Samantha Kleinberg

Associate Professor

School: School of Engineering and Science

Department: Computer Science

Building: Gateway Center

Room: S322

Phone: (201) 216-5614

Fax: (201) 216-8249



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


Health Informatics

Artificial intelligence + cognition

Institutional Service
  • MS Advising committee Member
Professional Societies
  • Cognitive Science Society Member
  • AAAI Member
  • AMIA – American Medical Informatics Association Member
  • ACM – Association for Computing Machinery Member
Selected Publications
  1. Marsh, J. K.; Zheng, M.; Nickerson, J. V.; Kleinberg, S. (2019). Making bad choices: The influence of causal diagrams on decision making. Psychonomic Society Annual Meeting.
  2. M, M.; Marsh, J. K.; Kleinberg, S. (2019). The Role of Causal Information and Perceived Knowledge in Decision-Making. Cognitive Science Society Annual Meeting.
  1. Kleinberg, S.; Kleinberg, S. (2019). Time and Causality across the Sciences. Cambridge University Press.
Book Chapter
  1. Kleinberg, S. (2019). An Introduction to Time and Causality. Time and Causality Across the Sciences. New York, NY: Cambridge University Press.
Conference Proceeding
  1. 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.
  2. Mirtchouk, M.; Srikishan, B.; Kleinberg, S. (2021). Hierarchical Information Criterion for Variable Abstraction. Machine Learning for Healthcare.
  3. Kleinberg, S.; Marsh, J. K. (2021). It’s Complicated: Improving Decisions on Causally Complex Topics. CogSci.
  4. 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).
  5. Hameed, H.; Kleinberg, S. (2020). Comparing Machine Learning Techniques for Blood Glucose Forecasting Using Free-living and Patient Generated Data. Machine Learning for Healthcare.
  6. 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.
  7. 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).
  8. Zheng, M.; Kleinberg, S. (2019). Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series. Machine Learning for Healthcare.
  9. 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..
  10. 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. Zheng, M.; Marsh, J. K.; Nickerson, J.; Kleinberg, S. (2020). How causal information affects decisions. Cognitive Research: Principles and Implications (1 ed., vol. 5).
  2. 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).
  3. Kleinberg, S. (2020). On the use and abuse of Hill's viewpoints on causality: a commentary on Hill's 1972 ``The environment and disease: association or causation?''. Observational Studies (vol. 6, pp. 17--19).
  4. 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).
  5. 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).