Bio
I am an Assistant Professor of Quantitative Psychology at the University of Oklahoma. I am also a Faculty Affiliate with the Data Institute for Societal Challenges (DISC), the Institute for Community and Society Transformation (ICAST), the TSET Health Promotion Research Center (HPRC), and the Center for the Study of Emerging Technologies (CSET). I completed a Ph.D. in Quantitative Methods, Measurement, and Statistics at the University of California, Merced in 2026, an M.Sc. in Methodology and Statistics (Cum Laude) at Utrecht University in 2021, and a B.A. in Psychology (Highest Honors) at Sungkyunkwan University in 2019.
My overarching research goal is to advance flexible modeling approaches for complex social and behavioral data. I specialize in Bayesian statistics, latent variable modeling, and methods for missing and longitudinal data. More recently, my research has expanded to emerging data types and the integration of artificial intelligence into quantitative methodology. To this end, I develop and evaluate innovative statistical methods with applications across diverse research contexts. More details can be found in the Program of Research.
A complete list of my peer-reviewed publications can be found on the Publications page. For additional information about my academic background and professional activities, please see my curriculum vitae.
Program of Research
My program of research is organized around three interconnected lines of inquiry.
(1) Bayesian Latent Variable Modeling
I advance Bayesian methodology for latent variable models, including structural equation models, latent mediation models, growth curve models, and mixture models. My research develops and evaluates Bayesian methods for estimation, model evaluation, regularization, model averaging, and prior specification to improve the flexibility, robustness, and interpretability of latent variable modeling. Current work extends these methods to increasingly complex latent variable models and explores the integration of artificial intelligence into Bayesian methodology.
(2) Bayesian Approaches to Complex Data
I develop and evaluate Bayesian methods for analyzing complex social and behavioral data. To date, this work has primarily focused on missing and longitudinal data, including Bayesian estimation and model fit evaluation for longitudinal attrition and Bayesian nonparametric multiple imputation in the presence of population heterogeneity. My research is expanding to emerging data types, including social network, intensive longitudinal, and text data, with work on Bayesian model averaging for exponential random graph models as one example.
(3) Interdisciplinary Methodological Research
I collaborate with researchers across disciplines to translate methodological innovations into practice, develop novel applications, and identify new methodological questions arising from real-world research problems. This work includes making Bayesian methods more accessible to broader research communities through tutorial articles and software development, and applying Bayesian methods to research in the health and medical sciences. Beyond my work in Bayesian methodology, my interdisciplinary collaborations span educational measurement, psychometrics, machine learning, and deep learning.