Health and AI Lab

Translating data into actionable knowledge

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.


Min Zheng defends his thesis

Min Zheng successfully defended his thesis, Individualized Causal Models for Assisting Real World Decision Making

Papers at AMIA and UbiComp 2017

Replicability, Reproducibility, and Agent-based Simulation of Interventions accepted to AMIA 2017

Recognizing Eating from Body-Worn Sensors: Combining Free-living and Laboratory Data accepted to IMWUT, and will be presented at UbiComp 2017

TaCitS at Stevens

HAIL hosts the highly interdisciplinary Time and Causality in the Sciences conference June 2017.

Two awards this spring

Mark received the Stephen L. Bloom Theoretical Computer Science Award! Samantha received the Provost's Early Career Award for Research Excellence.

Stevens covers our dietary monitoring work

New article on our research working toward fully automated dietary monitoring.

UbiComp 2016 Honorable Mention

Paper Automated Estimation of Food Type and Amount Consumed from Body-worn Audio and Motion Sensors received a best paper Honorable Mention award at UbiComp 2016.

Kavli fellow

Samantha was selected as a National Academy of Sciences Kavli Fellow and gave a talk on causality at the Kavli Frontiers of Science meeting at the National Academy of Sciences. Video here.


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