Aryana Arsham

Assistant ProfessorData Science

Aryana Arsham is Assistant Professor of Data Science at Goucher College. She earned her Ph.D. in Statistics from the University of Maryland, Baltimore County in 2019. She then completed her post-doctoral training at the National Cancer Institute. Her research interests are in computational statistics and data analysis.

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Publications

Machine learning techniques for the prediction of indoor gamma-ray dose rates–Strengths, weaknesses and implications for epidemiology. Journal of Environmental Radioactivity, 282, 107595. (2025). Kendall GM, Appleton JD, Chernyavskiy P, Arsham A, Little MP.

Classifying Different Types of Solar-Wind Plasma with Uncertainty Estimations Using Machine Learning. Solar Physics, 299(9):131. (2024). Narock T, Pal S, Arsham A, Narock A, Nieves-Chinchilla T.

Smoothing Lexis diagrams using kernel functions: A contemporary approach. Statistical Methods in Medical Research, 32(9), 1799-1810. (2023). Rosenberg PS, Filho AM, Elrod J, Arsham A, Best AF, Chernyavskiy P.

Cost‐effectiveness analysis under multiple effectiveness outcomes: A probabilistic approach. Statistics in Medicine, 42(22), 3936-3955. (2023). Arsham A, Bebu I, Mathew T.

Alternative stopping rules to limit tree expansion for random forest models. Scientific Reports, 12(1), 15113. (2022). Little MP, Rosenberg PS, Arsham A.

Effects of stopping criterion on the growth of trees in regression random forests. The New England Journal of Statistics in Data Science, 1-16. (2022). Arsham A, Rosenberg PS, Little MP.

A Bivariate Regression-Based Cost-Effectiveness Analysis. Journal of Statistical Theory and Practice, 16(2). (2022). Arsham A, Bebu I, Mathew T.

Summary of Radiation Research Society Online 66th Annual Meeting, Symposium on “Epidemiology: Updates on epidemiological low dose studies,” including discussion. International Journal of Radiation Biology, 97(6), 866–873. (2021). Milder CM, Kendall GM, Arsham A, Schollnberger H, Wakeford R, Cullings HM, Little MP.

Conference Presentations

Machine learning techniques as a means of improving dosimetry in studies of indoor gamma-ray dose rates – strengths, weaknesses and implications for epidemiology
M.P. Little, J.D. Appleton, P. Chernyavskiy, A. Arsham A, G.M. Kendall
Radiation Research Society's 70th Annual Meeting (September 2024)

Effects of stopping criterion on the growth of trees in regression random forests with application to cancer epidemiology
M.P. Little, P.S. Rosenberg, A. Arsham
Joint Statistical Meetings (August 2023)

Probabilistic metrics for cost-effectiveness analysis under multiple effectiveness measures
A. Arsham, I. Bebu, T. Mathew
42nd Annual North American Meeting (October 2020)

Machine Learning methods used to model UK background gamma ray data and associated low dose childhood cancer risk
A. Arsham, R. Wakeford, M.P. Little, G.M. Kendall
Radiation Research Society’s 66th Annual Meeting (October 2020)

Effects of stopping criterion in the growth of trees in regression random forests
A. Arsham, P.S. Rosenberg, M.P. Little
Joint Statistical Meetings 2020 (August 2020)