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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.

Chris Johnson
Chris Johnson
– Scalar, Vector, and Tensor Field Visualization
– Uncertainty Visualization
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Valerio Pascucci
Valerio Pascucci
– Topological Methods
– Data Streaming
– Big Data
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Mike Kirby
Mike Kirby
– Uncertainty Visualization
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Alex Lex
Alex Lex
– Information Visualization
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Bei Wang
Bei Wang
– Information Visualization
– Scientific Visualization
– Topological Data Analysis
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Kate Isaacs
Kate Isaacs
– Data Visualization
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Paul Rosen
Paul Rosen
– Data Visualization
– Human Computer Interaction
– Topological Data Analysis
Profile
Andrew McNutt
Andrew McNutt
– Data Visualization
– Human Computer Interaction
– Programming Interfaces
Profile

Research


Center for Extreme Data Management Analysis and Visualization (CEDMAV)
The Center for Extreme Data Management Analysis and Visualization (CEDMAV) focuses on theoretical and algorithmic research, systems development, and tool deployment for dealing with extreme data.
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Intel oneAPI Center of Excellence
The SCI Institute oneAPI Center of Excellence team is extending its Intel GVI work to pursue new high-performance visual computing methods that utilize oneAPI cross-architecture programming, which delivers performance and productivity, along withprovides the ability to create single source code that takes advantage of CPUs, GPUs and other accelerator technologiescan be deployed across a variety of architectures.
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Topological Data Analysis and Data Visualization (TDAVIS)
TDAVIS focuses on topological data analysis, data visualization, and computational topology. They work on combining topological, geometric, statistical, data mining, and machine learning techniques with visualization to study large and complex data for information exploration and scientific discovery. Some of their current research activities involve the analysis and visualization of high-dimensional point clouds, scalar fields, vector fields, tensor fields, networks, and multivariate ensembles.
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Visualization Design Lab
Interests at the VDL include the process of designing and developing visualizations, visualization for biology, visualization frameworks, and, more generally, visualization of big, heterogeneous, and complex datasets.
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