2026 EMNLP TimelyRAG: Semantic-Temporal Hybrid Retrieval for Time-Critical Question Answering in Overlapping-Evolving Documents Youngeun Nam, Joeun Kim, Hwanjun Song, Susik Yoon, Jae-Gil Lee, and Byung Suk Lee In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Findings, 2026 PDF Poster EMNLP Clustering-Based Balanced Sampling and Allocation with Data Parallelism for High-Performance Fine-Tuning Hyunjin Kim, Youngeun Nam, Jaemin Han, Wonhyeok Choi, and Jae-Gil Lee In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Main, 2026 PDF ACL QuDAR: Query-Wise Dual-Perspective Adaptive Retrieval Joeun Kim, Seunghyouk Yoon, Xuan-Bach Le, Youngeun Nam, Doyoung Kim, Hwanjun Song, and Jae-Gil Lee In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), Main, 2026 PDF Poster Slides CSUR Universal Time-Series Representation Learning: A Survey Patara Trirat, Yooju Shin, Junhyeok Kang, Youngeun Nam, Jihye Na, Minyoung Bae, Joeun Kim, Byunghyun Kim, and Jae-Gil Lee ACM Computing Surveys (CSUR), 2026 PDF 2025 KDD Bi-Modal Learning for Networked Time Series Youngeun Nam*, Jihye Na*, Susik Yoon, Hwanjun Song, Jae-Gil Lee, and Byung Suk Lee In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025 PDF Video Poster KDD Mitigating Source Label Dependency in Time-Series Domain Adaptation under Label Shifts Jihye Na, Youngeun Nam, Junhyeok Kang, and Jae-Gil Lee In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025 PDF Video ICWSM Mobility Networked Time-Series Forecasting Benchmark Datasets Jihye Na*, Youngeun Nam*, Susik Yoon, Hwanjun Song, Byung Suk Lee, and Jae-Gil Lee In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 2025 PDF Poster 2024 TheWebConf Breaking the time-frequency granularity discrepancy in time-series anomaly detection Youngeun Nam, Susik Yoon, Yooju Shin, Minyoung Bae, Hwanjun Song, Jae-Gil Lee, and Byung Suk Lee In Proceedings of the ACM on Web Conference (TheWebConf), 2024 PDF Video Poster KDD Semi-Supervised Learning for Time Series Collected at a Low Sampling Rate Minyoung Bae, Yooju Shin, Youngeun Nam, Young Seop Lee, and Jae-Gil Lee In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024 PDF Video 2023 ECML PKDD Context-Aware Deep Time-Series Decomposition for Anomaly Detection in Businesses Youngeun Nam, Patara Trirat, Taeyoon Kim, Youngseop Lee, and Jae-Gil Lee In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2023 PDF Poster Slides AAAI Demo AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series Patara Trirat*, Youngeun Nam*, Taeyoon Kim, and Jae-Gil Lee In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2023 PDF Video Poster 2022 AAAI COVID-EENet: Predicting Fine-Grained Impact of COVID-19 on Local Economies Doyoung Kim, Hyangsuk Min, Youngeun Nam, Hwanjun Song, Susik Yoon, Minseok Kim, and Jae-Gil Lee In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2022 PDF Video 2020 KDD Hi-COVIDNet: Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea Minseok Kim, Junhyeok Kang, Doyoung Kim, Hwanjun Song, Hyangsuk Min, Youngeun Nam, Dongmin Park, and Jae-Gil Lee In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020 PDF Video