About Us

Following the recent development of artificial intelligence (AI) technology, there have been growing research interests and opportunities in applying AI to enhance data visualizations and enrich the way people engage with data.

We, the AI4VIS research team in HKUST VisLab, are exploring AI-powered techniques for modelling, authoring, analysing, and interacting with data visualizations. In vision of formalizing visualizations as an emerging data format, we apply advanced AI techniques to visualizations. We have diverse research interests such as visualization recommendation, information extraction from visualizations and automated layouting.


  • Wakey-Wakey was accepted in ACM UIST! Make your cute animated text XD
  • InkSight was accepted in IEEE VIS! Start to document your findings with ease!
  • DMiner was accepted in IEEE TVCG! Read more about dashboard mining.
  • Notable was accepted in ACM CHI.
  • Our ongoing project was funded by the Research Grants Council with 1M HKD.
  • Two papers were published on ACM CHI!
  • Check our latest survey on AI4VIS (to appear in TVCG) to learn more about the emerging field!
  • Two papers on visualization recommendation were accepted on IEEE VIS!
  • Articles


    InkSight: Leveraging Sketch Interaction for Documenting Chart Findings in Computational Notebooks

    OldVisOnline: Curating a Dataset of Historical Visualizations

    Yu Zhang, Ruike Jiang, Liwenhan Xie, Yuheng Zhao, Can Liu, Tianhong Ding, Siming Chen, Xiaoru Yuan

    Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling


    ComputableViz: Mathematical Operators as a Formalism for Visualization Processing and Analysis

    DashBot: Insight-Driven Dashboard Generation Based on Deep Reinforcement Learning


    MultiVision: Designing Analytical Dashboards with Deep Learning Based Recommendation

    KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation

    Best Paper Honorable Mention in IEEE VIS Conference

    InfoColorizer: Interactive Recommendation of Color Palettes for Infographics

    Linping Yuan, Ziqi Zhou, Jian Zhao, Yiqiu Guo, Fan Du, Huamin Qu


    MobileVisFixer: Tailoring Web Visualizations for Mobile Phones Leveraging an Explainable Reinforcement Learning Framework


    Towards Automated Infographic Design: Deep Learning-based Auto-Generation of Extensible Timeline


    Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach

    Hammad Haleem, Yong Wang, Abishek Puri, Sahil Wadhwa, Huamin Qu

    Current Members


    Aoyu Wu


    Grants & Funding

    "An Integrated Framework for Extracting and Utilizing Information from Data Visualizations in Digital Documents", awarded by Hong Kong Research Grants Concil — General Research Fund: 16210722 (1,039,978 HKD).