Samantha Kleinberg

I am primarily motivated by trying 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 my Health and AI Lab focuses 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.

See lab website: http://www.healthailab.org/

Farber Chair Professor, Computer Science September 2024 - present
Farber Chair Associate Professor, Computer Science September 2023 - August 2024
Associate Professor, Computer Science September 2018-August 2023
Assistant Professor, Computer Science September 2012-August 2018

Selected recent publications:
J. D. Pleuss, A. L. Deierlein, and S. Kleinberg. Estimating Days Needed for Dietary
Assessment in Pregnancy: A Modeling Study. The American Journal of Clinical Nutrition, 123(2), 2026.

E. Korshakova and S. Kleinberg. Evaluating Causal and Non-Causal Text Messages
to Promote Physical Activity in Adults: A Randomized Pilot Study. JMIR Form Res,
9, 2025.

S. Kleinberg and J. K. Marsh. Where the Women Are: Gender Imbalance in Computing and Faculty Perceptions of Theoretical and Applied Research. IEEE Access,
2025.

L. A. Gomez, J. Claassen, and S. Kleinberg. Causal Inference for Time Series
Datasets with Partially Overlapping Variables. Journal of Biomedical Informatics,
2025.

Y. Shen, E. Choi, and S. Kleinberg. Predicting Postprandial Glycemic Responses
With Limited Data in Type 1 and Type 2 Diabetes. Journal of Diabetes Science and
Technology, 2025.

V. Cheung, C. Leone, D. Lagnado, and S. Kleinberg. Causal and Counterfactual
Reasoning about Gradual and Abrupt Events. In Proceedings of the 46th Annual
Meeting of the Cognitive Science Society (CogSci), 2025.

A. A. Toye, A. Celik, and S. Kleinberg. Benchmarking Missing Data Imputation
Methods for Time Series Using Real-World Test Cases. In Conference on Health,
Inference, and Learning (CHIL), 2025.

S. Kleinberg, J. D. Pleuss, and A. L. Deierlein. Food Records Show Daily Varia-
tion in Diet During Pregnancy: Results From the Temporal Research in Eating,
Nutrition, and Diet during Pregnancy (TREND-P) Study. The Journal of Nutrition,
154(12):3780–3789, 2024.

J. K. Marsh, O. Asan, and S. Kleinberg. Perceived Penalties for Sharing Patient
Beliefs with Healthcare Providers. Medical Decision Making, 44(6):617–626, 2024.

S. Kleinberg and J. K. Marsh. Less is More: Information Needs, Information Wants,
and What Makes Causal Models Useful. Cognitive Research: Principles and Implica-
tions, 8, 2023.

Health Informatics, Causal Inference