Collection

Intelligent Visualization and Visual Analytics

Submission status
Closed
Visualization and visual analytics play a vital role in combining human and machine intelligence for understanding data and solving real-world problems. These approaches emphasize the communication and collaboration between users and systems. With unprecedented ability to communicate with human and discover insights from data, the recent advancement of artificial intelligence (AI) offers tremendous opportunities in enhancing visualization approaches. Numerous questions need to be answered to fully exploit the power of AI in realizing these opportunities: How can AI facilitate the data management for visualizing extreme-scale datasets? How can AI accelerate the complex transformation to deliver interactive performance? How can AI extract critical information from data and simplify the visualization? How can AI determine the effective visual representation for given data? How can AI generate visualization upon requests from users? How can AI recommend content for users? How can AI understand the interaction intention from users and respond appropriately? How can AI learn from visualization experts and design visualization and interactive interface on its own? This special issue calls for novel techniques in intelligent visualization and visual analytics. In addition to answering the above questions, we welcome research papers that present new possibilities and expand horizons in this direction. The scope of the call for papers includes, but is not limited to, the following topics: Automatic visualization design Automatic visualization creation Intelligent visualization recommendation Intelligent interaction system Intelligent interaction recommendation Intelligent data management AI-powered visualization evaluation AI-powered parallel computation for visualization Neural representation of scalar, vector, and tensor fields Surrogate models for scientific visualization Deep feature identification and classification

Editors

  • Xiaoru Yuan Xiaoru Yuan  &

    Xiaoru Yuan

    Xiaoru Yuan is a tenured faculty member in the School of Electronics Engineering and Computer Science. He received the Ph.D. degree in Computer Science in 2006, from the University of Minnesota at Twin Cities. His primary research interests are in the field of scientific visualization, information visualization and visual analytics. He has co-authored over 60 technical papers in IEEE Visualization, IEEE Information Visualization, IEEE TVCG, IEEE EuroVis, IEEE PacificVis and other major international visualization conference and journals.

  • Jun Tao Jun Tao

    Jun Tao

    Jun Tao is an Associate Professor of Computer Science at Sun Yat-sen University and National Supercomputer Center in Guangzhou. He received a Ph.D. degree in Computer Science from Michigan Technological University in 2015, and worked as a postdoc researcher at the University of Notre Dame from 2015 to 2018. His major research interest is scientific visualization, especially on applying deep learning, information theory, and optimization techniques to interactive flow visualization and multivariate data exploration. He served on the program committees of IEEE VIS, IEEE PacificVis, ChinaVis, and other visualization conferences.

Articles (8 in this collection)