Bio
I am a Ph.D. candidate in Quantitative Methods, Measurement, and Statistics at the University of California, Merced. I will receive my doctorate by May 2026, with a dissertation titled Advanced Methods for Implementation of Bayesian Growth Mixture Modeling, funded by the American Psychological Association (APA) and the Society of Multivariate Experimental Psychology (SMEP).
My overarching research goal is to advance flexible modeling approaches for complex data in the social and behavioral sciences. I specialize in Bayesian inference, latent variable modeling, and missing data. With these specializations, I integrate quantitative, computational, and machine learning approaches to develop, evaluate, and implement innovative statistical methods that expand their applicability across diverse research contexts. More details can be found in the Program of Research.
As of October 2025, I have published 12 peer-reviewed journal articles, including 7 as first author, in top-tier methodological and applied journals. A complete list of my refereed journal publications can be found on the Articles page. For additional details on my academic training and experience, please see my curriculum vitae.
Education
University of California, Merced, USA
Ph.D. in Quantitative Methods, Measurement, and Statistics, 2026
Utrecht University, The Netherlands
M.Sc. in Methodology and Statistics (Cum Laude), 2021
Sungkyunkwan University, South Korea
B.A. in Psychology (Highest Honors), 2019
Program of Research
My program of research can be detailed with three lines.
(1) Bayesian Inference for Statistical Modeling
I advance Bayesian methodology for latent variable models, including structural equation models, growth curve models, and mixture models. My research examines Bayesian estimation and evaluation of these models and integrates computational methods for Bayesian regularization and Bayesian model averaging.
(2) Missing Data Analysis for Practical Applications
I examine the impact of missingness on latent variable modeling, including latent mediation analysis, growth curve models, and mixture models. I further develop computational, machine learning, and deep learning approaches for handling missing data within the framework of multiple imputation.
(3) Methodology-Driven Collaboration for Interdisciplinary Impact
I collaborate with researchers across disciplines to create synergies that translate methodological innovations into practice, generate novel applications, and foster contributions at the intersection of disciplines. My collaborations span applied areas involving the analysis of educational, psychological, and health data, as well as methodological areas such as educational measurement, psychometrics, social network analysis, and artificial intelligence.
Selected Awards
Outstanding Teaching Award
University of California, Merced, 2025
Psychological Sciences Dissertation Fellowship
University of California, Merced, 2025
APA Dissertation Research Award
American Psychological Association, 2025
SMEP Dissertation Research Grant
Society of Multivariate Experimental Psychology, 2025
Nomination for Open Science Award
Open Science Community Utrecht, 2024
Graduate Student Opportunity Program Fellowship
University of California, Merced, 2022–2023