I am an assistant professor in the department of artificial intelligence at Korea University.
My research interests include LLM, Multi-modal AI, and AI agents.
I'm looking for highly motivated graduate & undergraduate students and post-doc researchers.
AutoAI LAB @ Korea University
Jaeyoung Choe, Jihoon Kim, and Woohwan Jung, "Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents" , Annual Meeting of the Association for Computational Linguistics Findings of ACL: ACL 2025, 2025
Injae Na, Keonwoong Noh, and Woohwan Jung, "Automatic Transmission for LLM Tiers: Optimizing Cost and Accuracy in Large Language Models" , Annual Meeting of the Association for Computational Linguistics Findings of ACL: ACL 2025, 2025
Suyong Kwon, Kyuseok Shim, and Woohwan Jung, "Cardinality Estimation of LIKE Predicate Queries using Deep Learning ”, ACM International Conference on Management of Data (SIGMOD), 2025
Kyungri Park, Woohwan Jung, "Improving Detail in Pluralistic Image Inpainting with Feature Dequantization", IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025
Daeyoung Hong, Woohwan Jung, and Kyuseok Shim, ”DP-FROST: Differentially Private Fine-tuning of Pre-trained Models with Freezing Model Parameters.” International Conference on Computational Linguistics (COLING), 2025
Keonwoong Noh, Seokjin Oh, Woohwan Jung, "Beyond Reference: Evaluating High Quality Translations Better than Human References", Empirical Methods in Natural Language Processing (EMNLP), 2024
Jiwon Suh, Injae Na, Woohwan Jung, "Improving Domain-Specific ASR with LLM-Generated Contextual Descriptions", (INTERSPEECH), 2024
Su Ah Lee, Seokjin Oh, Woohwan Jung, "Enhancing Low-Resource Fine-Grained Named Entity Recognition by Leveraging Coarse-Grained Datasets", Empirical Methods in Natural Language Processing (EMNLP), 2023
Jaeyoung Choe, Keonwoong Noh, Nayeon Kim, Seyun Ahn, Woohwan Jung, "Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models" Findings of ACL: EMNLP 2023.
Seongwoon Oh, Woohwan Jung, and Kyuseok Shim, "THUNDER: Named Entity Recognition using a Teacher-student Model with Dual Classifiers for Strong and Weak Supervisions”, European Conference on Artificial Intelligence (ECAI), 2023 [Code]
Suyong Kwon, Woohwan Jung, and Kyuseok Shim, "Cardinality Estimation of Approximate Substring Queries using Deep Learning ”, International Conference on Very Large Databases (VLDB), 2022 [Code]
Daeyoung Hong, Woohwan Jung, and Kyuseok Shim, ”Collecting Geospatial Data under Local Differential Privacy with Improving Frequency Estimation.” IEEE Transactions on Knowledge and Data Engineering (TKDE), (2022) (SCI) [Code]
Daeyoung Hong, Woohwan Jung, and Kyuseok Shim, ”Collecting Geospatial Data for Personalized Services with Local Differential Privacy.” IEEE International Conference on Data Engineering (ICDE), 2021 [Code]
Woohwan Jung, Kyuseok Shim, "Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation", International Conference on Computational Linguistics (COLING), 2020 [Code] (Outstanding paper)
Woohwan Jung, Kyuseok Shim, "T-REX: A Topic-Aware Relation Extraction Model", ACM Conference on Information and Knowledge Management (CIKM), 2020 [Code]
Woohwan Jung, Suyong Kwon and Kyuseok Shim, ”TIDY: Publishing a Time Interval Dataset with Differential Privacy.” IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019 (SCI)
Woohwan Jung, Younghoon Kim, Kyuseok Shim, "Crowdsourced Truth Discovery in the Presence of Hierarchies for Knowledge Fusion", Extending Database Technology (EDBT), 2019 [Datasets] [Code]
Younghoon Kim, Woohwan Jung, Kyuseok Shim, "Integration of graphs from different data sources using crowdsourcing", Information Sciences, Volume 385, 2017, Pages 438-456 (SCI)
B.S. Seoul National University (2012)
Ph.D. Seoul National University (2021)
Outstanding paper (COLING 20)
Student travel grant (CIKM 20)
Reviewer: ACL ARR, CIKM, IEEE TKDE, Information Sciences, ACCV, ICEIC, IEEE BigComp
2025
Natural Language Processing (Spring 2025)
Deep learning (Spring 2025)
AI in finance (Spring 2025)
2024
Deep learning (Fall 2024)
Optimization for AI (Fall 2024)
Deep generative models (Spring 2024)
2023
Deep learning (Fall 2023)
Deep learning (Spring 2023)
Data structure (Spring 2023)
AI in finance (Spring 2023)
2022
Artificial Intelligence (Fall 2022)
Optimization for AI (Fall 2022)
Deep learning (Spring 2022)
Probability theory (Spring 2022)
2021
Artificial Intelligence (Fall 2021)
Deep generative models (Fall 2021)
Deep learning (Spring 2021)
Probability theory (Spring 2021)