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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


Yarden Livnat

Information Visualization

alex lex

Alex Lex

Information Visualization


Bei Wang

Information Visualization
Scientific Visualization
Topological Data Analysis

Visualization Project Sites:

Associated Labs:

Publications in Visualization:

Optimization of Volumetric Computed Tomography for Skeletal Analysis of Model Genetic Organisms
S.X. Vasquez, M.S. Hansen, A.N. Bahadur, M.F. Hockin, G.L. Kindlmann, L. Nevell, I.Q. Wu, D.J. Grunwald, D.M. Weinstein, G.M. Jones, C.R. Johnson, J.L. Vandeberg, M.R. Capecchi, C. Keller. In The Anatomical Record: Advances in Integrative Anatomy and Evolutionary Biology, Vol. 291, pp. 475--487. 2008.
PubMed ID: 18286615

Volumetric Parameterization and Trivariate B-spline Fitting using Harmonic Functions
T. Martin, E. Cohen, R.M. Kirby. In Proceedings of ACM Solid and Physical Modeling, Stony Brook, NY, Note: Awarded Best Paper, pp. 269-280. 2008.

Towards the Visualization of Multi-Dimentional Stochastic Distribution Data
K. Potter, J. Krüger, C.R. Johnson. In Proceedings of The International Conference on Computer Graphics and Visualization (IADIS) 2008, pp. 191--196. 2008.

Unified Volume Format: A General System For Efficient Handling Of Large Volumetric Datasets
J. Krüger, K. Potter, R.S. MacLeod, C.R. Johnson. In Proceedings of IADIS Computer Graphics and Visualization 2008 (CGV 2008), pp. 19--26. 2008.
PubMed ID: 20953270

With the continual increase in computing power, volumetric datasets with sizes ranging from only a few megabytes to petascale are generated thousands of times per day. Such data may come from an ordinary source such as simple everyday medical imaging procedures, while larger datasets may be generated from cluster-based scientific simulations or measurements of large scale experiments. In computer science an incredible amount of work worldwide is put into the efficient visualization of these datasets. As researchers in the field of scientific visualization, we often have to face the task of handling very large data from various sources. This data usually comes in many different data formats. In medical imaging, the DICOM standard is well established, however, most research labs use their own data formats to store and process data. To simplify the task of reading the many different formats used with all of the different visualization programs, we present a system for the efficient handling of many types of large scientific datasets (see Figure 1 for just a few examples). While primarily targeted at structured volumetric data, UVF can store just about any type of structured and unstructured data. The system is composed of a file format specification with a reference implementation of a reader. It is not only a common, easy to implement format but also allows for efficient rendering of most datasets without the need to convert the data in memory.

The Need For Verifiable Visualization
R.M. Kirby, C.T. Silva. In IEEE Computer Graphics and Applications, Vol. 28, No. 5, pp. 78--83. 2008.
DOI: 10.1109/MCG.2008.103

Visualization is often employed as part of the simulation science pipeline, it's the window through which scientists examine their data for deriving new science, and the lens used to view modeling and discretization interactions within their simulations. We advocate that as a component of the simulation science pipeline, visualization must be explicitly considered as part of the validation and verification (V&V) process. In this article, the authors define V&V in the context of computational science, discuss the role of V&V in the scientific process, and present arguments for the need for verifiable visualization.

Visual Analysis of Bioelectric Fields
X. Tricoche, R.S. MacLeod, C.R. Johnson. In Visualization in Medicine and Life Sciences, Mathematics and Visualization, Springer-Verlag, pp. 205--220. 2008.

Who Votes For What? A Visual Query Language for Opinion Data
G. Draper, R. Riesenfeld. In IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, IEEE, pp. 1197--1204. Nov, 2008.
DOI: 10.1109/tvcg.2008.187

Fast Isosurface Extraction Methods for Large Image Data Sets
Y. Livnat, S.G. Parker, C.R. Johnson. In Handbook of Medical Image Processing and Analysis, 2nd edition, Ch. 47, Note: (to appear), Edited by Isaac N. Bankman, Elsevier, pp. 801--816. 2008.

DOE Visualization and Knowledge Discovery
C.R. Johnson, R. Ross, S. Ahern, J. Ahrens, W. Bethel, K.L. Ma, M. Papka, J. van Rosendale, H.W. Shen, J. Thomas. Note: Report from the DOE/ASCR Workshop on Visual Analysis and Data Exploration at Extreme Scale, October, 2007.

CRA-NIH Computing Research Challenges in Biomedicine Workshop Recommendations
D. Reed, C.R. Johnson. Note: Computing Research Association (CRA), 2007.

