Director, Center for Extreme Data Management Analysis and Visualization, Professor, Scientific Computing and Imaging Institute, School of Computing, University of Utah DOE Laboratory Fellow at the Pacific Nothwest National Laboratory, Computational Sciences & Mathematics Division
Erdös number: 3 (via Janos Pach and Herbert Edelsbrunner) 

Valerio Pascucci is the founding Director of the Center for Extreme Data Management Analysis and Visualization (CEDMAV) of the University of Utah. Valerio is also a Faculty of the Scientific Computing and Imaging Institute, a Professor of the School of Computing, University of Utah, and a DOE Laboratory Fellow, of the Pacific Northwest National Laboratory. Previously, Valerio was a Group Leader and
Project Leader in the Center for Applied Scientific Computing at the
Lawrence Livermore National Laboratory, and Adjunct Professor of
Computer Science at
the University of California
Davis. Prior to his CASC tenure, he was a senior research
associate at the University of Texas
at Austin, Center for
Computational Visualization, CS
and TICAM
Departments. Valerio earned a Ph.D. in computer science at Purdue
University in May 2000, and a EE Laurea (Master), at the University
"La Sapienza" in Roma, Italy, in December 1993, as a member of the
Geometric
Computing Group. Valerio came to the U.S. in 1995 after having
grown up in Roma,
Italy.

Interactive editing of massive imagery made simple: Turning Atlanta into Atlantis. B. Summa, G. Scorzelli, M. Jiang, P.T. Bremer, and V. Pascucci, ACM Transactions on Graphics (TOG) TOG, Volume 30 Issue 2, 2011, pp. 7:17:13. To be presented at SIGGRAPH 2011. Paper. 



Parallel Gradient Domain Processing of Massive Images. S. Philip, B. Summa, P.T. Bremer, and V. Pascucci, Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization 2011, pp. 1119. Paper. 

Visual Exploration of High Dimensional Scalar Functions . S. Gerber, P.T. Bremer, v. Pascucci, and R. Whitaker, IEEE Transactions on Visualization and Computer Graphics 16(6), pp. 12711280, 2010. Paper. 

Interactive Exploration and Analysis of Large Scale Simulations Using Topologybased Data Segmentation. P.T. Bremer, G. Weber, J. Tierny, V. Pascucci, M. Day, and J. Bell, IEEE Transactions on Visualization and Computer Graphics 99, 2010. Paper. 

Analyzing and Tracking Burning Structures in Lean Premixed Hydrogen Flames. P.T. Bremer, G. Weber, V. Pascucci, M. Day, and J. Bell, IEEE Transactions on Visualization and Computer Graphics 16(2), 2010. Paper. 

StreamingEnabled Parallel Dataflow Architecture for Multicore Systems. H. T. Vo, D. K. Osmari, B. Summa, J. L. D. Comba, V. Pascucci, and C. T. Silva, Comput. Graph. Forum, 29(3), pp. 10731082, 2010, Proceedings of IEEE/VGTC EuroVis 2010. Paper. 

Topologically Clean Distance Fields. A. Gyulassy, V. Natarajan, Mark Duchaineau , Valerio Pascucci, Eduardo M. Bringa, Andrew Higginbotham, and Bernd Hamann, IEEE Transactions on Visualization and Computer Graphics 13(6), pp. 14321439, Proceedings of VIS 2007. Paper. 

Efficient Computation of MorseSmale Complexes for Threedimensional Scalar Functions. A. Gyulassy, V. Natarajan, Valerio Pascucci, and Bernd Hamann, IEEE Transactions on Visualization and Computer Graphics 13(6), pp. 14401447, Proceedings of VIS 2007. Paper. 

Genus Oblivious Cross Parameterization: Robust Topological Management of Intersurface Maps. Janine Bennett, Valerio Pascucci, and Ken Joy, Proceedings of Pacific Graphics 2007 pp. 238247, IEEE Computer Society, 2007. Paper. 

Topological Landscapes: A Terrain Metaphor for Scientific Data. Gunther Weber , P.T. Bremer, and Valerio Pascucci, IEEE Transactions on Visualization and Computer Graphics to appear, Proceedings of VIS 2007. Paper. 

