Bumjun Park

About Me

Bumjun Park

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.

My academic interests include spatial and environmental statistics, functional data analysis, and network and graphical modeling. I work with Professor Jing Ma at the Fred Hutchinson Cancer Center on studying data-driven methods of detecting communities and networks within microbiota, along with robust approaches to statistical inference.

I also do research with Professor Eardi Lila on advancing functional data analysis methods in analyzing neuroimaging and neurological biomarker data, with the goal of improving our understanding of disorders such as stroke and Alzheimer’s disease. And with Professor Jon Wakefield, I am investigating Bayesian methods of modeling mortality data in the presence of missingness.

March 2015- February 2018

Hankuk Academy of Foreign Studies

International Department
September 2018 - May 2023

University of Wisconsin-Madison

BS in Statistics
Certificate in Mathematics, Economic Analytics
September 2023 -

University of Washington

Ph.D. Student in Biostatistics

Publications

Published

Statistical Mapping of PFOA and PFOS in Groundwater Throughout the Contiguous United States

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.

B. Park, H. Kang, and C. Zahasky. (2024) Statistical Mapping of PFOA and PFOS in Groundwater Throughout the Contiguous United States. Environmental Science and Technology.

(manuscript) (code) (supplementary figures)