ViDA
Visual Data Analysis Laboratory
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News:
We received a 3-years (2025/08~2028/07) NSTC grant (2025/07)
張辰瑜、吳承霖 received "College Student Research Scholarship (大專生專題計劃)" (2025/07)
We received one NSTC grant (2/3), covering from 2025/08 to 2026/07 (2025/07)
[Congratulation!] - 李美娉 (Nathania Josephine)、王昱琳 passed their Master's thesis defense (2025/06)
吳承霖、張辰瑜 received 資訊工程系 - 傑出專題獎 (2025/06)
One poster, "NarratorVis: Automated Context-Aware Visual Data Story Generation Using Rule-Based Approach and Large Language Model", received the poster honorable mention award at IEEE PacificVis 2025. (2025/04)
We host the IEEE Pacific Visualization Conference 2025 at Chang Yung-fa International Convention Center from April. 22 - 25. (2025/04)
李美娉 (Nathania Josephine) received a Ph.D. program funding offer from the University of Utah. (2025/02)
We received a grant (2024/10 ~ 2025/05) from the Central Weather Administration (CWA). (2024/10)
One paper, "Automated Narrative Generation from Tabular Data using a Rule-Based and LLM Fusion Approach", has been accepted by The 10th International Conference on Science and Technology (ICST), and received the Best Paper Award. (2024/10)
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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