Publications

2026

  1. 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
  2. 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
  3. 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
  4. 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

  1. 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
  2. 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
  3. 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

  1. 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
  2. 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

  1. 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
  2. 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

  1. 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

  1. 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