Robust Online Computation of Reeb Graphs: Simplicity and Speed. V. Pascucci, G. Scorzelli, P.T. Bremer, and A. Mascarenhas, ACM Transactions on graphics, pp. 58.158.9, 2007, Proceedings of SIGGRAPH 2007. Paper. Abstract. Find undesired tunnels in a 3D model using the Reeb graph (106MB video). Reeb graph of a running horse (7.7MB video). 



Progressive Volume Rendering of Large Unstructured Grids. S. Callahan, L. Bavoil, V. Pascucci, and C. Silva, IEEE Transactions on Visualization and Computer Graphics Vol. 12, No. 5, pp. 13071314, 2006. Proceedings of IEEE VIS 2006. (pdfpaper) 

Spectral surface quadrangulation. S. Dong, P.T. Bremer, M. Garland, V. Pascucci, and J. Hart, ACM Transactions on Graphics, Volume 25 , Issue 3, pp.10571066qs (July 2006). Proceedings of SIGGRAPH 2006 (pdfpaper) 

PersistenceSensitive Simplification of Functions on
2Manifolds. H. Edelsbrunner, D. Morozov, and V. Pascucci, In Proceeding of the 22th ACM Symposium on Computational Geometry (SoCG), 2006. (pdfpaper) 

Streaming Simplification of Tetrahedral Meshes. H. Vo, S. Callahan, P. Lindstrom, V. Pascucci, and C. Silva, IEEE Transactions on Visualization and Computer Graphics, to appear. 

Topologybased Simplification for Feature Extraction from 3D Scalar Fields A. Gyulassy, V. Natarajan, V. Pascucci, P.T. Bremer, and Bernd Hamann, IEEE Visualization 2005, pages 275280, 2005. (pdfpaper) 

CacheOblivious Mesh Layouts S.E. Yoon, P. Lindstrom, V. Pascucci, and D. Manocha, SIGGRAPH 2005, (ACM Transactions on Graphics), vol 24, n. 3,pages 886893, 2005. (pdfpaper) (webpage) (source code) 

MultiResolution computation and presentation of Contour Trees V. Pascucci, K. ColeMcLaughlin, and G. Scorzelli, LLNL Technical Report number UCRLPROC208680. Preliminary version appeared in the proceedings of the IASTED conference on Visualization, Imaging, and Image Processing (VIIP 2004), 2004, pp.452290. (pdfpaper) (Linux and windows demo) 

Local and Global Comparison of Continuous Functions H. Edelsbrunner, J. Harer, V. Natarajan, and V. Pascucci, IEEE Conference on Visualization, 2004, pages 275280. (pdfpaper) 

Implicit Occluders S. Pesco, P. Lindstrom, V. Pascucci, and C. Silva, IEEE/SIGGRAPH Symposium on Volume Visualization, 2004, pages 4754. (pdfpaper) 

Encoding Volumetric Grids For Streaming Isosurface Extraction A. Mascarenhas, M. Isenburg, V. Pascucci, and J. Snoeyink, In Proceeding of the 2nd International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), 2004, to appear. (pdfpaper) 

Progressive DimensionIndependent Boolean Operations A. Paoluzzi, V. Pascucci, and G. Scorzelli, In Proceeding of the 9th ACM Symposium on Solid Modeling and Applications (SM04), 2004, pages 203211. (pdfpaper) (mechanicalpieceavianimation) (templeavianimation) 

Timevarying Reeb Graphs for Continuous SpaceTime Data H. Edelsbrunner, J. Harer, A. Mascarenhas, and V. Pascucci, In Proceeding of the 20th ACM Symposium on Computational Geometry (SoCG), 2004, pages 366372. (pdfpaper) 

Topology Diagram of Scalar Fields in Scientific Visualization V. Pascucci, Chapter 8 in Topological Data Structures for Surfaces, March, 2004. (bookwebsite) 

Contour Trees and Small Seed Sets for Isosurface Generation M. van Kreveld, R. van Oostrum, C. Bajaj, V. Pascucci and D. Schikore Chapter 5 in Topological Data Structures for Surfaces, March, 2004. (bookwebsite) 

Isosurface Computation Made Simple: Hardware Acceleration, Adaptive Refinement and Tetrahedral Stripping V. Pascucci, Joint Eurographics  IEEE TVCG Symposium on Visualization (VisSym), 2004, pages 293300. (pdfpaper) 

