Bio
I am an Assistant Professor of Quantitative Psychology at the University of Oklahoma. 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 AI 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.
As of August 2026, I have published 14 peer-reviewed journal articles, including 9 as first author, in top-tier methodological and applied journals. A complete list of my refereed journal publications can be found on the Publications page. For additional details on my academic training and experience, 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 AI into Bayesian methodology.
(2) Methods for Complex Data
I develop and evaluate methods for analyzing complex social and behavioral data, with particular emphasis on missing data and longitudinal modeling. My research examines the performance of Bayesian methodology under challenging data settings and develops computational solutions, including Bayesian nonparametric multiple imputation for incomplete data in latent variable mixture models. Ongoing work extends this research using machine learning and deep learning to analyze emerging data types.
(3) Interdisciplinary Methodological Research
I collaborate with researchers across disciplines to translate methodological innovations into practice, generate novel applications, and inspire new methodological developments through real-world research problems. These collaborations span the educational, psychological, health, and medical sciences, as well as methodological areas including educational measurement, psychometrics, social network analysis, and artificial intelligence.