Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.


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 Hansen

Volume Rendering
Ray Tracing

Valerio Pascucci

Topological Methods
Data Streaming
Big Data

Chris Johnson

Scalar, Vector, and
Tensor Field Visualization,
Uncertainty Visualization

Mike Kirby

Uncertainty Visualization

Ross Whitaker

Topological Methods
Uncertainty Visualization

Miriah Meyer

Information Visualization
alex lex

Alex Lex

Information Visualization

Bei Wang

Information Visualization
Scientific Visualization
Topological Data Analysis

Centers and Labs:

Funded Research Projects:

Publications in Visualization:

Quantitative Comparative Evaluation of 2D Vector Field Visualization Methods
D.H. Laidlaw, R.M. Kirby, J.S. Davidson, T.S. Miller, M. da Silva, W.H. Warren, M. Tarr. In Proceedings of IEEE Visualization 2001, San Diego, CA, pp. 143--150. October, 2001.

Topology Preserving Smoothing of Vector Fields
R. Westermann, C.R. Johnson, T. Ertl. In IEEE Trans. Vis & Comp. Graph., Vol. 7, No. 3, pp. 222--229. 2001.
DOI: 10.1109/2945.942690

Proposes a technique for topology-preserving smoothing of sampled vector fields. The vector field data is first converted into a scalar representation in which time surfaces implicitly exist as level sets. We then locally analyze the dynamic behavior of the level sets by placing geometric primitives in the scalar field and by subsequently distorting these primitives with respect to local variations in this field. From the distorted primitives, we calculate the curvature normal and we use the normal magnitude and its direction to separate distinct flow features. Geometrical and topological considerations are then combined to successively smooth dense flow fields, at the same time retaining their topological structure.

Keywords: vector field methods, ip image processing signal processing, surface processing, ncrr

Dynamic Data Driven Application Systems: Creating a dynamic and symbiotic coupling of application/simulations with measurements/experiments
A. Deshmukh, C.C. Douglas, M. Ball, R.E. Ewing, C.R. Johnson, C. Kesselman, C. Lee. Note: 28 pages, Edited by W. Powell, R. Sharpley, National Science Foundation, 2000.

Statistical Analysis For FEM EEG Source Localization in Realistic Head Models
School of Computing Technical Report, L. Zhukov, D. Weinstein, C.R. Johnson. No. UUCS-2000-003, University of Utah, February, 2000.

The Visual Haptic Workbench
J.D. Brederson, M. Ikits, C.R. Johnson, C.D. Hansen, J.M. Hollerbach. In Proceedings of the Fifth PHANToM Users Group Workshop, pp. 46--49. October, 2000.

Fast Isosurface Extraction Methods for Large Image Data Sets
Y. Livnat, S.G. Parker, C.R. Johnson. In Handbook of Medical Imaging, Edited by A.N. Bankman, Academic Press, San Diego, CA pp. 731--745. Nov, 2000.

A Level-Set Method for Flow Visualization
R. Westermann, C.R. Johnson, T. Ertl. In Proceeding of IEEE Visualization 2000, IEEE Computer Society, Salt Lake City pp. 147--154. 2000.

An Inverse EEG Problem Solving Environment and its Applications to EEG Source Localization
D.M. Weinstein, L. Zhukov, C.R. Johnson. In NeuroImage (suppl.), pp. 921. 2000.

Computational Steering and the SCIRun Integrated Problem Solving Environment
S.G. Parker, M. Miller, C.D. Hansen, C.R. Johnson. In Proceedings of Dagstuhl 1997 Workshop on Scientific Visualization, Note: Invited and peer reviewed, Edited by Hans Hagen and Greg Nielson and Frits Post, pp. 257--266. 2000.

Immersive Virtual Reality for Visualizing Flow Through an Artery
A. Forsberg, R.M. Kirby, D.H. Laidlaw, G.E. Karniadakis, A. van Dam, J. Elion. In Proceedings of IEEE Visualization 2000, Salt Lake City, UT, pp. 457--460. October, 2000.