A Multiresolution Data Structure for Twodimensional Morse Functions P.T. Bremer, H. Edelsbrunner, Bernd Hamann, and V. Pascucci, IEEE Conference on Visualization, 2003, pages 139146. (pdfpaper) (avianimation, may require Vidomi Player) 

A MultiLayered Image Cache for Scientific Visualization E. LaMar, and V. Pascucci, IEEE Symposium on Parallel and LargeDataVisualization and Graphics (PVG), 2003, pages 6168. (pdfpaper) (pdfpresentation) 

Geometric Programming for Computer Aided Design A. Paoluzzi, with contributions from V. Pascucci, M.Vicentino, C. Baldazzi, and S. Portuesi John Wiley and Sons, 2003. (pdftableofcontetnts) (buythebook) (visitthePLASMsite) (vrmlS._Stefano_Rotondo) 

Parallel Computation of the Topology of Level
Sets V. Pascucci, K. ColeMcLaughlin, Algorithmica, vol. 38, n. 2, October 2003, pages 249268. (pdfpaper) 

 Loops
in Reeb Graphs of 2Manifolds K. ColeMcLaughlin, H. Edelsbrunner, J. Harer, V. Natarajan, and V. Pascucci. In Proceeding of the 19th ACM Symposium on Computational Geometry (SoCG), 2003, pages 344350. (pdfpaper) (pdfpresentation) 

Morse Complexes for Piecewise Linear
3Manifolds H. Edelsbrunner, J. Harer, V. Natarajan, and V. Pascucci,. In Proceeding of the 19th ACM Symposium on Computational Geometry (SoCG), 2003, pages 361370. (pdfpaper) (pdfpresentation) 

Using Graphs for Fast Error Term
Approximation of Timevarying Datasets C. Nuber, E. C. LaMar, V. Pascucci, Bernd Hamann, K. I. Joy, In Proceedings of Eurographics VisSym 2003, to appear. (pdfpaper) 

RealTime Monitoring of Large Scientific Simulations V. Pascucci, D. Laney, R. J. Frank, G. Scorzelli, L. Linsen, Bernd Hamann, and F. Gygi, In Proceedings of the 18th annual ACM Symposium on Applied Computing, March, 2003, Melbourne, FL, pages 194198. (pdfpaper) (pdfpresentation) 

Dynamic
maintenance and visualization of molecular surfaces, C. Bajaj, V. Pascucci, A. Shamir, R. J. Holt, and A. N. Netravali, Discrete Applied Mathematics, Volume 127, Issue 1, April 2003, pages 2351. (pdfpaper) (mpeganimation) 

Hierarchical Indexing for OutofCore Access to MultiResolution Data V. Pascucci and , R. J. Frank, Chapter in Hierarchical and Geometrical Methods in Scientific Visualization, 2002, pages 225241. (pdfpaper) 

Interactive Viewdependent Rendering of Large Isosurfaces B. Gregorski, Mark Duchaineau, P. Lindstrom, V. Pascucci, and , K. I. Joy, In Proceedings of IEEE Conference on Visualization, 2002, pages 475482. (pdfpaper) 

Slow Growing
Subdivision (SGS) in Any Dimension: Towards Removing the Curse of
Dimensionality, V. Pascucci, Computer Graphics Forum, vol. 21, n. 3, September 2002, pages 451460, (Proceeding of Eurographics 2002). (pdfpaper) (pdfpresentation) 

Terrain Simplification Simplified: A General Framework for ViewDependent OutofCore Visualization, P. Lindstrom and V. Pascucci, IEEE Transactions on Visualization and Computer Graphics, vol. 8, n.3, JulySeptember 2002, pages 239254. (pdfpaper) (mpeganimation) 

Finding line segments with Tabu Search. 

Global Static Indexing for Realtime Exploration of Very Large Regular Grids. 

Temporal and Spatial Level of Details for Dynamic Meshes. 

Visualization of Large Terrains Made Easy. 

Time critical isosurface refinement and smoothing, V. Pascucci and C. Bajaj, In Proceeding of the ACM Symposium on Volume Visualization and Graphics (VolVis), 2000, pages 2242. (pdfpaper) (pdfpresentation) 

MultiResolution Dynamic Meshes with Arbitrary Deformations. 