Dynamically identifying and tracking contaminants in water bodies
C.C. Douglas, M.J. Cole, P. Dostert, Y. Efendiev, R.E. Ewing, G. Haase, J. Hatcher, M. Iskandarani, C.R. Johnson, R.A. Lodder. In Proceedings of the 7th International Conference on Computational Science (ICCS) 2007, Part I, Beijing, China, Lecture Notes in Computer Science (LNCS), Vol. 4887, Edited by Y. Shi and G.D. van Albada and P.M.A. Sloot and J.J. Dongarra, Springer-Verlag, Berlin Heidelberg, pp. 1002--1009. May, 2007.

Dynamic data-driven application systems for empty houses, contaminat tracking, and wildland fireline prediction
C.C. Douglas, D. Bansal, J.D. Beezley, L.S. Bennethum, S. Chakraborty, J.L. Coen, Y. Efendiev, R.E. Ewing, J. Hatcher, M. Iskandarani, C.R. Johnson, M. Kim, D. Li, R.A. Lodder, J. Mandel, G. Qin, A. Vodacek. In Grid-Based Problem Solving Environments, IFIP series, Edited by P.W. Gaffney and J.C.T. Pool, Springer-Verlag, Berlin, pp. 255-272. 2007.
DOI: 10.1007/978-0-387-73659-4_14

SciDAC Visualization and Analytics Center for Enabling Technology
E.W. Bethel, C.R. Johnson, K. Joy, S. Ahern, V. Pascucci, H. Childs, J. Cohen, M. Duchaineau, B. Hamann, C.D. Hansen, D. Laney, P. Lindstrom, J. Meredith, G. Ostrouchov, S.G. Parker, C.T. Silva, A.R. Sanderson, X. Tricoche. In Journal of Physics, Conference Series, Vol. 78, No. 012032, pp. (published online). 2007.

Fast Isosurface Extraction Methods for Large Image Data Sets
Y. Livnat, S.G. Parker, C.R. Johnson. In Handbook of Medical Imaging: Processing and Analysis, 2nd Edition, Ch. 44, Edited by Isaac Bankman, Academic Press, 2007.

DOE's SciDAC Visualization and Analytics Center for Enabling Technologies - Strategy for Petascale Visual Data Analysis Success
E.W. Bethel, C.R. Johnson, C. Aragon, Prabhat, O. Rübel, G. Weber, V. Pascucci, H. Childs, P.-T. Bremer, B. Whitlock, S. Ahern, J. Meredith, G. Ostrouchov, K. Joy, B. Hamann, C. Garth, M. Cole, C.D. Hansen, S.G. Parker, A.R. Sanderson, C.T. Silva, X. Tricoche. In CTWatch Quarterly, Vol. 3, No. 4, 2007.

NIH/NSF Visualization Research Challenges Report,
C.R. Johnson, R. Moorhead, T. Munzner, H. Pfister, P. Rheingans, T.S. Yoo, (Eds.). Note: IEEE Press, 2006.
ISBN: 0-7695-2733-7

NSF Blue Ribbon Panel Report on Simulation Based Engineering Science
J.T. Oden, J. Fish, C.R. Johnson, A. Laub, D. Srolovitz, T. Belytschko, T.J.R. Hughes, D. Keys, L. Petzold, S. Yip. Note: NSF Report, 2006.

Purpose: To explore the emerging discipline of Simulation Simulation-Based Engineering Science, its major components, its importance to the nation, the challenges and barriers to its advancement, and to recommend to the NSF and the broader community concerned with science and engineering in the United States, steps that could be taken to advance development in this discipline.
PPT Presentation

Biomedical Computing and Visualization
C.R. Johnson, D.M. Weinstein. In Proceedings of the Twenty-Ninth Australasian Computer Science Conference (ACSC2006): Conferences in Research and Practice in Information Technology (CRPIT), Hobart, Australia, Vol. 48, Edited by Vladimir Estivill-Castro and Gill Dobbie, pp. 3-10. 2006.

NIH/NSF Visualization Research Challenges Report Summary
C.R. Johnson, R. Moorhead, T. Munzner, H. Pfister, P. Rheingans, T. Yoo. Note: NIH/NSF, pp. 66-73. 2006.

VACET: Proposed SciDAC2 Visualization and Analytics Center for Enabling Technologies
W. Bethel, C.R. Johnson, C.D. Hansen, S.G. Parker, A.R. Sanderson, C.T. Silva, X. Tricoche, V. Pascucci, H. Childs, J. Cohen, M. Duchaineau, D. Laney, P. Lindstrom, S. Ahern, J. Meredith, G. Ostrouchov, K. Joy, B. Hamann. In J. Phys.: Conf. Ser., Vol. 46, pp. 561--569. 2006.