Conference Information
SciVis 2017 : IEEE Scientific Visualization
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Phoenix, Arizona, USA
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Call For Papers
The IEEE Scientific Visualization (SciVis) Conference solicits novel research ideas and innovative applications in all areas of scientific visualization. The scope of the conference, co-located at VIS with the annual IEEE Visual Analytics and IEEE Information Visualization Conferences, includes both fundamental research contributions within scientific visualization, as well as advances towards understanding or solving real world problems, or that impact a particular application in a significant way.

Please note that topics focused on visual analytics, e.g., computational solutions facilitated by visual interfaces to support analysis, might be a better match for the IEEE VAST Conference at IEEE VIS. Similarly, topics which clearly focus on information visualization, e.g., graphical representation of abstract data to aid cognition, might be a better match for the IEEE InfoVis Conference, also at IEEE VIS. Papers chairs reserve the right to move papers between conferences based on its topic and perceived fit.


Research contributions are welcomed across a range of topics including, but not limited to:

Visualization, rendering, and manipulation of spatial data

    Scalar, vector and tensor fields
    Multidimensional multi-field, multi-modal, and multivariate data,
    Time-varying data
    Regular and unstructured grids
    Point-based data
    Volumetric data
    Streaming data

Visual computing, systems and methodologies

    System and toolkit design
    Topology-based and geometry-based techniques
    Feature extraction and pattern analysis
    Uncertainty visualization
    View-dependent visualization
    Glyph-based techniques
    Texture based techniques
    Illustrative visualization
    Integrating spatial and non-spatial data visualization
    Applications of visual analytics approaches
    Computational steering.

Interaction techniques and devices

    User interfaces
    Interaction design
    Coordinated and multiple views
    Data editing for validation
    Manipulation and deformation
    Multimodal input devices
    Haptics for visualization
    Mobile and ubiquitous visualization
    Visual interaction for data science
    Interaction with visualizations in different display environments.

Data Science

    Large-scale computing
    Storage and data analytics
    Distributed, cluster, and grid computing
    Scalable data management on and off the cloud
    High-performance computing on multi-core, GPUs, FPGA, and embedded devices
    Information extraction and knowledge discovery from big data
    Petascale visualization
    Application of computer vision techniques
    Statistical modeling
    Data mining, machine learning
    Clustering techniques
    Reduced-order modeling
    Visual steering for data retrieval.

Display techniques

    Large and high-res displays
    Giga-pixel displays
    Wrist displays/wearable displays
    Stereo displays
    Immersive and virtual environments
    Mixed and augmented visualization
    Projector-camera systems
    Perception and cognition coupled displays
    Small displays
    Mobile Devices


    Collaborative and distributed visualization
    Visual design and design studies
    Mathematical theories for visualization
    Scalability issues
    Visualization verification
    Information theoretic approaches
    Perception theory
    Scene and motion perception
    Knowledge-assisted visualization


    Usability studies and task analysis
    Design and user studies
    Validation and verification visualization
    Statistical techniques
    Human computation

Visual computing applications

    Mathematics, physical sciences and engineering
    Earth, space, and environmental sciences
    Flow fields
    Terrain visualization
    Geographic/geospatial visualization
    Molecular, biomedical and medical visualization
    Bioinformatics visualization
    Software visualization
    Business and finance visualization
    Social and information sciences
    For the masses
    Multimedia (image/video/music)

Visual computing for emerging applications

    Live cell imaging
    Imaging genetics
    Sensor networks
    Urban science
    Computational architecture

Paper Types

Paper Type: Technique

A technique paper describes a new or significantly improved algorithm or technique in sufficient detail so that other researchers can reproduce the results. This technique should ideally be of general application rather than being restricted to a single task or single source of data, and the exposition should be focused on what the technique does, how it does it, when to use it, and what the computational and other costs are.

Paper Type: System

A system paper describes a solution to a problem where the major task is building a large complex software artifact, applying largely known visualization techniques. Here, the focus should be on the design decisions, the implications for software / hardware structure, and comparison with other systems.

Paper Type: Application

An application paper normally starts with an encapsulated description of a problem domain and the questions to be resolved by visualization, then describes the application of visualization to the task, any novel techniques developed, and how the visualization solution answered the questions posed. Techniques related to a single problem are normally application papers, and evaluation is often limited because many application papers are essentially custom software for a specific problem.

Paper Type: Evaluation

An evaluation paper is usually an empirical assessment of how effective a technique or system is when used by humans. As such, these often involve rigorous experimental protocols and statistical analysis, but this is not the only possible form of evaluation. Good evaluation papers go beyond statistical analysis to explain causes, construct models and predict effectiveness of related systems.

Paper Type: Theory

A theory paper describes aspects of the process by which humans construct visualizations to explore data or communicate with other humans. These papers do not usually involve implementation, but contribute by illuminating the role of visualization in data analysis and often by proposing models for improving visualization as a discipline.
Last updated by Dou Sun in 2017-02-25
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