Ph.D. Student
Hi! My name is Bumjun Park. I am from Seongnam, South Korea, and grew up between Seongnam and Madison, Wisconsin. I am currently a doctoral student in Biostatistics at the University of Washington. I am a proud Wisconsin Badger, holding a degree in Statistics and certificates in Mathematics and Economic Analytics. I am a native speaker of English and Korean.
I am advised by Professor Professor Eardi Lila, working to advance functional data analysis methods for neuroimaging and biomarkers in Alzheimer's disease. My broader academic interests include spatial and environmental statistics, functional data analysis, and network modeling.
I also do research with Professor Amy Willis on developing phylogenetic modeling methods to assist in allergen detection from pollen samples. And with Professor Jon Wakefield, I am investigating Bayesian methods of modeling mortality data, addressing the challenges of data missingness to produce more robust estimates of child mortality.
International Department
BS in Statistics
Certificate in Mathematics, Economic
Analytics
Ph.D. Student in Biostatistics
Per-and polyfluoroalkyl substances (PFAS) are synthetic chemicals that are increasingly being detected in groundwater. The negative health consequences associated with human exposure to PFAS make it essential to quantify the distribution of PFAS in groundwater systems. Mapping PFAS distributions is particularly challenging because a national patchwork of testing and reporting requirements has resulted in sparse and spatially biased data. In this analysis, an Inhomogeneous Poisson Process (IPP) modeling approach is adopted from ecological statistics to continuously map PFAS distributions in groundwater across the contiguous United States. The model is trained on a unique dataset of 8,910 PFAS groundwater measurements, using combined concentrations of two PFAS analytes. The IPP model predictions are compared with results from random forest models to highlight the robustness of this statistical modeling approach on sparse datasets. This analysis provides a new approach to not only map PFAS contamination in groundwater but also prioritize future sampling efforts.