Degrees
- Ph.D. Applied Mathematics
- University of Colorado, Boulder, Boulder, Colorado, United States
- On hidden structures in contaminated symbolic data
- B.S. Mathematics
- University of Arizona, Tucson, Arizona, United States
- B.S. Molecular and Cellular Biology
- University of Arizona, Tucson, Arizona, United States
Interests
Research
Probability theory (mostly discrete), random graphs, differential privacy, asymptotic statistics, applied statistics, computational/mathematical biology, biostatistics, statistical physics, biophysics. Current working with international collaborators on differential privacy of federated machine learning methods, Bayesian network modeling of joint DNA methylation, multidisciplinary application of mixture-based techniques to control FDR, and maximal couplings in mixed Poisson models.
Courses
2026-27 Courses
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Intro to Statistical Computing
DATA 375 (Fall 2026) -
Theory of Probability
MATH 464 (Fall 2026)
2025-26 Courses
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Theory of Probability
MATH 464 (Fall 2025)
Scholarly Contributions
Journals/Publications
- Pearson, A., & Lladser, M. E. (2023). On latent idealized models in symbolic datasets: unveiling signals in noisy sequencing data. Journal of Mathematical Biology, 87(2), 26.
- Kightley, E. P., Pearson, A., Evans, J. A., & Bortz, D. M. (2018). Fragmentation of biofilm-seeded bacterial aggregates in shear flow. European Journal of Applied Mathematics, 29(6), 1062--1078.
- Coyne, A. N., Siddegowda, B. B., Estes, P. S., Johannesmeyer, J., Kovalik, T., Daniel, S. G., Pearson, A., Bowser, R., & Zarnescu, D. C. (2014). Futsch/MAP1B mRNA Is a Translational Target of TDP-43 and Is Neuroprotective in a Drosophila Model of Amyotrophic Lateral Sclerosis. The Journal of Neuroscience, 34(48), 15962--15974.
Proceedings Publications
- Pearson, A., & Lladser, M. E. (2023). Hidden independence in unstructured probabilistic models. In 34th International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA 2023).
