I joined Bucknell after finishing my Ph.D. in 2012.
- Carnegie Mellon University, Ph.D. in Statistics
- Carnegie Mellon University, M.S. in Statistics
- State University of New York at Fredonia, B.S. in Mathematics/Secondary Education
My research interests are broadly focused in machine learning and data science, but I predominately work in statistical clustering. Clustering is considered an exploratory technique and method of unsupervised learning, with the goal of classifying groups of unlabelled observations. I work on both theoretical and applied problems in clustering, with applications in educational research, social justice and public health. I’m additionally interested in the quickly growing field of sports analytics.
I teach a range of statistics courses from the introductory to advanced levels. I also contribute to the core curriculum with a Foundation Seminar titled Sports, Statistics, and Society and an Integrated Perspectives course, co-taught with Professor Brian King titled Introduction to Data Science. I strive to empower my students to successfully learn and appreciate statistics and data science. Further, I work to build a strong community among students and ensure that my classes are inclusive spaces.
Sweet, T., Flynt, A., and Choi, D. (2019), "Clustering ensembles of social networks." Network Science, 7(2), 141–159.
Flynt, A., and Dean, N. (2019), "Growth mixture modeling with measurement selection." The Journal of Classification, 36(1), 3–25.
Flynt, A., Dean, N., and Nugent, R. (2019), "sARI: a soft agreement measure for class partitions incorporating assignment probabilities." Advances in Data Analysis and Classification, 13(1), 303–323.