Progressive Compression and Transmission of Arbitrary Triangular Meshes. 

Parallel accelerated isocontouring for outofcore visualization. 

Hypervolume visualization: A challenge in simplicity. 

The Contour Spectrum, C. Bajaj, V. Pascucci, and D. R. Schikore In Proceedings of IEEE Conference on Visualization, 1997, pages 167175. (pdfpaper) (pdfpresentation) 

Contour trees and small seed sets for
isosurface traversal. M. van Kreveld, R. van Oostrum, C. Bajaj, V. Pascucci, and D. R. Schikore, In Proceedings of the 13th ACM Annual Symposium on Computational Geometry (SoCG),1997, pages 212220. ACM Press. (pdfpaper) 

Fast Isocontouring for Improved Interactivity. C. Bajaj, V. Pascucci, and D. R. Schikore, In Proceedings of the IEEE, ACM/SIGGRAPH Symposium Volume Visualization 1996, pages 3946. ACM Press. (pdfpaper) 

NURBS based
Brep models for macromolecules and their properties. C. Bajaj, H. Y. Lee, R. Merkert, and V. Pascucci In Proceedings of the 4th Symposium on Solid Modeling and Applications, pages 217228, New York, May 1416 1997. ACM Press. (pdfpaper) 

Splitting a complex of convex polytopes in any dimension, C. Bajaj, and V. Pascucci In Proceedings of the 12th ACM Symposium On Computational Geometry (SoCG), 1996, pages 8897. (pdfpaper) 

Prototype shape modeling with a design language, A. Paoluzzi, V. Pascucci, and C. Sansoni In Proceedings of the Fifth International Conference on ComputerAided Design Futures, (CAAD Futures '95), 1995, pages 127141. (pdfpaper) 

