STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting. - Cheonbok Park; Chunggi Lee; Hyojin Bahng; Taeyun won; Kihwan Kim; Seungmin Jin; Sungahn Ko; Jaegul Choo (CoRR, 2019)

ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed. - Cheonbok Park; Chunggi Lee; Hyojin Bahng; Yunwon Tae; Seungmin Jin; Kihwan Kim; Sungahn Ko; Jaegul Choo (CIKM, 2020)

A Visual Analytics System for Exploring, Monitoring, and Forecasting Road Traffic Congestion. - Chunggi Lee; Yeonjun Kim; Seungmin Jin; Dongmin Kim; Ross Maciejewski; David S. Ebert; Sungahn Ko (IEEE Trans. Vis. Comput. Graph., 2020)

An Empirical Experiment on Deep Learning Models for Predicting Traffic Data. - Hyunwook Lee; Cheonbok Park; Seungmin Jin; Hyeshin Chu; Jaegul Choo; Sungahn Ko (ICDE, 2021)

An Empirical Study on How People Perceive AI-generated Music. - Hyeshin Chu; Joohee Kim; Seongouk Kim; Hongkyu Lim; Hyunwook Lee; Seungmin Jin; Jongeun Lee; Taehwan Kim; Sungahn Ko (CIKM, 2022)

Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting. - Hyunwook Lee; Seungmin Jin; Hyeshin Chu; Hongkyu Lim; Sungahn Ko (ICLR, 2022)

A Graphical Workflow Exploration Environment For Visual Analytics. - Chunggi Lee; Juyoung Oh; Seungmin Jin; Isaac Cho; Sungahn Ko (CoRR, 2022)

A Visual Analytics System for Improving Attention-based Traffic Forecasting Models. - Seungmin Jin; Hyunwook Lee; Cheonbok Park; Hyeshin Chu; Yunwon Tae; Jaegul Choo; Sungahn Ko (IEEE Trans. Vis. Comput. Graph., 2023)