Position: Professor 

Office: 602 Political Science & Economics Building (정경관 602호)

Tel: 02) 3290-2249

Email: yoons77@korea.ac.kr

Area of Interest : High dimensional models, Quantile Regression, Machine Learning

Education

Ph.D. in Statistics, 2010, Ohio State University, Columbus, OH, U.S.A.

M.S. in Statistics, 2006, Ohio State University, Columbus, OH, U.S.A.

B.A. in Statistics, 2003, Korea University, Seoul, Korea

Professional Experiences

2024 – current: Associate Dean, College of Political Science and Economics, Korea University

• 2023 – current: Professor, Korea University, Seoul, South Korea
• 2018 – 2023: Associate Professor, Korea University, Seoul, South Korea
• 2017 – 2018: Assistant Professor, Korea University, Seoul, South Korea
• 2016 – 2017: Senior Lecturer, University of Waikato, Hamilton, New Zealand
• 2013 – 2015: Lecturer, University of Waikato, Hamilton, New Zealand
• 2010 – 2013: Postdoctoral Fellow, Department of Biostatistics, University of Texas MD Anderson Cancer Center

• Awards

• 2022 과학기술우수논문상
• 2020 석탑연구상 [Research Award, KU]
  2019 석탑강의상 [Teaching Award, KU]
  2015 Journal of Nonparametric Statistics Best Paper Award 
  2015 Genesis Oncology Professional Development Award 
   

Peer-reviewed Journals

• (*: corresponding author, ^: graduate student under supervision)

● Kim, H. J., MacEachern, S. N., Kim, Y. M., and Jung, Y. (2026) Kernel density estimation with a Markov chain Monte Carlo sample, Computational Statistics and Data Analysis, 214, 108271.

• ● Kim, S.^{^} and Jung, Y.^*  (2025)  Enhancing​ ​quantile​ ​estimation​ ​via​ ​quantile​ ​combination​ ​under​ ​heteroscedasticity, Statistical Papers, 66 (142) 

● Kim, H.^{^}, Noh, Y.^{^}, Park, J.^{^}, Park, M.^{^}, Yang, B.^{^}, and Jung, Y.^* (2025) Applying embedding models and advanced RAG techniques for Korean text classification. The Korean Journal of Applied Statistics, 38 (4), 571 – 588.

• ● Lee, S.^{^}, and Jung, Y.^* (2025) Predicting LMS login frequency using transformer- and LLM-based time series models. Journal of the Korean Data & Information Science Society, 36 (3), 543 – 556.
• ● Kim, S.^{^}, Lee, S.^{^}, Shin, N.^{^}, and Jung, Y.^* (2025) Variable-selection consistency of linear quantile regression by validation set approach. Statistics & Probability Letters, 223, 110431.
• ● Kim, M.^{^}, Kim, Y.^{^}, Min, J.^{^}, Seong, H.^{^}, Cho, Y., and Jung, Y.^* (2024) Comparison and application of sequence-to-text based language models. The Korean Data & Information Science Society, 35 (6), 815 – 834.

• ● Jung, H.^{^}, Choi, J.^{^}, Weon, S.^{^}, Kim, S.^{^}, Park, J., Lee, N., and Jung, Y.^* (2024) Ensemble learning for detecting game bot in MMORPG. The Korean Data & Information Science Society, 35 (4), 461 – 473.
• ● Kim, S.^{^} and Jung, Y.^* (2024) Local quantile ensemble for machine learning methods. Communications for Statistical Applications and Methods, 31 (6), 627 – 644.
• ● Jeon, J.^{^} and Jung, Y.^* (2024) Data augmentation methods for classifying Korean texts. The Korean Journal of Applied Statistics, 37 (5), 599 – 613.

• ● Lee, M.^{^}, Park, S.^{^}, Lee, S.^{^}, Kim, S.^{^}, Cho, Y., Song, D., Lee, M, and Jung, Y.^* (2024) AI-based early detection to prevent user churn in MMORPG. The Korean Journal of Applied Statistics, 37 (4), 525 – 539.
• • ●Shin, W.^{^} and Jung, Y.^* (2023) Deep support vector quantile regression with non-crossing constraints, Computational Statistics, 38 (4), 1947 – 1976.
  • ● Lee, D.^{^} and Jung, Y.^* (2022) Tutorial and applications of convolutional neural network models in image classification. Journal of  the Korean Data & Information Science Society, 33 (3), 533 – 549.

• ● Jeong, J.^{^} and Jung, Y.^* (2022) Wafer Bin Map Failure Pattern Recognition Using Hierarchical Clustering. The Korean Journal of Applied Statistics, 35 (3), 407 – 419.  (Written in Korean)

