66 Copublished by the IEEE CS and the AIP 1521-9615/06/$20.00 © 2006 IEEE COMPUTING IN SCIENCE & ENGINEERING

Editors: Jim X. Chen, jchen@cs.gmu.edu

R. Bowen Loftin, loftin@tamug.edu

V I S U A L I Z A T I O N C O R N E R

convened the Visualization ResearchChallenges (VRC) Executive Com-mittee—made up of this article’s au-thors—to write a new report. Here, wesummarize that report, available in fullat http://tab.computer.org/vgtc/vrc/and in print.2

The VRC report aims to

• evaluate the progress of the matur-ing visualization field,

• help focus and direct future researchprojects, and

• provide guidance on how to appor-tion national resources as researchchallenges change rapidly in the fast-paced information technology world.

We explore the state of the field, exam-ine visualization’s potential impact onareas of national and international im-portance, and present our findings andrecommendations for the future of thisgrowing discipline. Our audience istwofold: visualization research’s sup-porters, sponsors, and application userson the one hand, and visualization re-searchers and practitioners on the other.Our findings and recommendations re-flect information gathered from visual-ization and applications scientists duringtwo VRC workshops, as well as inputfrom the larger visualization community.

Visualization’s ValueAdvances in computing science andtechnology have engendered unprece-dented improvements in scientific, bio-medical, and engineering research;defense and national security; and in-dustrial innovation. Continuing andaccelerating these advancements willrequire comprehending vast amountsof data and information produced frommyriad sources.3 Visualization—namely,helping people explore or explain datathrough software systems that providea static or interactive visual represen-tation—is critical to achieving thisgoal. Visualization designers can ex-ploit the high-bandwidth channel ofhuman visual perception and help uscomprehend information orders ofmagnitude more quickly than wewould reading raw numbers or text.

Visualization is fundamental to un-derstanding models of complex phe-nomena, such as multilevel models ofhuman physiology from DNA towhole organs, multicentury climateshifts, international financial markets,or multidimensional simulations ofairflow past a jet wing. The “Charac-terizing Flow Visualization Methods”sidebar summarizes a study of severalflow visualization techniques, whereasthe “Fusion Plasma Physics” sidebar

explains how visualization enables ex-periments of complex phenomena. Vi-sualization reduces and refines datastreams, letting us winnow huge vol-umes of data in applications—for ex-ample, public health surveillance at aregional or national level that tracksthe spread of infectious diseases. The“Virus Structures” sidebar shows howvisualization helps structural biologistsunderstand how virus structure corre-lates with strength. Visualizations ofsuch application problems as hurri-cane dynamics and biomedical imag-ing are generating new knowledge thatcrosses traditional disciplinary bound-aries. Visualization can also provideindustry with a competitive edge bytransforming business and engineer-ing practices into more understand-able procedures.

Although well-designed visualiza-tions can help people enormously,naive visualization attempts are all toooften ineffective or even actively mis-leading. Designing effective visualiza-tions is a complex process that requiresa sophisticated understanding of hu-man information-processing capabili-ties, both visual and cognitive, and asolid grounding in the considerablebody of work that already exists in thevisualization field. Further research invisualization—and the transfer of ef-fective visualization methodologiesinto the working practice of medicine,science, engineering, and business—will be critical in handling the ongoinginformation explosion.

Although visualization is itself a dis-cipline, advances in visualization lead

VISUALIZATION RESEARCH CHALLENGESA Report Summary

By Robert Moorhead, Chris Johnson, Tamara Munzner, Hanspeter Pfister,Penny Rheingans, and Terry S. Yoo

N EARLY 20 YEARS AGO, THE US NATIONAL SCIENCE FOUN-

DATION (NSF) CONVENED A PANEL TO REPORT ON VI-

SUALIZATION’S POTENTIAL AS A NEW TECHNOLOGY.1 IN 2004,

THE NSF AND THE US NATIONAL INSTITUTES OF HEALTH (NIH)

