My name is Hangil Noah Hong and I am an undergraduate researcher in the field of quantitative psychology.
I would like to be addressed as Noah.
My current interests lie in the intersection of Bayesian statistics, longitudinal data analysis, and psychometrics.
I am preparing for graduate school applications (Fall 2026) in Statistics, Biostatistics, and/or Quantitative Psychology.
The following are some of my interests:
・Bayesian Statistics: e.g. Bayesian hierarchical modeling and Bayesian Nonparametrics
・Longitudinal data analysis: Analysis of longitudinal data from behavioral scinece, neuroscience, and biomedical science using mixed-effects models, state space models, FDA and other machine learning algorithms.
・Broad Interests: Decision Theory, Causal Inference and Spatial Statistics.
I am currently carrying out research applying Gaussian Process regression to longitudinal data in behavioral science, under the guidance of Prof. Kazuya Fujita at the University of Tsukuba, Japan.
I previously completed a research project on multilevel modeling, extending mixed-effects location–scale models and analyzing PISA 2022 data within this framework, under the supervision of Prof. Kazuhiro Yamaguchi at the university.
I have also completed another four-months research project on single-case experiment design, leveraging hierarchical interrupted time-series analysis using R and Stan.
Although my degree is in psychology, I have completed 40+ credits of coursework from the department of Mathematics and Engineering at the University of Tsukuba. Unfortunately, the university does not currently offer double majors or minors. I will be graduating with a B.A. in Psychology.
Coursework:
Mathematics: Calculus, Linear Algebra, Topology, Measure Theory, Probability Theory, Differential Equations, Vector Calculus, Applied Mathematics (Fourier Analysis & Laplace Transform).
Statistics and Data Science: Mathematical Statistics, Applied Probability, Psychological Data Analysis (GLM & GLMM), Psychometrics (Item Response Theory), Econometrics (Causal Inference Methods for Economics and Panel Data Analysis).
Languages & Tools:
Languages (Spoken):
English (professional proficiency), Japanese (fluent), Korean (conversationally fluent)