Lab

Translating Data Into Actionable Knowledge

Principal Investigator: Associate Professor Samantha Kleinberg

About Us

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.

Research

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.

• Causality

• Biomedical informatics

• Automated dietary monitoring

• Cognition

Funding

We are grateful for the support of multiple sponsors, including NSF, NIH, and the James S. McDonnell Foundation.