Embedded Files
Publications
Publications
(*) = co-first author, (†) = corresponding author
2028
[arXiv] [journal] S. Park et al. (2028). Linear Shrinkage Convexification of Penalized Linear Regression With Missing Data. Stat. Sin., 38(1), (forthcoming)
2026
[arXiv] [journal] M. Shin, J. Lee, S. Park et al. (2026). Positive definite covariance matrix estimation via linear shrinkage. J. Korean Stat. Soc.
[arXiv] [journal] S. Kim, G. Kim, S. Park et al., (2026). Voting intentions during the later stage of the COVID-19 pandemic: The roles of risk perception and performance evaluations in South Korea, PLOS ONE, 21(4), e0345621.
[arXiv] [journal] S. Oh, S. Park, and H. Park. (2026). Nonparametric Linear Discriminant Analysis for High Dimensional Matrix-Valued Data. Stat. Anal. Data Min. 19(1), e70060.
2025
[arXiv] [journal] [codes] J. Kim, S. Park, and K-Y. Bak. (2025). Analysis of US airport network and KOSPI volatility based on covariance regression modeling method. Commun. Stat. Appl. Methods. 32, 655-672.
[arXiv] [journal] (†) N. K. Phat, Y. Lee, S. Park, N. P. Long. (2025). Risk Factors for Tuberculosis Treatment Outcomes: A Statistical Learning-based Exploration using the SINAN Database with Incomplete Observations. BMC Med. Inform. Decis. Mak., 25(301).
[arXiv] [journal] [codes] (†) K-Y. Bak, S. Park. (2025). Linear Covariance Selection Model Via l1-penalization. Comput. Stat. Data Anal., 209, 108176.
[arXiv] [journal] (†) N. T. N. Tien, S. Park, N. P. Long et al. (2025). EasyPubPlot: A Shiny Web Application for Rapid Omics Data Exploration and Visualization. J. Proteome Res., In press.
2024
[arXiv] [journal] M. Lee, S. Park et al. (2024). Dynamics of COVID-19 risk perception and predictors in South Korea: a two-year longitudinal study from the pandemic’s beginning (2020–2021). J. Risk Res., 27(8), 1010–1027.
[arXiv] [journal] (†) Y. Lee, S. Park. (2024). High-dimensional Missing Data Imputation Via Undirected Graphical Model. Stat. Comput., 34(160).
[arXiv] [journal] S. Park et al. (2024). Variable Selection in Bayesian Multiple Instance Regression using Shotgun Stochastic Search. Comput. Stat. Data Anal., 196, 107954.
[arXiv] [journal] D. Xiong, S.Park et al. (2024). Bayesian Multiple Instance Classification based on Hierarchical Probit Regression. Ann. Appl. Stat., 18(1), 80-99.
2023
[arXiv] [journal] N. H. Anh, N. P. Long, S. Park et al. (2023). Molecular and metabolic phenotyping of hepatocellular carcinoma for biomarker discovery: a meta-analysis. Metabolites, 23(11), 1112.
[arXiv] [journal] K. P. Nhung, N. P. Long, S. Park et al. (2023). Alterations of Lipid-Related Genes during Anti-Tuberculosis Treatment: Insights into Host Immune Responses and Potential Transcriptional Biomarkers. Front. Immunol., 14, xx-yy.
[arXiv] [journal] T. T. M. Nhung, N. P. Long, S. Park et al. (2023). Genome-wide kinase-MAM interactome screening reveals the role of CK2A1 in MAM Ca2+ dynamics linked to DEE-66. PNAS, 120(32), e2303402120.
[arXiv] [journal] S. Park et al. (2023). Sparse Hanson–Wright inequality for a bilinear form of sub-Gaussian variables. Stat., 12( 1), e539.
[arXiv] [journal] (†) S. Kim, S. Park et al. (2023). Robust Tests for Scatter Separability Beyond Gaussianity. Comput. Stat. Data Anal., 179, 107633.
2022
[arXiv] [journal] (†) Y. Kim, S. Park et al. (2022). Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequences, BMC Bioinform., 23, 469.
[arXiv] [journal] T. Lu, S. Park et al. (2022). Netie: inferring the evolution of neoantigen–T cell interactions in tumors, Nat Methods., 19, 1480-1489.
[arXiv] [journal] N. P. Long, S. Park et al. (2022). Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize metabolism-centric biomarkers for pulmonary tuberculosis. Sci Rep., 12, 13395.
[arXiv] [journal] S. Park and J. Lim. (2022). An Overview of Heavy-Tail Extensions of Multivariate Gaussian Distribution and Their Relationships. J. Appl. Stat., 49(13), 3477-3494.
