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; Jae-Gil Lee 001 (KDD, 2020)

COVID-EENet: Predicting Fine-Grained Impact of COVID-19 on Local Economies. - Doyoung Kim; Hyangsuk Min; Youngeun Nam; Hwanjun Song; Susik Yoon; Minseok Kim; Jae-Gil Lee 001 (AAAI, 2022)

Korean Online Hate Speech Dataset for Multilabel Classification: How Can Social Science Improve Dataset on Hate Speech? - Taeyoung Kang; Eunrang Kwon; Junbum Lee; Youngeun Nam; Junmo Song; JeongKyu Suh (CoRR, 2022)

AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series. - Patara Trirat; Youngeun Nam; Taeyoon Kim; Jae-Gil Lee 001 (AAAI, 2023)

Context-Aware Deep Time-Series Decomposition for Anomaly Detection in Businesses. - Youngeun Nam; Patara Trirat; Taeyoon Kim; Youngseop Lee; Jae-Gil Lee 001 (ECML/PKDD (6), 2023)

Semi-Supervised Learning for Time Series Collected at a Low Sampling Rate. - Minyoung Bae; Yooju Shin; Youngeun Nam; Youngseop Lee; Jae-Gil Lee 001 (KDD, 2024)

Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection. - Youngeun Nam; Susik Yoon; Yooju Shin; Minyoung Bae; Hwanjun Song; Jae-Gil Lee 001; Byung Suk Lee 001 (WWW, 2024)

Universal Time-Series Representation Learning: A Survey. - Patara Trirat; Yooju Shin; Junhyeok Kang; Youngeun Nam; Jihye Na; Minyoung Bae; Joeun Kim; Byunghyun Kim; Jae-Gil Lee 001 (CoRR, 2024)

Mobility Networked Time-Series Forecasting Benchmark Datasets. - Jihye Na; Youngeun Nam; Susik Yoon; Hwanjun Song; Byung Suk Lee 001; Jae-Gil Lee 001 (ICWSM, 2025)