JULY/AUGUST 2006 67

inevitably to advances in other disci-plines. Just as knowledge of mathe-matics and statistics has becomeindispensable in subjects as diverse asthe traditional sciences, economics, se-

curity, medicine, sociology, and publicpolicy, so too is visualization becomingindispensable in enabling researchersin other fields. Like statistics, visual-ization is concerned with analyzing and

interpreting information, both quanti-tatively and qualitatively, and with pre-senting data in a way that most clearlyconveys their salient features. Bothfields develop, understand, and abstract

CHARACTERIZING FLOWVISUALIZATION METHODS

F or decades, researchers have been developing and pub-lishing visualization techniques that advance the state of

the art. However, disproportionately few quantitative stud-ies compare visualization techniques. Figure A shows thedifferences between flow visualization methods, showingsix methods for visualizing the same 2D vector field.1 Sub-jects who participated in the user study performed severaltasks, including identifying the type and location of criticalpoints in visualizations. Assuming roughly equal importancefor all tasks, the streamline visualization performed best

overall: on average, subjects were fastest and most accuratewhen using it.

This study produced both quantitative results and a basisfor comparing other visualization methods, creating moreeffective methods, and defining additional tasks to furtherunderstand trade-offs among methods. A future challenge isto develop evaluation methods for more complex 3D time-varying flows.

Reference

1. D.H. Laidlaw et al., “Comparing 2D Vector Field Visualization Meth-

ods: A User Study,” IEEE Trans. Visualization and Computer Graphics,

vol. 11, no. 1, 2005, pp. 59–70.

(1) (2) (3)

(4) (5) (6)

Figure A. Six methods for visualizing the same 2D vector field. They include (1) icons on a regular grid; (2) icons on ajittered grid; (3) layering method inspired by oil painting; (4) line-integral convolution; (5) image-guidedstreamlines; and (6) streamlines seeded on a regular grid. (Figure courtesy of David Laidlaw, Brown University.)

68 COMPUTING IN SCIENCE & ENGINEERING

data-analytic ideas and package them astechniques, algorithms, and softwarefor various application areas.

However, despite visualization’s im-portance to discovery, security, andcompetitiveness, support for researchand development in this critical, multi-disciplinary field has been inadequate.Unless we recommit ourselves to sup-porting visualization research, devel-opment, and technology transfer, we’llsee a decline in the progress of discov-ery in the other important disciplinesthat depend on visualization. As thesedisciplines lose their ability to harnessand interpret information, the discov-ery rate itself will decline. In the in-evitable chain reaction, the US willlose its competitive edge in businessand industry.

State of the Field:InfrastructureOne of the 1987 NSF report’s1 key em-phases was the need for a national in-frastructure to enable visualizationresearch and application. Many of thereport’s needs have been satisfied, whileothers remain a challenge.

Visualization HardwareMany of the original report’s hardwareconcerns have been allayed over timeand by Moore’s law. Processors withwhat used to be considered supercom-puter-class power are now available incommodity desktop PCs that cost a fewthousand dollars. Graphics perfor-mance that used to require special-pur-pose workstations costing tens orhundreds of thousands of dollars isnow available as a commodity graphicscard for a few hundred. Fast and cheaphardware aimed at the business and en-tertainment mass markets allows un-precedented access to computationaland graphics power for visualization—a boon for both visualization resear-chers and end users. The latestgeneration of programmable graphicspipelines on these cards are flexibleenough to spark an explosion of so-phisticated processing techniques thatare now feasible in real time, benefitingvisualization users who need to explorelarge data sets.

Developers have also made greatadvances in visualization-specific hard-ware. Volume rendering is extremely

computationally intensive. A greatdeal of academic and industrial re-search in software volume-renderingalgorithms helped the field matureand finally made hardware creationfeasible. Grant-funded university re-search that began at the State Univer-sity of New York (SUNY) led to thedevelopment of an actual productthrough Mitsubishi Electric ResearchLab (MERL), culminating in the suc-cessful spin-off company TeraRecon(www.terarecon.com).

