Published and accepted papers
Lee, S., Kim, T., Kim, J., and Jeong, K. (2026).
Reliability-Aware Deep Learning Framework for Chemical Genotoxicity Prediction with Uncertainty Quantification.
Journal of Chemical Information and Modeling, 66(11), 6350–6360.
Seo, K., Lee, S., and Lim, J. (2026).
On Parameter Estimation for the Truncated Skew-Normal Distribution.
Communications for Statistical Applications and Methods, to appear.
Lim, J., Kim, J., Lee, S., and Lee, S. (2026).
Shapley-Based Fault Variable Identification in MSPC.
Quality Engineering, 1–21.
Lee, S.†, Lee, J.†, Yoon, U., et al. (2026).
Advancing Chemical Safety Prediction: An Integrated GNN Framework with DFT-Augmented Cyclic Compound Solution.
Journal of Cheminformatics, 18(28).
Lee, S.†, Chung, Y.†, Ahn, S., and Lim, J. (2026).
Outlier Detection for Histogram-Valued Data Using Wasserstein Distance-Based One-Class SVM.
Journal of the Korean Statistical Society, 55, 175–196.
Nam, J.†, Lee, S.†, Jo, S., Kim, J., Lee, J., Koo, J., Lee, B., Jeong, K., and Yu, D. (2025).
Improving Vapor Pressure Prediction through Integration of Multiple Molecular Representations: A Super Learner Approach.
Journal of Chemometrics, 39(2), e70003.
Jeong, K., Nam, J., Lee, S., Koo, J., Lee, J., Yu, D., Jo, S., and Kim, J. (2024).
Prediction of Flash Point of Materials Using Bayesian Kernel Machine Regression Based on Gaussian Processes with a LASSO-Like Spike-and-Slab Hyperprior.
Journal of Chemometrics, e3586.
Rashid, M.†, Lee, S.†, Kim, K. H., Kim, J., and Jeong, K. (2024).
Machine Learning Approach for Predicting the Hole Mobility of Perovskite Solar Cells.
Advanced Theory and Simulations, 7(6).
Kim, J., Lee, S., and Cho, H. (2016).
An Analysis of Scientific Military Training Data Using a Joint Model for Longitudinal and Time-to-Event Data.
Journal of the Korean Data Analysis Society, 18(6), 2975–2985.
† Equal contribution.
Lee, S., Lim, J., Kim, J., and Wang, X.
Bayesian Donor Set Selection in Synthetic Controls.
Under review (revised).
Lee, S., Lim, J., and Ahn, S.
Monitoring of Histogram-Valued Data.
Under review (revised).
Lee, S., Choi, S., Kim, B., and Lim, J.
Testing the Validity of Weight Constraints in Synthetic Control Method. (in Korean)
Under revision.
A Binomial State-Space Model for Estimating the Abundance of Unmarked Animals from Camera Trap Data
Regression model to estimate the density and abundance of unmarked animals from camera-trap data
Composite Likelihood Estimation for Log–Concave Hidden Markov Models
Large-Scale Covariate-Assisted Multiple Testing Procedure under Dependence