• ● Park, J.^{^} and Jung, Y.^* (2022) A Review and Comparison of Convolution Neural Network Models under a Unified Framework. Communications for Statistical Applications and Methods, 29 (2), 161 – 176.
• ● Son, M. , Choi, T., Shin, S. J.,  Jung, Y., and Choi, S. (2022) Regularized Linear Censored Quantile Regression. Journal of the Korean Statistical Society. 51, 589 – 607.
• ● Jung, Y.^* and Kim, H.^{^} (2022) Weighted Validation of Heteroscedastic Regression Models for Better Selection, Statistical Analysis and Data Mining: The ASA Data Science Journal. 15, 57 – 68.
• ● Shin, W.^{^}, Kim, M.^{^}, and Jung, Y.^* (2022) Efficient Information-based Criteria for Model Selection in Quantile Regression. Journal of the Korean Statistical Society. 51, 245 – 281. 
• ● Shin, W.^{^} and Jung, Y.^* (2021) Efficient information-based quantile regression model tuning with heteroscedastic errors. Journal of the Korean Data & Information Science Society, 32 (5), 917 – 929. (Written in Korean)
• ● Min, S.^{^} and Jung, Y.^* (2021) Comparative Study of Prediction Models for Public Bicycle Demand in Seoul. Journal of the Korean Data & Information Science Society, 32 (3), 585 – 592. (Written in Korean)
• ● Lee, H. J.^{^} and Jung, Y.^* (2021) Comparison of Deep Learning-based Autoencoders for Recommender Systems. The Korean Journal of Applied Statistics, 34 (3), 329 – 345.  (Written in Korean)
• ● Han, H.^{^} and Jung, Y.^* (2021) Comparison of Audio Input Representations on Piano Transcription Using Neural Networks. Journal of the Korean Data & Information Science Society, 32 (2), 439 – 453.

• ● Jung, Y.^*, MacEachern, S. N., and Kim, H. (2021) Modified Check Loss for Efficient Estimation via Model Selection in Quantile Regression, Journal of Applied Statistics, 48 (5), 866 – 886.
• ● Jung, Y.^* (2020) Optimal Regression Parameter-specific Shrinkage by Plug-in Estimation, Communications in Statistics – Theory and Methods, 49 (18), 4490 – 4505.
• ● Kim, D.^{^} and Jung, Y.^* (2019) A Numerical Study on Group Quantile Regression Models. Communications for Statistical Applications and Methods, 26 (4), 359 – 370.
  ● Jung, Y.^* (2019) Nonlinear Regression Models for Heterogeneous Data with Massive Outliers, Journal of Applied Statistics, 46 (8), 1456 – 1477.
  ● Jung, Y.^* and Hu, J. (2019) Review: Reversed Low-rank ANOVA Model for Transforming High Dimensional Genetic Data into Low Dimension, Journal of the Korean Statistical Society, 48 (2), 169 – 178.
  ● Jung, Y., Zhang, H., and Hu, J. (2019) Transformed Low-rank ANOVA Models for High-dimensional Variable Selection, Statistical Methods in Medical Research, 28 (4), 1230 – 1246.

• ● De Mello Costa, M.F., Ronchi, F.A., Jung, Y., Ivanow, A., Brage, J.V., Ramos. M.T., Casarini, D.E., and Slocombe, R.F. (2018) ACE Activity Post-race is Influenced by Furosemide Administration, Comparative Exercise Physiology , 14 (2), 119 – 125.

● Jung, Y.^* (2018) Multiple Predicting K-fold Cross-validation for Model Selection, Journal of Nonparametric Statistics , 30 (1), 197 – 215.

● Jung, Y.^* (2017) Shrinkage Estimation of Proportion via Logit Penalty, Communications in Statistics - Theory and Methods, 46 (5), 2447 – 2453.

● Hardie, C., Jung, Y., and Jameson, M. (2016) Effect of Statin and Aspirin Use on Toxicity and Pathological Complete Response Rate of Neo-adjuvant Chemoradiation for Rectal Cancer. Asia-Pacific Journal of Clinical Oncology, 12, 167 – 173.

● Jung, Y.^*, Lee, S. P., and Hu, J. (2016) Robust Regression for Highly Corrupted Response by Shifting Outliers. Statistical Modelling, 16 (1), 1 – 23.

● Jung, Y.^*, and Hu, J. (2015) A K-fold Averaging Cross-validation Procedure. Journal of Nonparametric Statistics, 27 (2), 167 – 179. [Journal of Nonparametric Statistics Best Paper Award 2015]

● Jung, Y., Lee, Y., and MacEachern, S. N. (2015) Efficient Quantile Regression for Heteroscedastic Models. Journal of Statistical Computation and Simulation, 85 (13), 2548 – 2568.

● Jung, Y., Hu, J., and Huang, J. (2014) Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA. Journal of the American Statistical Association, 109 (508), 1355 – 1367.

● Yoo, J., Kim, J., Ro, S., Jung, Y., Jung, S., Choo, S., Lee, J., and Chung, C. (2014) Impact of concomitant surgical atrial fibrillation ablation in patients undergoing aortic valve replacement. Circulation Journal, 78 (6), 1364 – 1371.

● Lester, J., Wessels, A., and Jung, Y. (2014) Oncology Nurses' Knowledge of Survivorship Care Planning: The Need for Education. Oncology Nursing Forum, 41 (2), E35 – E43.

● Lee, Y., MacEachern, S. N., and Jung, Y. (2012) Regularization of Case-Specific Parameters for Robustness and Efficiency. Statistical Science, 27 (3), 350 – 372.

● Lee, S., Lee I., Jung, Y. , McConkey, D., and Czerniak, B. (2012) In-Frame cDNA library combined with protein complementation assay identifies ARL11-binding partners. PLoS ONE, 7(12): e52290.

 

Conference Proceedings

● Jung, Y., and MacEachern, S. N. (2016) Efficient Model Selection in Linear and Non- linear Quantile Regression by Cross-validation. Proceedings of International Conference on Computational and Statistical Sciences 2016, Paris, France.

 

Technical Reports

● Jung, Y., MacEachern, S. N., and Lee, Y. (2010) Window Width Selection for L2 Adjusted Quantile Regression. Technical Report No. 835, Department of Statistics, The Ohio State University.

 

Patents

Research Topic

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