[arXiv] [journal] S. J Kim, N. P. Ahn, and S. Park et al. (2022). Metabolic and Cardiovascular Benefits of Apple and Apple-Derived Products: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Front. Nutr. 9:766155.
[arXiv] [journal] N. P. Long, S. Park et al. (2022). Molecular Perturbations in Pulmonary Tuberculosis Patients Identified by Pathway-level Analysis of Plasma Metabolic Features. PLOS ONE. 17(1):e0262545.
2021
[arXiv] [journal] N. P. Long, S. Park et al. (2021). A 10-gene biosignature of tuberculosis treatment monitoring and treatment outcome prediction. Tuberculosis, 131, 102138.
[arXiv] [journal] S. Park et al. (2021). Estimating High-dimensional Covariance and Precision Matrices under General Missing Dependence. Electron. J. Statist., 15(2), 4868-4915.
[arXiv] [journal] S. J Kim, S. Park et al. (2021). Effects of Oats (Avena sativa L.) on Inflammation: A Systematic Review and Meta-analysis of Randomized Controlled Trials. Frontiers in Nutrition, 8, 595.
[arXiv] [journal] S. J Kim, S. Park et al. (2021). Effects of β-Cryptoxanthin on Improvement in Osteoporosis Risk: A Systematic Review and Meta-Analysis of Observational Studies. Foods, 10(2), 296.
[arXiv] [journal] [codes] T. Lu, S. Park et al. (2021). Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data, Cell Reports, 34(1), 108589.
2020
[arXiv] [journal] N. P. Long, S. Park et al. (2020). Isolation and metabolic assessment of cancer cell mitochondria. ACS Omega, 5(42), 27304-27313.
[arXiv] [journal] (*) D-K Lee and S. Park et al. (2020). Research Quality-Based Multivariate Modeling for Comparison of the Pharmacological Effects of Black and Red Ginseng. Nutrients, 12(9), 2590.
[arXiv] [journal] N. P. Long, S. Park et al. (2020). Advances in Liquid Chromatography–Mass Spectrometry-Based Lipidomics: A Look Ahead. J. Anal. Test., 4, 183–197.
[arXiv] [journal] [codes] S. Park et al. (2020). Bayesian multiple instance regression for modeling immunogenic neoantigens. Stat. Methods in Med. Res., 29(10), 3032-3047.
2019
[arXiv] [journal] [codes] S. Park et al. (2020). Clustering of Longitudinal Interval Valued Data via Mixture Distribution under Covariance Separability. J. Appl. Stat., 47(10), 1739-1756.
[arXiv] [journal] (†) H. Choi, S. Park et al. (2019). Testing for stochastic order in interval-valued data . Korean J. Appl. Stat., 32(6), 879-887. (written in Korean), English version is available at arXiv.
[arXiv] [journal] S. Park and J. Lim. (2019). Non-asymptotic Rate for High-dimensional Covariance Estimation with Non-independent Missing Observations. Stat. Probabil. Lett., 153, 113-123.
[arXiv] [journal] S. Park et al. (2019). Interval Prediction on the Sum of Binary Random Variables Indexed by a Graph. Commun. Stat. Appl. Methods., 26, 261-272.
[arXiv] [journal] N. P. Long, S.Park et al. (2019). An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer. Cancers, 11(2), 155.
[arXiv] [journal] N. P. Long, S.Park et al. (2019). High-throughput omics and statistical learning integration for the discovery and validation of novel diagnostic signatures in colorectal cancer. Int. J. Mol. Sci., 20(2), 296.
[arXiv] [journal] N. P. Long, S.Park et al. (2019). Efficacy of integrating a novel 16-gene biomarker panel and machine learning algorithms to improve the differential diagnosis of rheumatoid arthritis and osteoarthritis. J. Clin. Med. , 8(1), 50.
[arXiv] [journal] [codes] S. Park et al. (2019). Permutation Based Testing on Covariance Separability. Comput. Stat., 34(2), 865-883.
2018
[arXiv] [journal] D. Lee, S. Park et al. (2018). In vitro tracking of intracellular metabolism-derived cancer volatiles via isotope labeling. ACS Cent. Sci., 4(8), 1037-1044.
2017 Impact Factor: 11.228
[arXiv] [journal] W. Son, S. Park, J. Lim. (2018). Independence and maximal volume of d-dimensional random convex hull. Commun. Stat. Appl. Methods., 25:79-89.
