Publications

* indicates mentored student or postdoctoral fellow

  1. Practical considerations when designing an online learning algorithm for an app-based mHealth intervention
    Gonzalez, R.*, Abbott, M.*, Hummel, S., Nallamothu, B., Dorsch, M., and Dempsey, W.
    Contemporary Clinical Trials 2026
  2. Incorporating Auxiliary Variables to Improve the Efficiency of Time-Varying Treatment Effect Estimation
    Shi, J.*, Wu, Z., and Dempsey, W.
    Journal of the American Statistical Association 2025
  3. A meta-learning method for estimation of causal excursion effects to assess time-varying moderation
    Shi, J.*, and Dempsey, W.
    Biometrics 2025
  4. Selective inference for sparse graphs via neighborhood selection
    Huang, Y.*, Panigrahi, S., and Dempsey, W.
    Electronic Journal of Statistics 2025
  5. Non-Stationary Latent Auto-Regressive Bandits
    Trella, A., Dempsey, W., Doshi-Velez, F., and Murphy, S.
    Reinforcement Learning Journal 2025
  6. A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data
    Abbott, M. R.*, Dempsey, W. H., Nahum-Shani, I., Potter, L. N., Wetter, D. W., Lam, C. Y., and Taylor, J. M. G.
    Biometrics 2025
  7. A Continuous-Time Dynamic Factor Model for Intensive Longitudinal Data Arising from Mobile Health Studies
    Abbott, M. R.*, Dempsey, W. H., Nahum-Shani, I., Lam, C. Y., Wetter, D. W., and Taylor, J. M. G.
    Psychometrika 2025
  8. Data integration methods for micro-randomized trials
    Huch, E.*, Nahum-Shani, I., Potter, L., Lam, C., Wetter, D. W., and Dempsey, W.
    Biometrics 2025
  9. RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions
    Huch, E.*, Shi, J.*, Abbott, M. R.*, Golbus, J. R., Moreno, A., and Dempsey, W. H.
    In Advances in Neural Information Processing Systems 2024
  10. A latent variable approach to jointly modeling longitudinal and cumulative event data using a weighted two-stage approach
    Abbott, M.*, Nahum-Shani, I., Lam, C., Wetter, D., and Dempsey, W.
    Statistics in Medicine 2024
  11. Exploring the Big Data Paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS)
    Yang, Y.*, Dempsey, W., Han, P., Deshmukh, Y., Richardson, S., Tom, B., and Mukherjee, B.
    Science Advances 2024
  12. Node-Level Community Detection within Edge Exchangeable Models for Interaction Processes
    Zhang, Y.*, and Dempsey, W.
    Journal of the American Statistical Association 2024
  13. Recurrent Event Analysis in the Presence of Real-Time High Frequency Data via Random Subsampling
    Dempsey, W.
    Journal of Computational and Graphical Statistics 2023
  14. SMART Binary: New Sample Size Planning Resources for SMART Studies with Binary Outcome Measurements
    Dziak, J. J., Almirall, D., Dempsey, W., Stanger, C., and Nahum-Shani, I.
    Multivariate Behavioral Research 2024
  15. CataBEEM: Integrating Latent Interaction Categories in Node-wise Community Detection Models for Network Data
    Zhang, Y.*, and Dempsey, W.
    In Proceedings of the 40th International Conference on Machine Learning 2023
  16. Design of experiments with sequential randomizations on multiple timescales: the hybrid experimental design
    Nahum-Shani, I., Dziak, J., Venera, H.*, Pfammatter, A., Spring, B., and Dempsey, W.
    Behavior Research Methods 2024
  17. Addressing selection bias and measurement error in COVID-19 case count data using auxiliary information
    Dempsey, W.
    The Annals of Applied Statistics 2023
  18. Assessing time-varying causal effect moderation in the presence of cluster-level treatment effect heterogeneity and interference
    Shi, J.*, Wu, Z., and Dempsey, W.
    Biometrika 2023
  19. Kernel Deformed Exponential Families for Sparse Continuous Attention
    Moreno, A.*, Nagesh, S., Chatterjee, S., Wu, Z., Dempsey, W., and Rehg, J.
    In Advances in Neural Information Processing Systems 2022
  20. Optimal test allocation strategy for COVID-19
    Du, J., Beesley, L., Lee, S., Zhou, X., Dempsey, W., and Mukherjee, B.
    Statistics in Medicine 2021
  21. A Geometry-Driven Longitudinal Topic Model
    Wang, Y.*, Hougen, C., Oselio, B.*, Dempsey, W., and Hero, A.
    Harvard Data Science Review 2021
  22. Hierarchical edge exchangeable models for structured interaction networks
    Dempsey, W., Oselio, B.*, and Hero, A.
    Journal of the American Statistical Association 2021
  23. A Functional EM Algorithm for Panel Count Data with Missing Counts
    Moreno, A.*, Wu, Z., Wetter, D., Lam, C., Nahum-Shani, I., Dempsey, W., and Rehg, J.
    In Advances in Neural Information Processing Systems 2021
  24. Exchangeable Markov multi-state survival processes
    Dempsey, W.
    Statistica Sinica 2021
  25. A Statistical Framework for Modern Network Science
    Crane, H., and Dempsey, W.
    Statistical Science 2020
  26. The stratified micro-randomized trial design: Sample size considerations for testing nested causal effects of time-varying treatments
    Dempsey, W., Liao, P., Kumar, S., and Murphy, S. A.
    The Annals of Applied Statistics 2020
  27. Relational exchangeability
    Crane, H., and Dempsey, W.
    Journal of Applied Probability 2019
  28. Just-in-Time but Not Too Much: Determining Treatment Timing in Mobile Health
    Liao, P., Dempsey, W., Sarker, H., Hossain, S., al’Absi, M., Klasnja, P., and Murphy, S. A.
    Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2018
  29. Survival Models and Health Sequences
    Dempsey, W., and McCullagh, P.
    Lifetime Data Analysis 2018
  30. Edge exchangeable models for interaction networks
    Crane, H., and Dempsey, W.
    Journal of the American Statistical Association 2018
  31. Weak continuity of predictive distribution for Markov survival processes
    Dempsey, W., and McCullagh, P.
    Electronic Journal of Statistics 2017
  32. iSurvive: An Interpretable, Event-time Prediction Model for mHealth
    Dempsey, W. H., Moreno, A.*, Scott, C. K., Dennis, M. L., Gustafson, D. H., Murphy, S. A., and Rehg, J. M.
    In Proceedings of the 34th International Conference on Machine Learning 2017
  33. Randomised trials for the Fitbit generation
    Dempsey, W., Liao, P., Klasnja, P., Nahum-Shani, I., and Murphy, S. A.
    Significance 2015
  34. Multiresolution analysis on the symmetric group
    Kondor, R., and Dempsey, W.
    In Advances in Neural Information Processing Systems 2012

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