PUBLICATIONS
Last updated on May 13, 2026
Last updated on May 13, 2026
Published and Accepted papers (* corresponding author)
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, in print.
Kim, H., Kwon, S., and Kim, J.* (2026). Enhancing Object Detection Algorithm for Size-Insensitive Performance, Machine Vision and Applications, in print.
Kwon, S., Jo, S.*, and Kim, J.* (2026). Frequency-Aware Priors for Variational Autoencoders under Class Imbalance, Neurocomputing, in print.
Jang, Y., Lee, J., Jeong, K.*, and Kim, J.* (2026). Multimodal Graph Fusion with Statistically Guided Parsimonious Descriptor Selection for Molecular Property Prediction, Journal of Cheminformatics, in print. (Editor’s Pick; included in the topical collection “Evaluating AI and Machine Learning Models in Cheminformatics: Benchmarking Techniques and Case Studies”)
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.
Namgung, J., Mun, J, Park, Y., Kim, J.*, and Park, B. (2024). Sex differences in autism spectrum disorder using class imbalance adjusted functional connectivity. NeuroImage, 304, 120956.
Mun, J., Bang.*, and Kim, J.* (2024). Weighted Support Vector Machine for Extremely Imbalanced Data. Computational Statistics & Data Analysis, 203, 108078.
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 LASSO-Like spike-and-slab hyperprior. Journal of Chemometrics, e3586.
Lee, M., Choi, M., Yang, T., Kim, J., Kim, J., Kwon, O., Cho, N. (2024). A Study on the Advancedment of Intelligent Military Drones: Focusing on Reconnaissance Operations. IEEE Access, 12, 55964-55975.
Choi, J., Jo, S.*, and Kim, J.* (2024). A Bayesian time-varying coefficient model for Cobb–Douglas production function. Computational Economics, 65(3), 1429-1455.
Rashid, M., Lee, S., Kim, K. H., Kim, J.*, and Jeong, K.*. (2024). Machine Learning approach for predicting the Hole Mobility of the Perovskite Solar Cells. Advanced Theory and Simulations, 7(6).
Lee, J., Kim, J.*, and Jo. S.*. (2024). A study on Bayesian beta regressions for modelling rates and proportion. The Korean Journal of Applied Statistics, 37(3), 339-353.
Nam, J., Mun, J., Jo, S.*, and Kim, J.*. (2024). Prediction of forest fire risk for artillery military training using weighted support vector machine for imbalanced data. Journal of Classification, 41, 170-189.
Kim, J., Han, C., Lee, J., Yun, W., Lee, S., Yang, T., Yu, D.*, and Jo, S.*. (2024). Improvement of SAR target classification using GAN-based data augmentation and wavelet transformation. Military Operations Research, 29(3).
Nam, J., and Kim, J.*. (2023). A Study on Military Application of Non-line-of-sight Imaging. Journal of the Military Operations Research Society of Korea, 49(3), 111-121.
Lee, S., Huh, M., Jo, S.*, Kim, J*. (2023). A study on US corn yield forecasts considering spatial dependence. Journal of the Korean Data And Information Science Society, 34(6), 941-955.
Kwon, O., Cho, N., Lee, J.K., Jung, Y., and Kim, J.* (2023). A Study on the Simulation Logic for Cyber Warfare using the Information Entropy Theory. Journal of the Military Operations Research Society of Korea, 49(2), 70-87.
Kim, J., Jang, B., and Bang, S.* (2023). Model-based recursive partitioning algorithm to penalized non-crossing multiple quantile regression for the right-censored data. Communications in Statistics-Simulation and Computation, 52(8), 3741-3757.
Cho, Y., Lee, S., Kim, J.*, and Yu, D.* (2023). Sparse partial correlation estimation with scaled Lasso and its GPU-parallel algorithm. IEEE Access, 11, 65093-65104.
Kim, J., Jo, S., and Lee, K.* (2023). Bayesian computational methods for state-space models with application to SIR model. Journal of Statistical Computation and Simulation, 93(7), 1207-1223.
Kwon, O., Kim, J., Kim, D., and Cho, N.* (2023). Estimation of Urbanization Factor in Wargame Model using Fractal Dimension. Journal of Korean Society of Industrial and Systems Engineering, 46(1), 42,47.
Jung, K., Kim, J., Jung, H., Seo, S., Hong, J., Bai, H., and Jo, S.*.(2023) Curve fitting algorithm of functional radiation-response data using Bayesian hierarchical Gaussian process regression model. IEEE Access, 11, 7109-7116. (Co-first author)
Cho, Y., Kim, J.*, and Yu, D.* (2022). Comparative Study of CUDA GPU Implementations in Python With the Fast Iterative Shrinkage-Thresholding Algorithm for LASSO. IEEE Access, 53324-53343.
Bang, S., and Kim, J.* (2022). A divide-oversampling and conquer algorithm based support vector machine for massive and highly imbalanced data. The Korean Journal of Applied Statistics, 35(2), 177-188.
Ahn, J., Kim, D., and Kim, J.* (2022) Object Recognition Using Convolutional Neural Network in military CCTV. JOURNAL OF THE KOREA SOCIETY FOR SIMULATION, 31(2), 11-20.
