Seoul National University
Mar. 2014-Aug. 2021 | Seoul, Republic of Korea
Thesis: On the Nonparametric Maximum Likelihood Estimation Method for Simultaneous Mean Vector Estimation and its Applications.
Advisor: Junyong Park, Ph.D. | HDMT Lab
HiNT Lab | High-dimensional Nonparametric Theory & Learning
Hoyoung Park
Developing empirical Bayes, nonparametric, and high-dimensional methods for modern scientific data at HiNT Lab, with applications in proteomics, biomedicine, and complex data integration.
Profile
Statistical methodology for data-rich science.
HiNT Lab
High-dimensional Nonparametric Theory & Learning
The lab name reflects three core pillars: high-dimensional data, nonparametric methodology, and statistical theory, while hint captures the inferential clues that careful statistical learning extracts from complex data.
I am an Assistant Professor in the Department of Statistics at Sookmyung Women's University. Before joining Sookmyung, I was a postdoctoral fellow at the National Institutes of Health, working on epidemiologic and proteomic studies of heart failure.
My work combines asymptotic theory, empirical Bayes ideas, high-dimensional estimation, classification, clustering, and multiple testing. I am especially interested in methods that adapt to heteroscedasticity, latent structure, and information borrowed across related domains.
Education
Academic training in statistics and mathematics.
Doctoral training in nonparametric maximum likelihood estimation and high-dimensional inference, grounded in mathematical foundations.
Sogang University
Mar. 2011-Feb. 2014 | Seoul, Republic of Korea
Graduated Summa Cum Laude.
2022-Present
Assistant Professor at Sookmyung Women's University
2024-Present
Early Career Advisory Board, Journal of Multivariate Analysis
NIH
Postdoctoral training in epidemiology, proteomics, and heart failure phenomics
News
Lab updates and student highlights
Recent student presentations, awards, joining milestones, and alumni updates from HiNT Lab.
Chaewon Song received a Poster Presentation Encouragement Award at the Korean Statistical Society meeting.
M.S. student Chaewon Song presented "Sharpening Variance Estimation: An Empirical Bayes Approach under Mean-Variance Dependence" at Seoul National University.
Yurim No presented at the Korean Statistical Society meeting.
Undergraduate intern Yurim No presented "Mirror Statistics-Guided Variable Selection for Enhanced Clustering in High-Dimensional Data" at Seoul National University.
Euichae Lee presented at the Korean Statistical Society meeting.
Undergraduate intern Euichae Lee presented "Empirical Bayesian Estimation of Prior Distributions Using NPMLE for Predicting Batting Averages in KBO" at Seoul National University.
Seungyeon Oh received an Oral Presentation Encouragement Award at the Korean Statistical Society meeting.
Alumna Seungyeon Oh gave an oral presentation, "Nonparametric Linear Discriminant Analysis for High Dimensional Matrix-Valued Data," at the 2025 Summer Korean Statistical Society meeting held at The-K Hotel Gyeongju.
Daeun Lee joined HiNT Lab.
Daeun Lee joined as an undergraduate intern.
Euichae Lee joined the M.S. program.
Euichae Lee continued in HiNT Lab as an M.S. course student after undergraduate research training.
Jimin Hong joined HiNT Lab.
Jimin Hong joined as an undergraduate intern.
Yurim No joined HiNT Lab.
Yurim No joined as an undergraduate intern.
Seungyeon Oh gave an oral presentation at the Korean Statistical Society meeting.
M.S. student Seungyeon Oh presented "Nonparametric Mean and Variance Adaptive Classification Rule for High-Dimensional Data with Heteroscedastic Variances" at Sungshin Women's University.
Chaewon Song joined HiNT Lab.
Chaewon Song joined as an M.S. course student.
Seungyeon Oh graduated from the M.S. program.
Seungyeon Oh continued as a Ph.D. student in the Department of Statistics at Seoul National University.
Euichae Lee joined HiNT Lab.
Euichae Lee joined as an undergraduate intern.
Research
Methods that adapt, borrow strength, and scale.
The research program links statistical theory with applied data problems where dimensionality, heterogeneity, and dependence are central rather than incidental.
Empirical Bayes and NPMLE
Adaptive shrinkage and nonparametric maximum likelihood estimation for mean vectors, variance heterogeneity, and matrix-valued data.
MTP and FDR Control
Multiple testing procedures for high-dimensional bioinformatics, including robust empirical likelihood and heteroscedastic settings.
Clustering and Data Integration
Semi-supervised clustering and integrative analysis for proteomic, epidemiologic, and biomedical cohort studies.
Transfer Learning and Domain Adaptation
Statistical strategies for transporting information across related populations, platforms, domains, and high-dimensional tasks.
High-Dimensional Classification
Discriminant analysis and covariance-aware rules for vector, matrix, and tensor-valued data.
Biomedical Applications
Proteomic risk scores, heart failure phenomics, survival outcomes, and biomarker-driven epidemiologic studies.
Grants
Research funding
Unified Nonparametric Empirical Bayes Framework for High-Dimensional Inference with Unknown Heteroscedasticity
Principal investigator project developing unified nonparametric empirical Bayes methods for high-dimensional inference under unknown heteroscedastic variances.
Incorporating Structural Characteristics of Data to Enhance Multivariate Methodologies
Collaborative project on using structural information in data to improve multivariate statistical methods.