Work in progress
Work in progress
S. Cho, S. Park et al. Testing Independence in High-Dimensional Data Based on the Absolute Sum of Cross Covariances (submitted)
(*) C. Kim, S. Park et al. Media Framing and Risk Perception: Consecutive research during the first year of COVID-19 in South Korea (in progress)
[arXiv] [journal] [codes] (†) M. Shin, J. Lim, S. Park. Graph Estimation Based on Neighborhood Selection for Matrix-variate Data (accepted in JMVA)
S. Seon, S. Park et al. Censored broken adaptive ridge rank regression via induced smoothing (submitted)
[arXiv] [journal] [codes] (†) H. Kim, S. Park. Matrix graphical model via joint estimation of partial correlation (submitted)
(†) J. A. Gómez, S. Park. Estimation of Multiple Graphical Models Via Pre-training (in progress)
(†) H. Kang, S. Park, S. Park. Analysis of Longitudinal Omics Profiles Using Linear Mixed Models (submitted)
Doctoral dissertation
Doctoral dissertation
[SNU arXiv] High-dimensional Covariance/Precision Matrix Estimation under General Missing Dependency.
Presentations
Presentations
(Contributed) Graph Estimation Based on Neighborhood Selection for Matrix-variate Data, @1st IMS New Researchers Conference Asia (2026), (Hong Kong).
(Invited) Graph Estimation Based on Neighborhood Selection for Matrix-variate Data, @학과세미나 (2026), Ewha Womans University, Seoul (Korea).
(Invited) Graphical Models for Matrix-variate Data, @Inha Statistics and Data Science Conference (2026), Inha University, Incheon (Korea).
(Invited) High-dimensional Missing Data Imputation Via Undirected Graphical Model, @IASC-ARS Interim Conference 2024, Taipei (Taiwan).
(Invited) Multiple Instance Learning / High-dimensional Covariance/Precision Matrix Estimation, @통계데이터사이언스 세미나 (2024), University of Seoul, Seoul (Korea).
(Poster) High-dimensional Missing Data Imputation Via Undirected Graphical Model, @Statistics in the Age of AI (2024) Washington, D.C. (USA).
(Invited) Introduction to Multiple Instance Learning. @학과세미나 (2024), Sungshin Women's University, Seoul (Korea).
(Invited) Testing Correlation Structure of Matrix-variate Data. @IMS-APRM 2024, Melbourne (Australia).
(Invited) Testing Correlation Structure of Matrix-variate Data. @The 3rd Big Data Colloquium (2023), Chonnam University (online).
(Invited) Variable Selection in Bayesian Multiple Instance Regression using Shotgun Stochastic Search. @EcoSta 2023, Toyko (Japan).
(Invited) Covariance Matrix Estimation with Incomplete Data and its Applications, @신진통계학자 학술대회(2023), Gyeonggi-do (Korea).
(Invited) Covariance Matrix Estimation with Incomplete Data and its Applications, @제 10회 통계세미나(2022), Korea University, Seoul (Korea).
(Contributed) A Survey of Multiple Instance Supervised Learning (다중 개체 지도 학습 문제에 대한 연구 개요), @한국통계학회(2022하계), Seoul (Korea).
(Contributed) Bayesian Multiple Instance Regression Model for Modeling Immunogenic Neoantigens, @한국통계학회(2021춘계) (online)
(Invited) Introduction to Multiple Instance Learning. @Colloquium (2021), Ajou University (online).
(Contributed) Estimating High-dimensional Covariance and Precision Matrices under General Missing Dependence, @Bernoulli-IMS One World Symposium 2020 (online)
(Invited) Estimating High-dimensional Covariance and Precision Matrices under General Missing Dependence, @KISS annual meeting 2020 (online)
(Contributed) Estimating High-dimensional Covariance and Precision Matrices under General Missing Dependence, @Joint Statistical Meetings 2020 (online)
(Invited) Estimating High-dimensional Covariance and Precision Matrices under General Missing Dependence. South Taiwan Statistics Conference 2020.
Selected as the Korean representative in CIPS-JSS-KSS Young Researcher's session
Cancelled due to COVID-19, but resumed in 2021 (online)
(Contributed) Modeling Immunogenic Neoantigens Using a Bayesian Multiple Instance Regression Model. @2019 WNAR/IMS/JR meeting, Oregon, PO (USA).
(Poster) Permutation Based Testing on Covariance Separability, @Joint Statistical Meetings 2017, Baltimore, MD (USA).
(Poster) Estimation of a bivariate convex function, @ERCIM WG CMStatistics 2016, Seville (Spain).
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