Research

(*) = first co-author

(#) = corresponding author

2024

• [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]      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]     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]     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.

2018

• [arXiv]  [journal]     S. Park et al. (2019). Permutation Based Testing on Covariance Separability. Comput. Stat., 34(2), 865-883.

• [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. 

• S-J. Kim, S. Park et al., Shape Modification via Constrained Uniform Approximation in Function Estimation. (in progress)

• S. Park et al. Linear Shrinkage Convexification of Penalized Linear Regression With Missing Data (submitted)

• Y. Kim, S. Park et al. Estimation and Deconvolution of Spiked Covariance Matrices and Their Applications to Image Intraclass Correlation Coefficient (in progress)

• S. Cho, S. Park et al. Absolute Correlation Sum Statistics and its Application to Finding Island Variables in High Dimensional Data (in progress)

• M. Lee, S. Park et al. Repeated Cross-sectional Study of COVID-19 Risk Perception: Unraveling Two Years of Dynamics in Korea (submitted)

• (#) Y. Lee, S. Park. High-dimensional Missing Data Imputation Via Undirected Graphical Model (under revision)

• (#) N. K. Phat, Y. Lee, N. P. Long, S. Park. Risk Factors for Tuberculosis Treatment Outcomes: A Statistical Learning-based Exploration using the SINAN Database with Incomplete Observations (submitted)

• (#) K-Y. Bak, S. Park. Linear Covariance Selection Model Via l1-penalization (submitted)

• [SNU arXiv]     High-dimensional Covariance/Precision Matrix Estimation under General Missing Dependency.

• (Invited) Introduction to Multiple Instance Learning. @학과세미나 (2024), Sungshin Women's University, Seoul (Korea).

• (Poster) High-dimensional Missing Data Imputation Via Undirected Graphical Model, @Statistics in the Age of AI, Washington, D.C. (USA).

• (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, @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).

• KISS Outstanding Students Paper Award, Korean International Statistics Society, 2020

• IMS Hannan Graduate Student Travel Award, Institute of Mathematical Statistics, 2020

• Best Doctoral Thesis Award of College of Natural Sciences, Seoul National University, Fall 2019

• Long-term Studying Abroad Scholarship Award from the Office of International Affairs, Seoul National University, 2017 (Research institute : Quantitative Biomedical Research Center at UT Southwestern Medical Center / Research period : 18.01.02 ~ 18.07.31)