I am primarily interested in data integration for high dimensional, network data. This can include both methodological work, such as 1., and application-driven work. My ongoing work is of this latter form, and involves applying DIVAS to Human Connectome Project data. I anticipate this work being submitted in this calendar year, but it has also inspired some exciting future methodological work on Jackstraw for DIVAS. As a coauthor on 1., I also help maintain the DIVAS github repository alongside Jack Prothero.

I am also quite interested in the budding intersection between philosophy and statistics. This is something I have termed, moral machine learning, and it is intended to encompass concerns such as algorithmic bias, data privacy, and interpretable methods. Thus far, this has manifest itself as a new course (see STOR 390 in my teaching page) at UNC and a pedagogical preprint reflecting on the preparation and delivery therein. However, I think there is far more to be said, both about best practices in making such a course more mainstream and in theoretical work surrounding the above issues.

Publications

1. Ackerman A., Zhang Z., Hannig J., Prothero J., Marron JS. Multifaceted Neuroimaging Data Integration via Analysis of Subspaces. Psychometrika. (2025) 1-26. doi
2. 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
3. Prothero, J.; Jiang, M.; Hannig, J. ; Tran-Dinh Q.; Ackerman A.; Marron, J.S. Data integration via analysis of subspaces (DIVAS). TEST (2024). doi
4. Ackerman, A.; Martin, B.; Tanisha, M.; Edoh, K.; Ward, J.P. High-Dimensional Contact Network Epidemiology. Epidemiologia (2023), 4, 286-297. doi

Preprints

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