Lee, S., Lim, C., Lee, C., Cho, Y., and Kim, J.* (2022). Development of standard loss analysis model using big data: Focusing on Republic of Korea army. Journal of Advances in Military Studies, 5(2), 159-172.
Won, K., Kim, J.*, Koo, J., Lee, H., and Kim, Y. (2021). A study on Implementation and Application of Communication Middleware for LVC Interoperability. Journal of the Korea Academia-Industrial cooperation Society, 22(12), 95-104.
Bang, S. Han, S., and Kim, J.* (2021). Divide and conquer algorithm based support vector machine for massive data analysis. Journal of the Korean Data And Information Science Society, 32(3), 463-473.
Han, B., Yun, W., and Kim, J.* (2020). Analysis of mobilization training data using beta regression. Journal of the Korean Data and Information Science Society, 31(3), 611-620.
Bang, S., and Kim, J.* (2020). Divide and conquer kernel quantile regression for massive dataset. The Korean Journal of Applied Statistics, 33(5), 569-578.
Han, B., Kim, J., Kim, W., and Yun, W.* (2020). An Analysis of Improving Factors of Special Qualification Training Accomplishment and Appropriate Training Duration for Mobilization Artillery Battalions. Journal of the Military Operations Research Society of Korea, 46(1), 43-58.
Kim, J. and Bang, S. (2020). Analysis of AI interview data using unified non-crossing multiple quantile regression tree model. The Korean Journal of Applied Statistics, 33(6), 753-762.
Bang, S., and Kim, J.* (2020). Sampling Method Using Gaussian Mixture Clustering for Classification Analysis of Imbalanced Data. Journal of The Korean Data Analysis Society, 22(2), 565-574.
Kim, M., and Kim, J.* (2020). International Issues on the Lethal Autonomous Weapons System and Proactive Countermeasures. Journal of National Defense Studies, 63(1), 171-204.
Kim, J., Cho, H., and Bang, S.* (2019). Unified noncrossing multiple quantile regressions tree. Journal of Computational and Graphical Statistics, 28(2), 454-465.
Kim, J., and Cho, H.* (2019). Seemingly unrelated regression tree. Journal of Applied Statistics, 46(7), 1177-1195.
Kim, J., Bang, S., and Kwon, O.* (2017). Analysis of scientific military training data using zero-inflated and Hurdle regression. Journal of the Korean Data and Information Science Society, 28(6), 1511-1520.
Kim, J., Cho, H., and Bang, S.* (2017). Multivariate quantile regression tree. Journal of the Korean Data and Information Science Society, 28(3), 533-545.
Kim, J., Lee, S., and Cho, H.* (2016). An Analysis of Scientific Military Training Data using Joint Model for Longitudinal and Time-to-event Data, Journal of the Korean Data Analysis Society, 18(6), 2975-2985.
Kim, J., Cho, H., and Bang, S.* (2016). Penalized Quantile Regression Tree. The Korean Journal of Applied Statistics, 29(7), 1361-1371.
Kim, J., Kim, G., and Cho, H.* (2015). Analysis of Survivability for Combatants during the Offensive Operation of the Tactical level. The Korean Journal of Applied Statistics, 28(5), 921-932.
Kim, J., Kim, J., and Kim, S.* (2007). A Hybrid Technological Forecasting Model by Identifying the Efficient DMUs : An Application to the Main Battle Tank. Journal of Technology Innovation, 15(2), 83-102.
Kim, J., Kim, J., and Kim, S.* (2007). A Comparative Study of Technological Forecasting Methods with the Case of Main Battle Tank by Ranking Efficient Units in DEA. Journal of the Military Operations Research Society of Korea, 33(2), 61-74.
Submitted papers
A Sequential Training Framework for Stabilizing Hierarchical Multi-Agent Reinforcement Learning
Class-Aware Topology-Guided Graph Attention for Large-Scale Skeleton-Based Firefighter Action Recognition
Gaussian Process-Based Bayesian Optimization for One-Class Classification
Hierarchical and Robust Bayesian Meta-Models for Estimating Cadmium Dose-Response Curves
A Fast and Scalable Transformer-Pointer Reinforcement Learning Framework for Weapon-Target Assignment
Sparse correlation matrix estimation with the screened beta-mixture shrinkage prior
HDD-CMS: Structure-Conditioned Diffusion with Hybrid Noise and Dynamic Optimization for Library Complementary Mass Spectrometry
Diffusion-Transformer Hybrid Framework for Dataset Construction in Deep Learning-Based TEL Detection from Satellite Imagery
Mercer Kernel-based Spatial Seemingly Unrelated Regression Models: Bayesian Inference and Hierarchical Extension
Heterogeneous Dual-Critic Architecture for Stable and Sample-Efficient Cooperative MARL
Reliable Identification and Out-of-Library Detection in Mass Spectra
From Text Plausibility to Image-Conditioned Correctness: Ontology-Guided Probing of VLM Evaluation
Working papers
Bayesian Neural Networks with Spatially Structured Priors via Gaussian Conditional Autoregressive Models
Intrinsic reward functions for multi-agent reinforcement learning in military simulation environments