OPTIM. LABUNIVERSITY OF SEOUL
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About the Lab and Research

Optim. Lab. Jong-June Jeon jj.jeon@uos.ac.kr
Department of Statistics and Graduate School of Statistics and Data Science, University of Seoul

Kant wrote, ‘Thoughts without content are empty; intuitions without concepts are blind’ (‘Gedanken ohne Inhalt sind leer, Anschauungen ohne Begriffe sind blind’). In statistics and data science, we reframe this insight for the present: ‘Application without theory is like an arrow without direction, while theory without application is an empty cry.’ Our department is rooted in a philosophical tradition that emphasizes the mutual need for theory and practice, and values a balance between theory and application in data science. We pursue research that is both practical and deep.

Optimization and Machine learning Lab (OPTIM. Lab.)
Optim. Lab. at the University of Seoul Graduate School of Statistics and Data Science studies how machine learning works and develops methods for optimizing learning models. Our work focuses on predictive and generative models using network, ranking, and stock-price data. Publications from the lab can be found on Google Scholar.

Recent Work (2023-Present)

  • Shin, W., Sakurai, K., Ogawa, T and Jeon J. J. (2026), "Modeling and Debiasing Temporal Popularity in Recommendation with Counting Processes", (working paper).
  • Shin, W. Sakurai, K., Ogawa, T and Jeon J. J. (2026), "Hawkes-Driven Popularity-Aware Sampling for Contrastive Recommendation Learning", (working paper).
  • Jung, J., Jeong, D., and Jeon J. J. (2026), "Embedding-Based Query Performance Prediction from Retriever-Generated Embeddings", (working paper).
  • Kim, J. Yoon, Y., Lim, J., and Jeon J. J. (2026), " Recovering Task Intents in Unified LLM-based Dialogue via Candidate Response Selection", (working paper).
  • Lim, J., An, S., and Jeon J. J. (2026), "ProDA: Projection-based Distribution Alignment for Deep Time Series Forecasting", (working paper).
  • An, S., Hong, S. and Jeon J. J. (2026), "Variational Inference for Dynamic Mixtures in Probabilistic Time-Series Forecasting", (submitted).
  • Cho, S., Woo, G., Lim, J., Kim, C, Hong S., and Jeon J. J. (2026), "CTDKG: Towards Uncovering Chemical–Gene–Disease Pathways via Knowledge Graphs", (working paper).
  • Cho, S. and Jeon J. J. (2026), "On the Identifiability of Subgraph Explanations in Self-Explainable GNNs", (working paper).
  • Hong, S. and Jeon J. J. (2026), "Uniform Pessimistic Risk and Optimal Portfolio", (submitted) (Link)
  • Yoon, Y., Kim, J., Choi, J. and Jeon J. J. (2026), "Context-Aware Subgraph Explanations for Multi-Task GNNs", CIKM 2026 (accepted)
  • Park, S., Lim, J., Jeon J. J. and An, S. (2026), "Generating High-Diversity Synthetic Tabular Data via Less-Constrained Prior ", IJCAI 2026 (Link).
  • Choi, S., Park, T., Lim, J., Murphy E., and Jeon, J. J. (2026), "Large Scale Road-Traffic Noise Mapping with AI Technology", Scientific Reports 2026 (Link).
  • An, S., Hong, S. and Jeon J. J. (2026), "Balanced Marginal and Joint Distributional Learning for Tabular Data Synthesis via Mixture Cramer–Wold Distance", Applied Sciences (Link).
  • Kim, C., Jeon J. J., Woo, K. and An, S. (2026), "Estimating Subgraph Importance with Structural Prior Domain Knowledge", PAKDD 2026 (Link).
  • Lim, J, An, S., Woo, G., Kim, C., and Jeon J. J. (2026), "Context-Driven Missing Data Imputation via Large Language Model", PAKDD 2026 (Link).
  • Lim, J, An, S. and Jeon J. J. (2025), "Impute Missing Entries with Uncertainty", AAAI 2026 (Link).
  • Shin, W. and Jeon J. J. (2025), "Revisiting IPS in Recommendation Models: Unveiling Its Impact on Model Performance", WSDM 2026 (Link).
  • Park, J., Cho, Y., and J. J. Jeon, J., Park, H.-C. Kim and Hong, S. (2025), "Unicorn: U-Net for Sea Ice Forecasting with Convolutional Neural Ordinary Differential Equations", Scientific Reports (Link).
  • Woo, K., Cho, S., Kim, D., Kim, C., Na, K., Choi, J. and Jeon, J.J. (2025), CHEM: Causally and Hierarchically Explaining Molecules , CIKM 2025 (Link).
  • Jung, J. and Jeon J. J. (2025), "Generalizing Query Performance Prediction under Retriever and Concept Shifts via Data-driven Correction", CIKM 2025 (Link).
  • An S., Hong, S., and Jeon J. J. (2025), "Variational AutoEncoder for Distributional Learning via Quantile Function Estimation", Neural Networks (Link).
  • Hong S., An, S. and Jeon J. J. (2025), "Improving SMOTE via Fusing Conditional VAE for Noise Adaptive Sampling", Applied Intelligence (Link)
  • Park, K, Hong, S., and Jeon J. J. (2025), "Dynamic High-Order Relations and event-driven temporal model for stock price forecasting", IJCAI 2025 (Link)
  • Park, M, Choi, Y., and Jeon J. J. (2025), "Does a Large Language Model Really Speak in Human‐Like Language?", Stat (Link)
  • Cho, S., Hong, S., and Jeon, J. J. (2025), "Adaptive Adversarial Augmentation for Molecular Property Prediction", Expert Systems with Applications (Link)
  • An, S., Woo, G., Shin, W., Kim, K., Kim, D., Choi, J., and Jeon J. J. (2025), "Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis" , AAAI 2025 (Link)
  • Kim D., Cho. S. Jeon J. J. and Choi J. (2024), "Inhalation toxicity screening of consumer products chemicals using OECD TG data based machine learning models", Journal of Hazardous Materials (Link)
  • Hong S., An, S. and Jeon J. J. (2024), "Multivariate Cryptocurrency Price Forecasting using Distributional Variational AutoEncoder", CIKM 2024 (Link)
  • Hong, S., Choi Y. , Jeon J. J. (2024), "Interpretable Transformer for Water Level Forecasting" (accepted) , Journal of International Forecasting (Link)
  • An, S., Song.K,, Jeon J. J. (2023), Causally Disentangled Generative Variational AutoEncoder", ECAI 2023 (Link)
  • An, S. and Jeon J. J. (2023), "Distributional Variational AutoEncoder To Infinite Quantiles and Beyond Gaussianity", NeuRIPS 2023 (Link)
  • Oh C., So J., Byun H., Lim Y., Shin M., Jeon J., Song.K (2023), Geodesic Multi-Modal Mixup for Robust Fine-Tuning, NeuRIPS 2023
  • An, S. and Jeon (2023), "Customization of Latent Space in Semi-Supervised Variational Autoencoder" , Pattern Recognition Letters (Link)

