I joined Bucknell after finishing my PhD in 2012.
- State University of New York at Fredonia, B.S. Mathematics, Secondary Education
- Carnegie Mellon University, M.S. Statistics
- Carnegie Mellon University, Ph.D. Statistics
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 Technology 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.