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, Department of Statistics and Data Science,  Yonsei University, September 2019 - present

Research Interest

• Statistical computing, computational methods for intractable likelihoods

• Bayesian deep learning,  functional data analysis

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

• Statistical methods for infectious disease research

Publications

*: corresponding author, ^: students supervised

• Lee, K., Lin, L., Park, J., and Jeong, S. (2025) Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality. Submitted

• Lee, J. H.^, Kim, J.^, Lee, H.^, and Park, J.* (2025) A Delayed Acceptance Auxiliary Variable MCMC for Spatial Models with Intractable Likelihood Function. Submitted

• Lee, S.^, Chang, W., Mateu, J., Lee, H., and Park, J.* (2025) A Spatio-Temporal Dirichlet Process Mixture Model on Linear Networks for Crime Data. Submitted

• Lee, H.^, Kim, S.^, Kang, B., and Park, J.* (2025) A Stein Gradient Descent Approach for Doubly Intractable Distributions. Submitted

• Lee, H.^, Oh, D.^, Choi, S., and Park, J.* (2025) Bayesian Function-on-Function Regression for Spatial Functional Data. Bayesian Analysis. In press

• Li, Y., Tong, D.,  Jeon, Y.^, Lee, B.S., Park, J., Kondragunta, S., Zhang, X., Siripun, N., Song, S., Mehta, C., and Chen, J.Z.(2025) Using AOD and UVAI to Reduce the Uncertainties in Wildfire Emission and Air Quality Forecast. Journal of Geophysical Research: Atmospheres. 130(7), e2024JD041816  

• Haran, M. , Kang, B., and Park, J. (2025) Algorithms for Models with Intractable Normalizing Functions. Handbook of Markov chain Monte Carlo, 2nd edition

• Kim, J., Lee, B., Min, C., Park, J., and Ryu, K. (2025) Density Estimation-based Stein Variational Gradient Descent. Cognitive Computation. 17(5)

• Yi, S.^, Kim, M.^, Park, J., Jeon, M., and Jin, IH. (2025) Impacts of Innovation School System in Korea: A Latent Space Item Response Model with Neyman-Scott Point Process. Journal of the Royal Statistical Society, Series A. 188(3), 935-952

• Cho, D.^, Chang, W., and Park, J. (2025) Fast Computer Model Calibration using Annealed and Transformed Variational Inference. Journal of Computational and Graphical Statistics. 34(2), 474-487 (Winner of the 2022 Korean International Statistical Society Outstanding Student Paper Award)

• Kim, S.^, Cho, Y., Ki, H.^, Park, S.^, Oh, D.^, Lee, S.^, Cho, Y.^, Kim, J., Lee, W., Park, J.*, Jin, IH.*, and Kang, S.* (2024) Improved Mean Field Estimates of GEMS AOD L3 Product: Using Spatio-temporal Variability. Atmospheric Measurement Techniques. 17(17), 5221-5241

• Jeon, Y.^, Chang, W., Jeong, S., Han, S., and Park, J.* (2024) A Bayesian Convolutional Neural Network-based Generalized Linear Model. Biometrics. 80(2), ujae057 (Winner of the 2022 Korean International Statistical Society Outstanding Student Paper Award)

• 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 (R package: binspp)

• 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 (Winner of the 2018 Korean International Statistical Society Outstanding Student Paper Award)

• 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

• National Research Foundation of Korea (2025S1A5C3A0200632411), Elucidating the Neuro-Cognitive Mechanisms of Social Isolation and Loneliness Based on Data Science: A Combined Analysis of fNIRS Neuroimaging and Multi-Modal Data, Co-I, June 2025 - May 2028

• National Research Foundation of Korea (RS-2025-00513129), Bayesian Methods for Spatial Functional Data, PI, March 2025 - February 2030

• Yonsei University Humanities and Social Sciences Creative Research Support Program, Bayesian Deep Learning Approach for Intractable Likelihood, PI, January 2025 - January 2027

• 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 - February 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), Computational Methods for High-Dimensional Spatial Data, PI, September 2019 - September 2022

Projects

• 국립환경과학원, 환경위성 평균장 (L3) 알고리즘 고도화, Co-I, 2023

• 교육부, 코로나19 학생 확진현황 연구분석, Co-I, 2022 

• 한국데이터산업진흥원, 코로나19 확진자 동선 및 소비 데이터를 활용한 감염병 확산에 대한 대국민 사전 알림 등 선제대응 체계 수립, PI, 2022 

• 바이엘 코리아, 40세 미만 젊은 여성에서의 침습 유방암의 영상-유전적 특징: 선행연구, Co-I, 2022

Professional Service Activities

• Associate Editor, Journal of Computational and Graphical Statistics, 2023 - present

• Associate Editor, Communications for Statistical Applications and Methods, 2023 - present

Honors

• Teaching Excellence Award, Yonsei University 2020, 2021, 2023

• Korean International Statistical Society Outstanding Student Paper Award, "A Function Emulation Approach for Intractable Distributions," Joint Statistical Meeting 2018

• Distinguished Graduate Fellowship, The Pennsylvania State University, 2014-2015

Teaching

Yonsei University 

• Functional Data Analysis (STA 6320), Fall 2019, 2020, Spring 2022-2024

• Spatio-Temporal Data Analysis (STA 6241), Spring 2020, Fall 2021-2023

• Bayesian Deep Learning (GEV6142), Fall 2023 

• Mathematical Statistics (STA6010), Spring 2021

• Deep Learning (STA 3140), Fall 2019-2021, Spring 2022-2024

• Bayesian Statistics (STA3105), Fall 2022-2024

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.