On-Going Work

  • Lim, J. and Jeon, J.J. (2025), Imputation and Distributional Learning

Funding

  • BK21 AIX-based Environmental Health Intelligent Digital Twin Education and Research Group, 2026, Ministry of Education
  • Development of an algorithm for predicting forest therapy effects based on mental health indicators, 2026, National Institute of Forest Science, Korea Forest Service
  • Prediction of the Arctic sea-ice opening index, 2026–2028, Korea Polar Research Institute
  • Next-generation operational AI boot camp program, 2026–2031, Ministry of Education
  • Missing-data imputation using language models, 2025–2029, Ministry of Science and ICT
  • Accelerated production of aircraft noise maps using AI models, 2025, Korea Environment Institute
  • Molecular toxicity network research, 2021–2025, Ministry of Environment
  • DS+ program, 2022–2027, Ministry of Science and ICT
  • Digital innovation talent development program (big data), 2021–2027, Ministry of Education
  • Basic Research Laboratory, 2022–2024, Ministry of Science and ICT
  • Basic researcher support program, 2022–2024, Ministry of Science and ICT
  • Personalization of large language models, 2023, University of Seoul
  • Development of robo-advisor algorithms, 2023, Gwanak Research Institute
  • Development of a model for estimating single-person households, 2021–2023, Seoul–University of Seoul–SKT–KCB–Shinhan Card consortium
  • Development of an AI decision-making model, 2021

Contact

  • Building: Room 712, Mirae Hall, University of Seoul
  • Tel: 82-2-6490-2637 / fax: 82-2-6490-2629
  • Address: Department of Statistics, University of Seoul, 163 Jeonnong-ro, Dongdaemun-gu, Seoul 02504, South Korea

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