I am primarily interested in the budding intersection between philosophy and statistics. This encompasses concerns of algorithmic bias, fairness metrics, data privacy, and interpretable methods. In general, I have termed this intersection moral machine learning and has inspired a new course (see STOR 390 in my teaching page).

Secondarily, I am also interested in high-dimensional data integreation and inference, compartmental models in epidemiology, and statistical pedagogy.

Publications

  1. Ackerman, A. Purposeful Bias: Algorithmic Bias under Ideal Data Conditions. IEEE Access. (2026). doi
  2. Ackerman A., Zhang Z., Hannig J., Prothero J., Marron JS. Multifaceted Neuroimaging Data Integration via Analysis of Subspaces. Psychometrika. (2025) 1-26. doi
  3. Ackerman, A. Moral Machine Learning: Teaching a Course at the Intersection of Applied Statistics and Moral Philosophy. Journal of Statistics and Data Science Education (2025), 1–23. doi 1
  4. Prothero, J.; Jiang, M.; Hannig, J. ; Tran-Dinh Q.; Ackerman A.; Marron, J.S. Data integration via analysis of subspaces (DIVAS). TEST (2024). doi
  5. Ackerman, A.; Martin, B.; Tanisha, M.; Edoh, K.; Ward, J.P. High-Dimensional Contact Network Epidemiology. Epidemiologia (2023), 4, 286-297. doi

Preprints

  1. The Hypothesis Interval: A Pedagogical Distinction for Inference on Proportions (submitted to the Journal of Statistics and Data Science Education)

Software 2

  1. DIVAS associated with 4.
  2. Hypothesis Intervals associated with Preprint 1.

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  1. This paper was awarded a graduate student paper award at the Ethics Across the Curriculum Conference. ↩

  2. Only software with a user-facing function or set of functions are listed. Code for reproducing results is given in the associated paper. ↩