Samantha Kleinberg
Professor and Farber Chair Professor
Charles V. Schaefer, Jr. School of Engineering and Science
Department of Computer Science
Education
- PhD (2010) New York University (Computer Science)
Research
Causality
Health Informatics
Artificial intelligence + cognition
Health Informatics
Artificial intelligence + cognition
General Information
See lab website: http://www.healthailab.org/
Experience
Farber Chair Professor, Computer Science Farber Chair Associate Professor, Computer Science Associate Professor, Computer Science
Assistant Professor, Computer Science September 2024 - present
September 2023 - August 2024
September 2018-August 2023
September 2012-August 2018
Assistant Professor, Computer Science September 2024 - present
September 2023 - August 2024
September 2018-August 2023
September 2012-August 2018
Professional Service
- NSF NSF Review Panel
- Conference on Health, Inference, and Learning Track chair, CHIL
- KDD Causality workshop, IJCAI, JDST, CogSci Conference and journal reviewing, conference program committee
- Chair, Heuristics and Causality in the Sciences conference
- NSF NSF review panel
- University of California, MRPI Grant reviewer
Professional Societies
- ASN – American Society for Nutrition Member
Selected Publications
Selected recent publications:
S. Kleinberg and J. K. Marsh. Where the Women Are: Gender Imbalance in Com-
puting 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.
S. Kleinberg and J. K. Marsh. Where the Women Are: Gender Imbalance in Com-
puting 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.
Courses
Health Informatics, Causal Inference