고려대학교 정경대학 통계학과

학력

• 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.S. in Statistics, 2003, Korea University, Seoul, Korea

경력 및 수상

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 – 2016: Lecturer, University of Waikato, Hamilton, New Zealand
2010 – 2013: Postdoctoral Fellow, University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A.


2024.03 - 현재: 고려대학교 정경대학 부학장  (Associate Dean, College of Political Science and Economics)
2024.03 - 현재: 고려대학교 통계연구소장   (Chair, Institute of Statistics)
2024.03 - 현재: 고려대학교 정책대학원 데이터통계학과 주임교수  (Chair, Department of Data Science)
2023.01 - 현재: 한국데이터정보과학회 학술이사  
2023.01 - 현재: 한국통계학회 편집이사 (Editor-in-Chief, Communications for Statistical Applications and Methods)


2023.01 - 2024.02: 에스앤제이랩 자문교수
2022.03 - 2024.02: 모티브인텔리젼스 자문교수
2021.01 - 2022.12: 응용통계연구 편집주간
2021.01 - 2022.12: 한국데이터정보과학회 재무이사
2021.08 - 2023.01: 고려대학교 통계연구소장
2021.08 - 2023.01: 고려대학교 정책대학원 데이터통계학과 주임교수
2019.01 - 2020.12: 한국데이터정보과학회 홍보이사


[수상]

• 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 

학부,대학원 담당과목

Classes I teach/have taught in Korea University.
 
STAT180: Statistical Computer Software (Undergraduate)
STAT221: Introduction to Probability Theory (Undergraduate)
STAT232: Mathematical Statistics (Undergraduate)
STAT242: Statistics for Social Science (Undergraduate)
STAT311: Sampling Theory (Undergraduate)
STAT341: Experimental Design Method (Undergraduate)
STAT342: Regression Analysis (Undergraduate)
STAT343: Categorical Data Analysis (Undergraduate)
STA513: Inferential Statistics (Graduate)
STA514: Statistical Methods for Analysis of Categorical Data (Graduate)
STA813: Topics in Theoretical Statistics (Graduate) 
FMB807: Statistical Methods in Finance (FMBA)
BUS935: Advanced Business Analytics I (MSBA)
BUS936: Advanced Business Analytics II (MSBA)

연구논문

• Peer-reviewed Journals
  (*: corresponding author, ^: graduate student under supervision)
  
  • Shin, W.^ and Jung, Y.* (2023) Deep Support vector quantile regression with non-crossing constraints. Computational Statistics, 38, 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 – 314.
  • 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.
  
  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.