PUBLICATIONS

Published and Accepted papers   (* corresponding author)

• • Son, M., Kim, H., Kim, H., Jin, Y.*, and Kim, J.*  (2026). Reliable Identification and Out-of-Library Detection in Mass Spectra, Journal of Chemometrics, 40(8), e70174.

  • Kim, H., and Kim, J.*  (2026). World Model-based Reinforcement Learning with Hierarchical Structure for Multi-Drone Combat Simulation, Journal of the Korea Society for Simulation. (In press, in Korean)

  • Kwon, S., and Kim, J.* (2026). An Agentic RAG Framework for Query-Type-Aware Execution Control and Evidence-Grounded Selective Verification, The Transactions of the Korea Information Processing Society. (In press, in Korean)

  • Park, J., Jang, Y., Kim, S., Park, T., Jo, S., Choi, D.*, and Kim, J.*  (2026). Class-Aware Topology-Guided Graph Attention for Large-Scale Skeleton-Based Firefighter Action Recognition, Applied Soft Computing, 203(B), 116098.

  • Park, J., Kim, H., Kim, H., Jang, Y., Cho, Y., Lee, R., Ju, B., and Kim, J.* (2026). A Simulation Framework for Military Mobilization Transportation Based on API–GIS Integration, Journal of Defense Policy Studies, 42(2), 113-145. (in Korean)

  • 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. (Included in the Special Issue "Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning")

  • Kim, H., Kwon, S., and Kim, J.* (2026). Enhancing Object Detection Algorithm for Size-Insensitive Performance, Machine Vision and Applications, 37(3), 42.

  • Kwon, S., Jo, S.*, and Kim, J.* (2026). Frequency-Aware Priors for Variational Autoencoders under Class Imbalance, Neurocomputing, 681(7), 132967.

  • 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, 18(18). (Editor’s Pick; included in the topical collection  “Evaluating AI and Machine Learning Models in Cheminformatics: Benchmarking Techniques and Case Studies”) 

Media coverage (link)

• • 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.

The TOP 10% of most-viewed papers published by the journal in 2024

• • 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. (in Korean)

  • 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. (in Korean)

  • 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. (in Korean)

  • 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. (in Korean)

  • 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. (in Korean)

• 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. (in Korean)

• 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. (in Korean)

• 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. (in Korean)

• 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. (in Korean)

• 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. (in Korean)

• 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. (in Korean)

• 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.(in Korean)

• 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.(in Korean)

• 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.(in Korean)

• Kim, J., Cho, H., and Bang, S.* (2016). Penalized Quantile Regression Tree. The Korean Journal of Applied Statistics, 29(7), 1361-1371.(in Korean)

• 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.(in Korean)

• 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.(in Korean)

• 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.(in Korean)

• Submitted papers

1. A Sequential Training Framework for Stabilizing Hierarchical Multi-Agent Reinforcement Learning

2. Gaussian Process-Based Bayesian Optimization for One-Class Classification

3. Hierarchical and Robust Bayesian Meta-Models for Estimating Cadmium Dose-Response Curves

4. A Fast and Scalable Transformer-Pointer Reinforcement Learning Framework for Weapon-Target Assignment

5. Sparse correlation matrix estimation with the screened beta-mixture shrinkage prior

6. HDD-CMS: Structure-Conditioned Diffusion with Hybrid Noise and Dynamic Optimization for Library Complementary Mass Spectrometry

7. A Simulation-Based Diffusion-Transformer Framework for Synthetic TEL Dataset Generation from Satellite Imagery

8. Mercer Kernel-based Spatial Seemingly Unrelated Regression Models: Bayesian Inference and Hierarchical Extension

9. Geometry-Aware Locally Constrained Optimal Transport for Low-NFE Flow Matching

10. Intrinsic reward functions for multi-agent reinforcement learning in military simulation environments

11. Heterogeneous Dual-Critic Architecture for Stable and Sample-Efficient Cooperative MARL

12. Multi-Kernel Gaussian Process Stacking for Boiling Point Prediction with Calibrated Uncertainty

• Working papers

• • • Bayesian Neural Networks with Spatially Structured Priors via Gaussian Conditional Autoregressive Models

    • PDA-Based Interphase Engineering with CNT/GO Hybrid Nanofillers for Balanced Aramid/Epoxy Composites

    • From Text Plausibility to Image-Conditioned Correctness: Ontology-Guided Probing of VLM Evaluation