In contrast, display technology im-provements have historically laggedfar behind the Moore’s law curve. Inthe past 20 years, cathode-ray-tube(CRT) displays have little more thandoubled in physical display size andresolution and have retained the sameweight and form factor. In the past,our ability to design user interfaceshas been constrained because moni-tors are relatively heavy and expen-sive. However, recent breakthroughsin flat-panel and projector technologyhave broken the CRT’s stranglehold.The combination of low cost, highresolution, and freedom from CRTs’

V I S U A L I Z A T I O N C O R N E R

FUSION PLASMA PHYSICS

A tokamak is a doughnut-shaped chamber used in fusionresearch. During tokamak experimental operation,

when a plasma is heated and confined magnetically, eventsoccasionally occur that rapidly terminate the plasma dis-charge. For future experiments, such as the InternationalThermonuclear Experimental Reactor (ITER), the stored en-ergy will be approximately 100 times greater than in pre-sent-day devices. Thus, these disruptions1 have the potentialto severely damage the material wall, especially if the heatflux is highly localized. Figure B shows the results of a simu-lation of a particular disruption in the DIII-D tokamak. Thefigure presents visualizations of the temperature isosurfaces,the magnetic field lines, and contours of the heat flux on thewall, which show toroidal and poloidal localization as a re-sult of the magnetic field lines’ topology. The localization re-sults from the plasma instability compressing the flux surfacein the plasma’s core and then transporting the resulting “hotspots” to the wall. Visualizing the data in 3D shows wherethe plasma wants to preferentially bulge, enabling the devel-opment of improved disruption mitigation techniques.

Reference

1. S.E. Kruger, D.D. Schnack, and C.R. Sovinec, “Dynamics of the Major

Disruption of a DIII-D Plasma,” Physics of Plasmas, vol. 12, no.

056113, 2005.

Figure B. Tokamak disruption simulation. The imagepresents visualizations of the temperature isosurfaces,the magnetic field lines, and contours of the heat fluxon the wall.

JULY/AUGUST 2006 69

weight and bulk constraints have ledto an explosion of computer-drivendisplays in new contexts that go be-yond simply replacing a user’s CRTwith a sleek flat-panel display that hasa smaller footprint.

The primary limitation in interac-tive visualization interfaces is thenumber of available pixels; we’repixel-bound, not CPU-bound or evenrender-bound, and currently face ashortage. High-resolution displayswill let us investigate new and excitingareas of the interface design space asdisplays approach paper-like resolu-tion. We can create wall-sized displayswith a resolution of dozens or even

hundreds of megapixels by tiling pro-jectors’ output. Although active sur-faces will still be relatively expensivein the near term, a longer-term visionincludes gigapixel displays that willeventually be as cheap, lightweight,and ubiquitous as wallpaper. Physi-cally large displays that encompass anobserver’s entire viewing field enableapplications that use peripheral visionas well as the foveal vision we cur-rently use with medium-sized desktopdisplays. Small-gadget displays willhave the one-megapixel resolutionthat we typically associate with desk-top displays. Small handhelds areubiquitous and portable, and, when

networked, can be used as controlpanels for a shared large display.

NetworkingThe visualization field has benefitedgreatly from the Internet’s expansionand commoditization. However, asdata sets continue to grow, it’s still dif-ficult—and sometimes prohibitive—tomove large-scale data sets across eventhe fastest networks to visualize datalocally. As such, we need advancesboth in networking and remote andcollaborative visualization algorithms,such as view-dependent algorithms,image-based rendering, multiresolu-tion techniques, importance-based

VIRUS STRUCTURE

Determining a virus’s 3D structure is the firststep toward understanding its virulent func-tion. A new experimental imaging approachuses cryo-electron microscopy (cryo-EM) forelucidating single-particle macromolecularstructures (such as viruses) at the highestquasi-atomic resolution, typically around 10Å. Figures C1 through C3 show visualizationsusing combined surface and volume render-ing of the same half-shell model of the RiceDwarf Virus (RDV).1 The different colors showthe nucleo-capsid shell from the outside (Fig-ures C1 and C3) and the inside (Figure C2),elucidating the local and global complexity ofeach structural protein unit’s quasi-symmetricpacking. You can see each unit, exhibiting atrimeric fold, further visualized from two dif-ferent views (Figures C4 and C5), as well aswith different colors (Figure C6), to show thethree different conformations of the mono-meric protein chain forming the trimericstructure unit. All of the structure units are au-tomatically segmented from a reconstructed3D electron-density map. Previously, struc-tural biologists have largely attempted these 3D ultrastruc-ture elucidation steps manually. The quasi-atomic models ofthese viruses and other macromolecular machines, con-structed via this structure-elucidation pipeline, provide mi-crobiologists and drug-discovery scientists with crucialinsight into molecular interactions within macro-assemblies.Such complexes’ large physical size and complexity, com-bined with cryo-EM’s very low signal-to-noise ratio, still pre-