Interactive Source Imaging with BioPSE
D.M. Weinstein, L. Zhukov, C.R. Johnson, S.G. Parker, R. Van Uitert, R.S. MacLeod, C.D. Hansen. In Chicago 2000 World Congress on Medical Physics and Biomedical Engineering, Chicago, IL., Note: Refereed abstract., July, 2000.

Large-Scale Computational Science Applications Using the SCIRun Problem Solving Environment
C.R. Johnson, S.G. Parker, D. Weinstein. In Proceedings of The International Supercomputer Conference 2000, 2000.

The BioPSE Inverse EEG Modeling Pipeline
D.M. Weinstein, P. Krysl, C.R. Johnson. In ISGG 7th International Conference on Numerical Grid Generation in Computation Field Simulations, The International Society of Grid Generation, Mississippi State University pp. 1091--1100. 2000.

The SCIRun Parallel Scientific Computing Problem Solving Environment
C.R. Johnson, S.G. Parker. In Ninth SIAM Conference on Parallel Processing for Scientific Computing, 1999.

The SCIRun Problem Solving Environment: Implementation within a Distributed Environment
M. Miller, C.D. Hansen, C.R. Johnson. In Ninth SIAM Conference on Parallel Processing for Scientific Computing, Note: extended abstract, 1999.

Interactive Simulation and Visualization
C.R. Johnson, S.G. Parker, C.D. Hansen, G.L. Kindlmann, Y. Livnat. In IEEE Computer, Vol. 32, No. 12, pp. 59--65. Dec, 1999.

Visualizing Multivalued Data from 2D Incompressible Flows Using Concepts from Painting
R.M. Kirby, H. Marmanis, D.H. Laidlaw. In Proceedings of IEEE Visualization 1999, San Francisco, CA, pp. 333--340. October, 1999.

Data and Visualization Corridors: Report on the 1998 DVC Workshop Series
R. Stevens, H. Fuchs, A. van Dam, P. Hanrahan, C.R. Johnson, C. McMillan, P. Heermann, S. Louis, T. Defanti, D. Reed, E. Cohen. Note: DOE Report, September, 1998.

The Department of Energy and the National Science Foundation sponsored a series of workshops on data manipulation and visualization of large-scale scientific datasets. Three workshops were held in 1998, bringing together experts in high-performance computing, scientific visualization, emerging computer technologies, physics, chemistry, materials science, and engineering. These workshops were followed by two writing and review sessions, as well as numerous electronic collaborations, to synthesize the results. The results of these efforts are reported here. Across the government, mission agencies are charged with understanding scientific and engineering problems of unprecedented complexity. The DOE Accelerated Strategic Computing Initiative, for example, will soon be faced with the problem of understanding the enormous datasets created by teraops simulations, while NASA already has a severe problem in coping with the flood of data captured by earth observation satellites. Unfortunately, scientific visualization algorithms, and high-performance display hardware and software on which they depend, have not kept pace with the sheer size of emerging datasets, which threaten to overwhelm our ability to conduct research. Our capability to manipulate and explore large datasets is growing only slowly, while human cognitive and visual perception are an absolutely fixed resource. Thus, there is a pressing need for new methods of handling truly massive datasets, of exploring and visualizing them, and of communicating them over geographic distances. This report, written by representatives from academia, industry, national laboratories, and the government, is intended as a first step toward the timely creation of a comprehensive federal program in data manipulation and scientific visualization. There is, at this time, an exciting confluence of ideas on data handling, compression, telepresence, and scientific visualization. The combination of these new ideas, which we refer to as Da ta and Visualization Corridors (DVC), can raise scientific data understanding to new levels and will improve the way science is practiced

An Integrated Problem Solving Environment: The SCIRun Computational Steering System
S.G. Parker, M. Miller, C.D. Hansen, C.R. Johnson, P.-P. Sloan. In 31st Hawaii International Conference on System Sciences (HICSS-31), Vol. VII, Edited by H. El-Rewini, pub-IEEE, pp. 147--156. January, 1998.

Computer Visualization in Medicine
C.R. Johnson. In National Forum, Vol. Fall, pp. 17--21. 1998.