Optim. Lab.

환영합니다!! 서울시립대학교 통계학과, 통계데이터사이언스 대학원 Optim.Lab.에 오신것을 환영합니다. 우리 연구실은 다양한 형태의 신경망 모형을 포함한 기계학습의 작동 원리를 이해하고 모형 최적화 방법론 연구를 하고 있습니다. 현재 연구실에서는 주로 네트워크, 순위, 주가 데이터를 다루며 예측모형 및 생성모형을 연구합니다. 연구실을 통해 생산한 논문은 다음 Google Scholar를 통해 확인하실 수 있습니다.

Recent Work (2023-Present)

• Park, K, Hong, S., and Jeon J. J. (2024), "Dynamic High-Order Relations and event-driven temporal model for stock price forecasting" (working paper).
• Lim, J, An, S., Woo, G., Kim, C., and Jeon J. J. (2024), "Context-Driven Missing Data Imputation via Large Language Model" (working paper).
• Kim, C., An, S., Woo, G., Lim, J, and Jeon J. J. (2024), "ViTal-MVP: Visual Transformation of Tabular Data for Multi-View Explainable AI" (working paper).
• Cho, S., Woo, G., Lim, J., Kim, C, Hong S., and Jeon J. J. (2024), "A New Large-Scale Knowledge Graph for Link Prediction in Chemicals inducing Diseases" (working paper).
• Choi, S., Park, T. and Jeon, J. J. (2024), "Large Scale Road-Traffic Noise Mapping with AI Technology" (working paper)
• Park, J., Hong, S., Cho, Y., and J. J. Jeon (2024), "Unicorn: U-Net for Sea Ice Forecasting with Convolutional Neural Ordinary Differential Equations" (working paper) (Link)
• An, S., Hong, S. and Jeon J. J. (2024), "Distributional Learning for Synthetic Tabular Data via Cramer-Wold Device", (working paper)
• Hong, S. and Jeon J. J. (2024), "Uniform Pessimistic Risk and Optimal Portfolio", (working paper) (Link)
• An S., Hong, S., and Jeon J. J. (2024), "Variational AutoEncoder for Distributional Learning via Quantile Function Estimation" (submitted).
• Hong S., An, S. and Jeon J. J. (2024), "Improving SMOTE via Fusing Conditional VAE for Noise Adaptive Sampling", (submitted)
• 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

• Jung, J. and Jeon, J.J. (2025), Query Performance Prediction Model on Statistics
• Shin, W. and Jeon, J.J. (2025), Exposure bias problem in Recommendation
• Cho, S., Shin, W., Woo, K., Kim, K., and Jeon, J.J. (2025), Link prediction model for CTD data analysis
• Woo, K., Cho, S., Shin, W., Kim, K., and Jeon, J.J. (2025), Explainable hypergpaph with causal inference

Funding

• 거대언어모형 개인화 연구, 2023, 서울시립대학교
• 로보어드바이저 알고리즘 개발, 2023, 관악연구소
• 기초연구실, 2022-2024, 과학기술정보통신부
• DS+ 사업단, 2022-2025, 과학기술정보통신부
• 디지털혁신인재양성사업단 (빅데이터), 2021-2023, 교육부
• 기본연구자지원사업, 2022-2024, 과학기술정보통신부
• 1인가구추정 모형개발, 2021-2023, 서울시-서울시립대-SKT-KCB-신한카드 컨소시엄
• 분자독성네트워크 연구, 2021-2025, 환경부
• 인공지능 의사결정 모형개발, 2021

Contact

• Building: 서울시립대학교 미래관 712호
• tel: 82-2-6490-2637 / fax: 82-2-6490-2629
• Address: 서울특별시 동대문구, 전농로 163 서울시립대학교 통계학과, 02504

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