sent significant computational and experimental challengesin this research.

Reference

1. Z. Yu and C. Bajaj, “Automatic Ultra-Structure Segmentation of Re-

constructed Cryo-EM Maps of Icosahedral Viruses,” IEEE Trans. Image

Processing: Special Issue on Molecular and Cellular Bioimaging, vol. 14,

no. 9, 2005, pp. 1324–1337.

(1)

(4) (5) (6)

(2) (3)

Figure C. The Rice Dwarf Virus. These visualizations combine surfaceand volume rendering for the same half-shell model of the virus. Thedifferent colors show the nucleo-capsid shell from (1 and 2) theoutside and (3) the inside. Each structural protein unit is furthervisualized from (4 and 5) two different views as well as with (6)different colors.

70 COMPUTING IN SCIENCE & ENGINEERING

methods, and adaptive resource-awarealgorithms.

Visualization SoftwareDataflow toolkits, introduced morethan 15 years ago, heralded the firstgeneration of general-purpose visual-ization software. Various application-specific and open-source packages haveappeared since, and many are in wide-spread use today. Several are delineatedin the new VRC report.2

A trade-off exists between quicklycreating a one-off prototype that suf-fices for a research paper but is toobrittle for general use and devoting thetime to create releasable code at the ex-pense of progressing on the nextresearch project. One benefit for re-searchers of releasing code to a usercommunity is that real-world use typi-cally spawns new research challengesstrongly tied to real problems. Suchties are extremely important for a ma-

turing visualization field. With open-source models, the user community it-self sometimes takes over some or allof the support burden. Releasing soft-ware doesn’t have to be a gargantuantask; often, people who find that a par-ticular piece of research softwareclosely matches their needs are happyto use software that is less polishedthan a commercial product.

The Visualization Toolkit (VTK;www.vtk.org) system4 began as an open-source initiative within General Elec-tric’s Global Research division, and hasrapidly moved into mainstream use inuniversities, national laboratories, andindustrial research labs worldwide (seethe “Visualization Toolkit” sidebar forexamples). It continues to accelerate de-velopment by providing reusable soft-ware, relieving programmers fromreinventing necessary infrastructure.The spin-off company Kitware (www.kitware.com) is built around an open-

source business model, in which cus-tomers can pay for support and cus-tomization while the communitycontinues to develop the free codebase.Similarly, the Insight Segmentation andRegistration Toolkit (ITK; www.itk.org)was an NIH open-source software ini-tiative intended to support a worldwidecommunity in image processing anddata analysis. It can interface openlywith visualization platforms such asVTK and SCIRun, the University ofUtah’s visualization system, which hasalso moved to open-source infrastruc-ture software to ease its integration intopublic and private research.

FundingWe note with particular concern therecent article by ACM President DavidPatterson5 that documents the declinein both industrial and military fundingfor basic research. Moreover, an in-creasingly low percentage of NSF pro-

V I S U A L I Z A T I O N C O R N E R

THE VISUALIZATIONTOOLKIT

I n 1993, three visualization re-searchers from General Electric’s

corporate R&D center began to de-velop an open-source visualizationsystem, known as the VisualizationToolkit (VTK). VTK was initially envi-sioned as a teaching and researchcollaboration tool, hence its releaseunder an open-source license. Thesoftware gained rapid acceptance, inpart due to the sophistication of itsobject-oriented design and softwareprocess, but also because of the usercommunity that formed around it.VTK is now used worldwide and hashelped spawn several small compa-nies and derivative products. Kitware(www.kitware.com), for example,was formed in 1998 to support VTK,subsequently creating productsbased on the toolkit, including theopen-source ParaView parallel visual-ization system and the proprietary VolView volume-rendering application.1 VTK continues to evolve with contributions fromresearchers in academia, US national laboratories, and businesses, and is used in dozens of commercial software applications.

