Jaewoo Park

Education

• Ph.D. Statistics, Pennsylvania State University, 2019 (Thesis advisor: Dr. Murali Haran)

• B.A. Economics and Applied Statistics, Yonsei University, 2013

Employment

• Assistant Professor, Department of Applied Statistics, Yonsei University, September 2019-

Research Interest

• Statistical computing, computational methods for intractable likelihoods

• Bayesian deep learning, Gaussian process emulation

• Modeling high-dimensional and non-Gaussian spatio-temporal data

• Statistical methods for infectious disease research

Publications

*: corresponding author, ^: students supervised

• Lee, H.^, Oh, D.^, Choi, S., and Park, J.* (2024) Bayesian Kriging Approaches for Spatial Functional Data. submitted. 

• Kim, J., Lee, B., Min, C., Park, J., and Ryu, K. (2024) Density Estimation-based Stein Variational Gradient Descent. submitted. 

• Yi, S.^, Kim, M.^, Park, J., Jeon, M., and Jin, IH. (2024) Impacts of Innovation School System in Korea: A Latent Space Item Response Model with Neyman-Scott Point Process. submitted. 

• Cho, D.^, Chang, W., and Park, J. (2024) Fast Compartment Model Calibration using Annealed and Transformed Variational Inference. submitted. 

• Jeon, Y.^, Chang, W., Jeong, S., Han, S., and Park, J.* (2024) A Bayesian Convolutional Neural Network-based Generalized Linear Model. submitted.

• Kang, H.B., Jung, Y.J.^, and Park, J.* (2024) Fast Bayesian Functional Regression for Non-Gaussian Spatial Data. Bayesian Analysis. 19(2), 407-438.

• Park, J.*, Yi, S.^, Chang, W., and Mateu, J. (2023) A Spatio-Temporal Dirichlet Process Mixture Model for Coronavirus Disease-19. Statistics in Medicine. 42(30), 5555-5576.

• Lee, B.S., and Park, J.* (2023) A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Datasets. Technometrics, 65(1), 105-116.

• Lee, J., Kim, SH., Kim, Y.^, Park, J., Park, GE.,  and Kang, BJ. (2022) Radiomics nomogram: Prediction of 2-year disease-free survival in young age breast cancer. Cancers, 14(18), 4461. 

• Park, J., Chang, W., and Choi, B. (2022) An Interaction Neyman-Scott Point Process Model for Coronavirus Disease-19. Spatial Statistics, 47, 100561.

• Park, J., Jeon, Y.^, Shin, M., Jeon, M., and Jin, IH. (2022) Bayesian Shrinkage for Functional Network Models, with Applications to Longitudinal Item Response Data. Journal of Computational and Graphical Statistics. 31(2), 360-377.

• Park, J.*, and Lee, S.^ (2022) A Projection-based Laplace Approximation for Spatial Latent Variable Models. Environmetrics, 33(1), e2703. (R package: fastLaplace)

• Park, J., Schweinberger, M., and Jin, IH. (2022) Bayesian Model Selection for High-Dimensional Doubly-Intractable Posterior Distributions with Applications to Psychometrics. Computational Statistics and Data Analysis. 165, 107325.

• Park, J.*, and Haran, M. (2021) Reduced-dimensional Monte Carlo Maximum Likelihood for Latent Gaussian Random Field Models. Journal of Computational and Graphical Statistics, 30(2),  269-283.

• Park, J.* (2021) Bayesian Indirect Inference for Models with Intractable Normalizing Functions. Journal of Statistical Computation and Simulation,  91(2), 300-315.

• Park, J., and Haran, M. (2020) A Function Emulation Approach for Doubly Intractable Distributions. Journal of Computational and Graphical Statistics, 29(1), 66-77.

• Jung, M.C., Park, J., and Kim, S. (2019) Spatial Relationships between Urban Structure and Air Pollution in Korea. Sustainability, 11(2), 476.

• Goldstein, J., Park, J., Haran, M., Liebhold, A., and Bjornstad, O.N. (2019) Quantifying Spatio-Temporal Variation of Invasion Spread. Proceedings of the Royal Society B, 286(1894), 20182294.

• Park, J., and Haran, M. (2018) Bayesian Inference in the Presence of Intractable Normalizing Functions. Journal of the American Statistical Association, 113(523), 1372-1390.

• Park, J., Goldstein, J., Haran, M., and Ferrari, M. (2017) An Ensemble Approach to Predicting the Impact of Vaccination on Rotavirus Disease in Niger. Vaccine, 35(43), 5835-5841. 

Grants

• Institute of Information & Communication Technology Planning & Evaluation (RS-2023-00259934), Development and Operation of Data Science Research and Education Program, Co-I, July 2023 - December 2027

• Basic Research Laboratory, National Research Foundation of Korea (RS-2023-00217705), Analysis for High-dimensional Infectious Disease Data Based on AI and Bayesian Machine Learning, Co-I, June 2023 - June 2026

• Young Scientist Grants, National Research Foundation of Korea (2020R1C1C1A0100386811), Computational Methods for High-Dimensional Spatial Data, PI, March 2020 - February 2025

• Yonsei University Future-Leading Research Initiative (2019-22-0194), PI, September 2019 - September 2022

Teaching

Yonsei University 

• Functional Data Analysis (STA 6320), Fall 2019, 2020, Spring 2022

• Spatio-Temporal Data Analysis (STA 6241), Spring 2020, Fall 2021 

• Mathematical Statistics (STA6010), Spring 2021

• Deep Learning (STA 3140), Fall 2019- 2021, Spring 2022

• Bayesian Statistics (STA3105), Fall 2022

Pennsylvania State University

• Elementary Probability (STAT 318), Spring 2017.

• Statistical Concepts and Reasoning (STAT 100), Summer 2016.

• Elementary Statistics (STAT 200), Summer 2015.

Students

I am looking for self-motivated Ph.D. students who are interested in the following areas: (1) computational methods for spatio-temporal data, (2) statistical methods for functional data, and (3) Bayesian deep learning algorithms. If you are interested in joining my research group, feel free to send me an email at jwpark88@yonsei.ac.kr. Please also see the list of current and past graduate students.