Bio
I am a Ph.D. candidate in Quantitative Methods, Measurement, and Statistics at the University of California, Merced. I firmly believe that scientific discovery is a dynamic process, driven by the continuous updating of scientific knowledge through experiential learning. This perspective closely aligns with the philosophical paradigm of Bayesian inference. 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 (Expected)
Utrecht University, The Netherlands
M.Sc. in Methodology and Statistics, 2021
Sungkyunkwan University, South Korea
B.A. in Psychology, 2019
Program of Research
(1) Bayesian methodology for theory construction and statistical modeling
I develop and evaluate Bayesian methods for latent variable models, including structural equation models, latent (e.g., piecewise) growth models, and mixture models.
(2) Practical and computational approaches to missing data science
I examine the consequences of missing data for statistical modeling and develop multiple imputation techniques using modern data science tools, including machine learning and deep learning algorithms.
(3) Application, dissemination, and collaborative research in quantitative methods
I communicate and collaborate with methodological and applied researchers by writing tutorial or applied papers and proposing new psychometric or modeling frameworks for longitudinal, categorical, or social network data. I have also authored multiple statistics tutorials for \(\texttt{R}\), \(\texttt{JASP}\), and \(\texttt{jamovi}\) aimed at applied researchers.
Selected Awards
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