Figure D. The VolView visualization software toolset. This example shows VolViewbeing used to visualize the spinal column, blood vessels, and tissue slides. (Figurecourtesy of Kitware.)

JULY/AUGUST 2006 71

posals are getting funding, and thereappears to be a bias in proposal review-ing toward low-risk incremental workrather than attacking grand challenges.

One important exception to thisdownturn is the new US National Vi-sualization and Analytics Center(NVAC) initiative,6 which will spendseveral hundred million dollars in thenext five years on visual analytics.Much of this funding will be focusedon the national security domain. Al-though considerable fundamental re-search will arise from this short-termprogram, NIH and NSF must ensurethat the health and science domains aresufficiently funded, and that long-termresearch continues to put the field on asolid scientific foundation.

RecommendationsWe explored these infrastructure andfunding findings, as well as other find-ings, with help from area experts in two

separate workshops held in Bethesda,Maryland, in September 2004 and SaltLake City, Utah, in May 2005. Basedon our panelists’ deliberations, wemake the following recommendations.

Principal AgencyLeadership RecommendationNSF and NIH must make coordinatedinvestments in visualization to addressthe 21st century’s most importantproblems, which are predominantlycollaborative and cross disciplines,agencies, and sectors. Both NSF andNIH can and should provide leader-ship to other federal funding partnersand to the research communities theysupport. To achieve this, they shouldmodify their programmatic practicesto better engage visualization capabil-ities across disciplines important toscientific and social progress, and en-courage and reward interdisciplinaryresearch, open practices, and repro-

ducibility in technical and scientificdevelopments. Such agency leadershipis critical if we’re to meet US needs incritical areas and maintain competi-tiveness worldwide.

Short-Term PolicyRecommendationNIH and NSF can immediately imple-ment funding policy changes to en-courage evaluation of visualization andcollaboration between visualization andother fields, without requiring newfunding initiatives. Likewise, journalsand conferences should update theirpublication review criteria to reflecthow important evaluation is in de-termining techniques’ success andcharacterizing their suitability. Meth-odological rigor should be expectedwhen user studies are proposed or re-viewed, and as necessary, visualizationresearchers should collaborate withthose trained in fields such as human–

Figure D demonstrates volume rendering using VolView, whereas Figure E shows a computational fluid dynamics visualization us-ing ParaView. Figure F uses the LOx Post data set, simulating the flow of liquid oxygen across a flat plate with a cylindrical post per-pendicular to the flow. This model studies the flow in a rocket engine, where the post promotes the mixing of the liquid oxygen.2

References

1. W.J. Schroeder, K. Martin, and B. Lorensen, The Visualization Toolkit: An Object Oriented Approach to Computer Graphics, 3rd ed., Kitware, 2004.

2. S.E. Rogers, D. Kwak, and U.K. Kaul, “A Numerical Study of Three-Dimensional Incompressible Flow around Multiple Posts,” Proc. AIAA Aerospace

Sciences Conf., AIAA paper 86-0353, 1986, pp. 1–12.

FIgure E. The ParaView toolset. Here, ParaView is beingused to visually analyze a computational fluid dynamicsdata set. (Figure courtesy of Kitware.)

Figure F. The Visualization Toolkit (VTK). This imageshows the flow of liquid oxygen across a flat plate with acylindrical post perpendicular to the flow. (Figurecourtesy of Kitware.)

72 COMPUTING IN SCIENCE & ENGINEERING

computer interaction, psychology, andstatistics. Peer review of proposals andpublications should also reward visual-ization driven by real-world data andtasks, in close collaboration with tar-get users. In other fields, reviewprotocols should encourage domainscientists who create partnerships withvisualization researchers, just as en-gagement with statisticians is consid-ered normal practice in areas such asbiomedical research.