High-dimensional Parameter Estimation Method and Its Applications
High-dimensional parameter estimation with applications to classification and clustering analysis of high-dimensional data.
Selected Work
Recent publications and manuscripts
†First author; *Corresponding author.
Longitudinal subcortical volume changes and their correlations with multiple PET and fluid biomarkers in dominantly inherited Alzheimer's disease
Choo, I.†*, Park, H., Gordon, B. A., Bateman, R. J., Bateman, R., Daniels, A. J., ... and Lopera, F. The Journal of Prevention of Alzheimer's Disease.
Nonparametric Linear Discriminant Analysis for High Dimensional Matrix Valued Data
Oh, S.†, Park, S., and Park, H.* Statistical Data Analysis and Data Mining.
High dimensional discriminant rules with shrinkage estimators of the covariance matrix and mean vector
Kim, J.†, Park, J., and Park, H.* Journal of Statistical Planning and Inference.
Nonparametric Mean and Variance Adaptive Classification Rule for High-Dimensional Data
Oh, S.† and Park, H.* Statistical Data Analysis and Data Mining.
A robust false discovery rate procedure using empirical likelihood with a fast algorithm
Park, H.† and Park, J.* Journal of Statistical Computation and Simulation.
Nonparametric inference for interval data using kernel methods
Park, H.†, Loh, J.-M. and Jang, W.* Journal of Nonparametric Statistics.
Effectiveness of a CGM-Based Personalized Diet Program on Weight Management and Blood Glucose
Park, H.†, Kim, Y., Jeon, K. S., Kim, M.-J., Dong, S.-Y., and Shin, G. D.* Submitted manuscript.
A Nonparametric Empirical Bayes Approach to Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy
Park, H.†, Kim, J.-H., and Lee, W.* Submitted manuscript.
An Adaptive Empirical Bayes Confidence Interval for Mean Estimation under Mean-Variance Dependence
Song, C.† and Park, H.* Submitted manuscript.
Semiparametric empirical Bayes method for Normal mean estimation
Park, H.† and Park, J.* Submitted manuscript.
Presentations
Talks and presentations
Selected recent talks by Hoyoung Park from the CV, followed by HiNT Lab student presentation highlights.
Selected Recent Talks by Hoyoung Park
Vector-Matrix-Tensor Discriminant Analysis via Nonparametric Empirical Bayes
Sungshin Women's University, School of Mathematics, Statistics and Data Science. Oral presentation.
Vector-Matrix-Tensor Discriminant Analysis via Nonparametric Empirical Bayes
Korean Data & Information Science Society. Oral presentation.
Leveraging Empirical Bayes for High-Dimensional Data: Techniques and Applications Across Diverse Fields
International Day of Women in Statistics and Data Science. Oral presentation.
Semi-supervised clustering method and its application to the proteomics data of heart failure patients
Hanyang University, Department of Mathematics. Oral presentation.
Semi-supervised clustering method and its application to the proteomics data of heart failure patients
Joint Statistical Meetings, Toronto, Canada. Oral presentation.
Earlier presentations are available in the full CV.
Student Presentations
Sharpening Variance Estimation: An Empirical Bayes Approach under Mean-Variance Dependence
Chaewon Song. Korean Statistical Society meeting, Seoul National University. Poster presentation; Poster Presentation Encouragement Award.
Mirror Statistics-Guided Variable Selection for Enhanced Clustering in High-Dimensional Data
Yurim No. Korean Statistical Society meeting, Seoul National University. Poster presentation.
Empirical Bayesian Estimation of Prior Distributions Using NPMLE for Predicting Batting Averages in KBO
Euichae Lee. Korean Statistical Society meeting, Seoul National University. Poster presentation.
Nonparametric Linear Discriminant Analysis for High Dimensional Matrix-Valued Data
Seungyeon Oh. Korean Statistical Society meeting, The-K Hotel Gyeongju. Oral presentation; Oral Presentation Encouragement Award.
Nonparametric Mean and Variance Adaptive Classification Rule for High-Dimensional Data with Heteroscedastic Variances
Seungyeon Oh. Korean Statistical Society meeting, Sungshin Women's University. Oral presentation.
People
Students and alumni
HiNT Lab members organized by current role and alumni status.
M.S. Students
Chaewon Song (송채원)
2024.09-2026.08
Thesis: An Adaptive Empirical Bayes Confidence Interval for Mean Estimation under Mean-Variance Dependence.Euichae Lee (이의채)
2026.02-Present
Undergraduate Interns
Yurim No (노유림)
2025.06-Present
Jimin Hong (홍지민)
2025.07-Present
Daeun Lee (이다은)
2026.07-Present
Alumni
Seungyeon Oh (오승연)
2022.09-2024.08
Current: Ph.D. Student, Department of Statistics, Seoul National University. Thesis: Nonparametric mean and variance adaptive classification rule for high-dimensional data with heteroscedastic variances.Euichae Lee (이의채)
2024.07-2026.02
Continued in HiNT Lab as an M.S. course student.Teaching
Courses across theory, computation, and applied statistics.
Undergraduate and graduate courses taught at Sookmyung Women's University.
Mathematical Statistics I
2023-2026 Spring
Lecture Notes (2026 ver)Data Mining
2023-2025 Fall
Lecture Notes (2025 ver)Nonparametric Statistics
2025 Fall
Lecture Notes (2025 ver)Linear Model Theory
2023-2026 Spring
Lecture Notes (2026 ver)Contact