The size of scientific data sets that are generated by evolving supercomputers, large sensor networks, and highresolution imaging devices is increasing rapidly, at an exponential rate. This project addresses the need for more effective data analysis methods. It develops technologies concerned with the analysis and representation of very large scientific data sets, emphasizing concepts that capture qualitative characteristics. In light of the limitations of purely visualizationbased approaches applied to "raw" scientific data sets directly, this project aims at devising new concepts for visualizing very large and complex data sets. The methods being developed first extract meaningful qualitative information from a given data set, which is then used to present the higherlevel information content of the data set in a significantly more compact form, thus stressing relevant qualitative behavior. The project builds on concepts from classical topology and geometry, which have contributed substantially to the development of the relatively new fields of computational topology and computational geometry. These two fields hold great potential for substantially advancing the visualization technology for understanding extremely large, complicated data sets. This projects adapts (and generalizes) computational topology and computational geometry algorithms that are wellestablished for smooth mathematical functions to realworld, finitesample data sets, i.e., functions sampled at a finite number of points (that could possibly be connected by a mesh). Realworld data sets are noisy, which further complicates the application of topological methods that were developed originally for smooth functions. This project investigates the generalization of techniques based on Morse and MorseSmale theory (studying criticalpoint behavior and drawing qualitative conclusions about functions) to discretized scalar fields that change over time and also contain noise. 
Development of software tools that run on embedded systems involves a number of challenges ranging from porting to specialized processors, running with extremely limited memory, reduced OS support, and dealing with particularly slow and imbalanced hardware performance. We propose a practical study of these challenges with focus on the Progressive Image Analysis and develop and adaptation to devices using an ARM core processor (used in digital cameras, GPS, hand held computers, smartcards, wristwatches, USB hard drives, ipod, and more) using linux and windows CE operating systems. Our concrete goal is to program an ARM system with 32/64 MB of memory to display in real time gigabytes to terabytes of high resolution satellite imagery and KML vector information. With this focused effort we will (i) produce a prototype tool of practical interest for the geospatial intelligence community and (ii) develop important expertise in formal and practical methods that allow successful development and deployment of software tools for embedded systems that can be used effectively on the field for defense and intelligence operations. 
In this project we conduct basic research in progressive algorithms for the realtime analysis of streaming data. The focus is on enabling feature and anomaly detection in massive amounts of image data. The project explores the use of hierarchical data representations and storage layout designs to achieve high performance capabilities. The utility of novel architectures, such as GPUbased clusters and/or cellbased machines, is also explored. We also investigate the support of realtime continuous and ad hoc queries over streaming image data, including the design of dynamic algorithms for query optimization enabling efficient usage of computing and storage resources. The results of this research will be useful to nextgeneration feature detection software tools for analysis of high resolution satellite imagery of interest to NGA and others. 
In this project we develop a new visualization framework based on the coupling of generalpurpose data analysis tools with Information Visualization techniques to allow rapid computation and effective presentation of metadata roadmaps guiding scientists in the exploration through terabytes of raw data. We use Morse theory as the mathematical foundation for the analysis, extracting multiscale models highlighting fundamental characteristics ubiquitous in scientific data, based on topology. Morse theory can be used in a wide range of applications such as the comparison of simulation results, validation of simulations with experimental data, image segmentation, or reconstruction of structures emerging in simulations from molecular dynamics, material sciences and combustion chemistry. The core of our technology is based on the development of combinatorial algorithms that robustly handle real, sampled data while maintaining the formal guarantees of the underlying mathematics of smooth functions. 
In this LDRD project we develop a suite of progressive visualization algorithms and a datastreaming infrastructure that enable interactive exploration of scientific datasets of unprecedented size. The methodology aims to globally optimize the data flow in a pipeline of processing modules. Each module reads a multiresolution representation of the input while producing a multiresolution representation of the output. The use of multiresolution representations provides the necessary flexibility to trade speed for accuracy in the visualization process. Maximum coherency and minimum delay in the dataflow is achieved by extensive use of progressive algorithms that continuously map local geometric updates of the input stream into immediate updates of the output stream. We implement a prototype software infrastructure that demonstrates the flexibility and scalability of this approach by allowing large data visualization on single desktop computers, on PC clusters, and on heterogeneous computing resources distributed over a wide area network. When processing terabytes of scientific data, we have achieved an effective increase in visualization performance of several orders of magnitude in two major settings: (i) interactive visualization on desktop workstations of large datasets that cannot be stored locally; (ii) realtime monitoring of a large scientific simulation with negligible impact on the computing resources available. The ViSUS streaming infrastructure enabled the realtime execution and visualization of the two LLNL simulation codes (Miranda and Raptor) run at Supercomputing 2004 on Blue Gene/L at its presentation as the fastest supercomputer in the world. In addition to SC04, we have run live demonstrations at the IEEE VIS conference and at invited talks at the DOE MICS office, DOE computer graphics forum, UC Riverside, and the University of Maryland. In all cases we have shown the capability to stream and visualize interactively data stored remotely at the San Diego Supercomputing Center or monitor in realtime simulation codes executed on a cluster of PC’s at LLNL. 
Advanced Methods in Scientific Visualization 

Proteomes and Proteins 

 
Multiresolution modeling, visualization,
compression and streaming of volumetric data Organized by: V. Pascucci Instructors: P. Cignoni, L. De Floriani, P. Lindstrom, V. Pascucci, J. Rossignac, and C. Silva, Tutorial for Eurographics, 2003. 

Multiresolution modeling, visualization
and compression of volumetric data Organized by: V. Pascucci Instructors: P. Cignoni, L. De Floriani, V. Pascucci, J. Rossignac, and C. Silva, Tutorial for IEEE Conference on Visualization, 2003. (webpage) 

 Chandrajit Bajaj (Ph.D. Advisor)
 Louis Bavoil
 Janine Bennett
 PeerTimo Bremer
 Steven Callahan
 Kree ColeMcLaughlin
 Shen Dong
 Mark Duchaineau
 Herbert Edelsbrunner
 Michael Garland
 Attila Gyulassy
 Bernd Hamann
 John Harer
 John Hart
 Martin Isenburg
 Ken Joy
 Daniel Laney
 Peter Lindstrom
 Dinesh Manocha
 Ajith Mascarenhas
 Dmitriy Morozov
 Vijay Natarajan
 Alberto Paoluzzi (Master Advisor)
 Guglielmo Rabbiolo
 Giorgio Scorzelli
 Arik Shamir
 Claudio Silva
 Jack Snoeyink
 Huy Vo
 SungEui Yoon
 Gunther Weber