I am an associate professor at the department of Statistics, Sungkyunkwan University, Korea.
My main research include High-dimensional Statistical Models, Statistical Machine Learning, Penalized Methods/Model Selection, Statistical Analysis of Complex Data, Nonparametric Function Estimation, Statistical Optimization etc.
You can contact me through my email.
Email: erlee [at] skku [dot] edu
Office: 32412, Dasan Hall of Economics, Sungkyunkwan University
B.Sc. in Dept. of Statistics, Seoul National University (cum laude), 2004.
M.Sc. in Dept. of Statistics, Seoul National University, 2006.
Ph.D. Statistics, Seoul National University, 25 Feb 2011.
Ph.D. thesis: Sparsity in Linear Regression, Functional Linear Regression and High-dimensional Principal Component Analysis (Best doctoral dissertation award in College of Natural Sciences, Seoul National University, 2011). Advisor: Prof. Byeong Uk Park
Associate Professor, Department of Statistics, Sungkyunkwan University: March, 2020- present
Assistant Professor, Department of Statistics, Sungkyunkwan University: March, 2016- February, 2020
Postdoctoral Researcher (Supervisor: Enno Mammen)
Dept. of Economics, Univ. of Mannheim, Mannheim, Germany: March, 2011 -15, February, 2016
Inst. for Applied Mathematics, Heidelberg Univ., Heidelberg, Germany : January, 2015 - 15, February 2016
Member of the SFB 884 "The Political Economy of Reforms", Univ. of Mannheim : March, 2011- February 2016
S. Park, E. R. Lee^, H. Kim and H. Zhao (2025) Transfer learning under large-scale low-rank regression models, Journal of the American Statistical Association, to appear.
E. R. Lee, S. Park, E. Mammen and B. U. Park (2024) Efficient functional lasso kernel smoothing for high-dimensional additive regression. Annals of Statistics, 52, 1741 - 1773.
S. Park, E. R. Lee* and H. Zhao (2024) Low-rank regression models for multiple binary responses and their applications to cancer cell-line encyclopedia data. Journal of the American Statistical Association, 119, 202-216.
Kim, H., Lee, E.* and Park, S. (2023) Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model, Scientific Reports, 13, 21979.
Park, S. , Kim, H. and Lee, E.* (2023) Regional quantile regression for multiple responses, Computational Statistics and Data Analysis, 188, 107826.
S. Park, E. R. Lee^ and G. Hong (2023) Varying-coefficients for regional quantile via KNN-based LASSO with applications to health outcome study, Statistics in Medicine, 42, 3903-3918.
E. R. Lee, S. Park, S. Lee and G. Hong (2023) Quantile forward regression for high-dimensional survival data. Lifetime Data Analysis, 29, 769-809.
E. R. Lee, J. Cho and S. Park (2022). Penalized kernel quantile regression for varying coefficient models. Journal of Statistical Planning and Inference, 217, 8-23.
S. Park and E. R. Lee* (2021) Hypothesis testing of varying coefficients for regional quantiles. Computational Statistics and Data Analysis, 159, 107204.
E. R. Lee and S. Park. (2021) Poisson reduced-rank models with sparse loadings. Journal of the Korean Statistical Society, 50, 1079-1097.
C. Jentsch, E. R. Lee* and E. Mammen (2021). Poisson reduced-rank models with an application to political text data, Biometrika, 108, 455-468.
C. Jentsch, E. R. Lee* and E. Mammen (2020). Time-dependent Poisson reduced rank models for political text data analysis, Computational Statistics and Data Analysis, 142, 1-20.
E. R. Lee, J. Cho and K. Yu (2019). A systematic review on model selection in high-dimensional regression, Journal of the Korean Statistical Society, 48,1-12.
E. R. Lee, K. H. Han and B. U. Park (2018). Estimation of errors-in-variables partially linear additive models. Statistica Sinica, 28, 2353-2373.
E. R. Lee* and E. Mammen (2016). Local linear smoothing for sparse high dimensional varying coefficient models. Electronic Journal of Statistics, 10, 855-894.
B. U. Park, E. Mammen, Y. K. Lee and E.R. Lee (2015). Varying coefficient regression models: a review and new developments. International Statistical Review, 83, 36-64.
E. R. Lee, H. Noh and B. U. Park (2014). Model selection via bayesian information criterion for quantile regression models. Journal of the American Statistical Association, 109, 216-229.
H. Noh and E. R. Lee* (2014). Component selection in additive quantile regression models. Journal of the Korean Statistical Society, 43, 439-452.
E. R. Lee and B. U. Park (2012). Sparse estimation in functional linear regression. Journal of Multivariate Analysis, 105, 1-17.
Y K. Lee, E. R. Lee and B. U. Park (2012). Principal component analysis in very high-dimensional spaces. Statistica Sinica, 22, 933-956.
P. Hall, E. R. Lee and B. U. Park (2009). Bootstrap-based penalty choice for the lasso, achieving oracle performance. Statistica Sinica, 19, 449-471.
Y. K. Lee and E. R. Lee (2008). Kernel methods for estimating derivatives of conditional quantiles. Journal of the Korean Statistical Society, 37, 365-374.
Y. K. Lee, E. R. Lee and B. U. Park (2006). Conditional quantile estimation by local logistic regression. Journal of Nonparametric Statistics, 18, 357-373.
* Corresponding Author
^ Co-first author
PI, National Research Foundation of Korea (중견연구), 2022-2027
PI, National Research Foundation of Korea (기본연구), 2019-2022.
PI, National Research Foundation of Korea (신진연구), 2016-2019.
2023-current : Associate Editor, Journal of the Korean Statistical Society
김현진 (금융감독원) : Estimation and Inference for High-Dimensional Heterogeneous Data
Jinwoo Cho ( Univ. of Pittsburgh, PhD Student in Statistics): Penalized Local Linear Quantile Regression for Varying Coefficient Models
정현우 (AhnLab 이상탐지팀): Clustering with Word Embedding for Multi-Sense Word
최태영 (넷마블컴퍼니 빅데이터팀): P-Spline for Multi-Level Data Analysis
김지수(라이나생명 데이터분석팀): Predicting movie ratings using Korean reviews with Multinomial Inverse Regression
유제진 (넷마블컴퍼니 빅데이터팀): Word2vec Word Sense Disambiguation with Clustering
강지선(삼성생명 디지털금융팀): Face Recognition and Comparative Study using the Nuclear Norm Regression model
박주희(미래에셋대우 빅데이터팀): Clustering Korean Online Newspaper using LDA Model
김인영(유플러스 디지털팀): Clustering for Multi-prototype Text Analysis
이운재: Constrained Estimation for Functional Linear Regression with Functional Response
손지훈(KCB 코리아크레딧뷰로): Wald test based on B-pline for functional linear regression with scalar response
Matrix Algebra-Spring 2016, Fall 2017, Spring 2018, Spring 2019
Introduction to Multivariate Statistical Analysis : Fall 2016-2020, this class is partially supported by DataCamp. Students will have full access to the entire DataCamp course curriculum for the semester.
Introduction to Regression Analysis: Spring 2017-2021
Intermediate Regression Analysis: Fall 2020, this class is partially supported by DataCamp. Students will have full access to the entire DataCamp course curriculum for the semester.
Advanced Regression Analysis :Spring 2021
Regression Analysis (graduate course): Spring 2017-2020
Nonparametric Statistics (graduate course) : Fall 2017-2019
Modern Statistical Methods (graduate course): Fall 2016