Samantha Kleinberg

• PhD (2010) New York University (Computer Science)

• 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

Abstract

1. M, M.; Marsh, J. K.; Kleinberg, S. (2019). The Role of Causal Information and Perceived Knowledge in Decision-Making. Cognitive Science Society Annual Meeting.

Book

1. Kleinberg, S.; Kleinberg, S. (2019). Time and Causality across the Sciences. Cambridge University Press.

Book Chapter

Conference Proceeding

1. Gomez, L. A.; Toye, A.; Hum, R. S.; Kleinberg, S. (2022). Simulating Health Time Series by Data Augmentation. Black in AI NeurIPS Workshop.
2. Kleinberg, S.; Alay, E.; Marsh, J. K. (2022). Absence Makes the Trust in Causal Models Grow Stronger. Proceedings of the 44th Annual Meeting of the Cognitive Science Society (CogSci).
3. Marsh, J. K.; Coachys, C.; Kleinberg, S. (2022). The Compelling Complexity of Conspiracy Theories. Proceedings of the 44th Annual Meeting of the Cognitive Science Society (CogSci).
4. Lu, C.; Reddy, C. K.; Chakraborty, P.; Kleinberg, S.; Ning, Y. (2021). Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare. IJCAI.
5. Mirtchouk, M.; Srikishan, B.; Kleinberg, S. (2021). Hierarchical Information Criterion for Variable Abstraction. Machine Learning for Healthcare.
6. Mirtchouk, M.; Kleinberg, S. (2021). Detecting Granular Eating Behaviors From Body-worn Audio and Motion Sensors. 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (pp. 1--4).
7. Kleinberg, S.; Marsh, J. K. (2021). It’s Complicated: Improving Decisions on Causally Complex Topics. CogSci.
8. Hameed, H.; Kleinberg, S. (2020). Comparing Machine Learning Techniques for Blood Glucose Forecasting Using Free-living and Patient Generated Data. Machine Learning for Healthcare.
9. Hameed, H.; Kleinberg, S. (2020). Investigating potentials and pitfalls of knowledge distillation across datasets for blood glucose forecasting. Proceedings of the 5th Annual Workshop on Knowledge Discovery in Healthcare Data.
10. Kleinberg, S.; Marsh, J. K. (2020). Tell me something I don't know: How perceived knowledge influences the use of information during decision making. Proceedings of the 42nd Annual Meeting of the Cognitive Science Society (CogSci).
11. Zheng, M.; Kleinberg, S. (2019). Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series. Machine Learning for Healthcare.
12. Yavuz, T. T.; Claassen, J.; Kleinberg, S. (2019). Lagged Correlations among Physiological Variables as Indicators of Consciousness in Stroke Patients. AMIA Annual Symposium Proceedings. Washington D.C..
13. Mirtchouk, M.; McGuire, D. L.; Deierlein, A. L.; Kleinberg, S. (2019). Automated Estimation of Food Type from Body-worn Audio and Motion Sensors in Free-Living Environments. Machine Learning for Healthcare.

Journal Article

1. Kleinberg, S.; Marsh, J. K. (2023). Less is more: information needs, information wants, and what makes causal models useful. Cognitive Research: Principles and Implications (vol. 8).
2. Gomez, L. A.; Toye, A. A.; Hum, R. S.; Kleinberg, S. (2023). Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation.. Journal of diabetes science and technology (pp. 19322968231181138).
3. Gomez, L. A.; Shen, Q.; Doyle, K.; Vrosgou, A.; Velazquez, A.; Megjhani, M.; Ghoshal, S.; Roh, D.; Agarwal, S.; Park, S.; Claassen, J.; Kleinberg, S. (2023). Classification of Level of Consciousness in a Neurological ICU Using Physiological Data.. Neurocritical care (1 ed., vol. 38, pp. 118-128).
4. Huang, J.; Yeung, A.; Armstrong, D.; Battarbee, A.; Cuadros, J.; Espinoza, J.; Kleinberg, S.; Mathioudakis, N.; Swerdlow, M.; Klonoff, D. (2023). Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. Journal of Diabetes Science and Technology (1 ed., vol. 17, pp. 224-238).
5. Thomas, D. M.; Kleinberg, S.; Brown, A. W.; Crow, M.; Bastian, N. D.; Reisweber, N.; Lasater, R.; Kendall, T.; Shafto, P.; Blaine, R.; Smith, S.; Ruiz, D.; Morrell, C.; Clark, N. (2022). Machine learning modeling practices to support the principles of AI and ethics in nutrition research.. Nutrition & diabetes (1 ed., vol. 12, pp. 48).
6. Korshakova, E.; Marsh, J. K.; Kleinberg, S. (2022). Health Information Sourcing and Health Knowledge Quality: Repeated Cross-sectional Survey.. JMIR formative research (9 ed., vol. 6, pp. e39274).
7. Zhang, P.; Fonnesbeck, C.; Schmidt, D.; White, J.; Kleinberg, S.; Mulvaney, S. (2022). Understanding Barriers to Self-Management Using Machine Learning and Momentary Assessment in Youth with Diabetes: An Observational Study. JMIR mHealth and uHealth (3 ed., vol. 10).
8. Zheng, M.; Marsh, J. K.; Nickerson, J.; Kleinberg, S. (2020). How causal information affects decisions. Cognitive Research: Principles and Implications (1 ed., vol. 5).
9. Zheng, M.; Marsh, J. K.; Nickerson, J.; Kleinberg, S. (2020). How causal information affects decisions.. Cognitive research: principles and implications (1 ed., vol. 5, pp. 6).
10. Zheng, M.; Ni, B.; Kleinberg, S. (2019). Automated Meal Detection from CGM Data Through Simulation and Explanation. JAMIA (12 ed., vol. 26, pp. 1592--1599).
11. Zheng, M.; Claassen, J.; Kleinberg, S. (2018). Automated Identification of Causal Moderators in Time-Series Data.. Proceedings of machine learning research (vol. 92, pp. 4-22).