Visualization
Visualization, sometimes referred to as visual data analysis, uses the graphical representation of data as a means of gaining understanding and insight into the data. Visualization research at SCI has focused on applications spanning computational fluid dynamics, medical imaging and analysis, biomedical data analysis, healthcare data analysis, weather data analysis, poetry, network and graph analysis, financial data analysis, etc.Research involves novel algorithm and technique development to building tools and systems that assist in the comprehension of massive amounts of (scientific) data. We also research the process of creating successful visualizations.
We strongly believe in the role of interactivity in visual data analysis. Therefore, much of our research is concerned with creating visualizations that are intuitive to interact with and also render at interactive rates.
Visualization at SCI includes the academic subfields of Scientific Visualization, Information Visualization and Visual Analytics.
Charles HansenVolume RenderingRay Tracing Graphics |
Valerio PascucciTopological MethodsData Streaming Big Data |
Chris JohnsonScalar, Vector, andTensor Field Visualization, Uncertainty Visualization |
Mike KirbyUncertainty Visualization |
Ross WhitakerTopological MethodsUncertainty Visualization |
Miriah MeyerInformation Visualization |
Yarden LivnatInformation Visualization |
Alex LexInformation Visualization |
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Visualization Project Sites:
- POWDER Display Wall
- Modeling, Display, and Understanding Uncertainty in Simulations for Policy Decision Making
- NSF-OCI: A GPU-Enabled Toolbox for Solving Hamilton-Jacobi and Level Set Equations on Unstructured Meshes
- ViSUS
- Uncertainty Quantification and Visualization
- Analysis and Visualization of Stochastic Simulation Solutions
- NSF-CRI: A Hierarchical Data Storage System
- Topological Data Analysis for Large Network Visualization