ViDA
Visual Data Analysis Laboratory
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News:
One paper, "Improving Daily Rainfall Downscaling over Taiwan's Complex Terrain with Surface-wind-informed Deep Learning Model", has been accepted for publication in the International Journal of Climatology. (2026/09)
[Congratulation!] - 黃子容 passed her Master's thesis defense (2026/07)
One paper, "Trustworthy Deep Learning-Assisted Visualization and Analysis for Distribution-Based Ensemble Scientific Data Summarization", has been accepted for publication in the IEEE Transactions on Visualization and Computer Graphics. (2026/03)
One paper, "A Visual Analytics Approach to Exploring Regional Physical Processes Reflected in Generative Climate Downscaling Models", has been accepted to the PacificVis2026 co-located workshop VisMeetsAI and will be published in Information Visualization Journal. (2026/02)
One paper, "NarratorVis: Automated Context-Aware Visual Data Story Generation Using Rule-Based Approach and Large Language Model", has been accepted to the PacificVis2026 co-located workshop VisMeetsAI and will be published in Information Visualization Journal. (2026/02)
[Congratulation!] - 張珮甄、蕭安隆、李峻豪、呂謙 passed their Master's thesis defense (2026/01)
Visual Data Analysis (ViDA) Laboratory is a research group at Department of Computer Science and Information Engineering, National Taiwan Normal University (NTNU) and led by Ko-Chih Wang (王科植). The main research directions of ViDA are large-scale data analysis and visualization, machine learning, high-performance computing and computer graphics. We conduct cutting-edge research in data visualization for scientific data processing and analysis. Visualization research is at an intersection of data science, computer graphics and large-scale data handling, and has been playing an increasingly important role in many applications. Our research focuses on leveraging machine learning techniques to develop trustworthy emulators that assist scientists in handling large-scale data and high-computation challenges in numerical simulations. We are also interested in employing visualization techniques to help scientists gain deeper insights into machine learning-based emulators, enhancing their confidence in using them effectively. For more details of ViDA's research, please check the Research page.
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