Midterm DirectionRecommendationNIH and NSF should establish pilotprograms to combine efforts and cre-ate collaborative development between

visualization and other research do-mains. Funding for such programsshould contribute proportionately toboth the visualization research and thedomain specialty. This should help im-prove the penetration of emergingtechnologies into new domains, in-creasing their facility to move data andshare results through visualization.Awards in this area should be dedi-cated to open access of source code,availability of research data to theworldwide community, and repro-ducibility of the technical and scien-tific developments.

F or the long term, we recommend acoordinated and sustained national

investment in a spectrum of centralizedand distributed research programs topromote foundational, transitional, andapplied visualization research in sup-port of science, medicine, business, and

other socially important concerns. Thisinvestment is critical if the US wants toremain competitive in a global researchand development community that hasincreasing resources. In addition tofunding transitional research, suchprograms should emphasize founda-tional research and integration ofmethodologies from other fields, andcollaboration with domain specialistswho provide driving problems in areasof national concern. A long-termfunding commitment is required tocreate and maintain curated data col-lections and open-source software topromote open science. Characterizinghow and why visualizations work, sys-tematically exploring visual represen-

tations’ design space, developing newinteraction approaches, and exploitingthe possibilities of novel display hard-ware will be particularly importantareas of emphasis.

AcknowledgmentsWe’re indebted to the expert and vi-sionary VRC workshop panelists forsharing their considerable talents andinsights, and to NIH and NSF for theirsponsorship. In particular, we thankpanelists Maneesh Agrawala, Liz Bul-litt, Steve Cutchin, David Ebert,Thomas Ertl, Steve Feiner, FeliceFrankel, Bob Galloway, Alyssa Good-man, Mike Halle, Pat Hanrahan,Chuck Hansen, Helwig Hauser, KarlHeinz Höhne, Ken Joy, Arie Kaufman,Daniel Keim, David Laidlaw, MingLin, Leslie Loew, Bill Lorensen,Wayne Loschen, Patrick Lynch, KwanLiu Ma, Alan MacEachren, ChrisNorth, Art Olson, Catherine Plaisant,

Jerry Prince, Will Schroeder, JackSnoeyink, John Stasko, Barbara Tver-sky, Matthew O. Ward, Colin Ware,Ross Whitaker, Turner Whitted, andJarke van Wijk.

References1. B.H. McCormick, T.A. DeFanti, and M.D.

Brown, Visualization in Scientific Computing,Nat’l Science Foundation, 1987.

2. C. Johnson et al., NIH-NSF Visualization Re-search Challenges Report, IEEE Press, 2006.

3. P. Lyman and H.R. Varian, How Much Infor-mation, 2003; www.sims.berkeley.edu/how-much-info-2003.

4. W. Schroeder, K. Martin, and B. Lorensen,The Visualization Toolkit: An Object-OrientedApproach to Computer Graphics, 3rd ed., Kit-ware, 2003.

5. D.A. Patterson. “The State of Funding forNew Computer Science Initiatives in Com-puter Science and Engineering,” Comm.ACM, vol. 48, no. 4, 2005, p. 21.

6. J.J. Thomas and K.A. Cook, eds., Illuminatingthe Path: The Research and DevelopmentAgenda for Visual Analytics, Nat’l Visualizationand Analytics Center, 2005.

Robert Moorhead is the Billie J. Ball Profes-

sor of Electrical and Computer Engineering

at Mississippi State University (MSU) and the

director of the Visualization, Analysis, and

Imaging Lab. He is also the associate direc-

tor for research for the MSU GeoResources

Institute. His research interests are focused

around visualization of enormous data sets,

remote visualization, and the effectiveness

of visualization techniques. Moorhead has a

PhD in electrical and computer engineering

from North Carolina State University. Con-

tact him at rjm@hpc.msstate.edu.

Chris Johnson directs the Scientific Com-

puting and Imaging (SCI) Institute at the

University of Utah where he is a Distin-

guished Professor of Computer Science and

holds faculty appointments in the Depart-

ments of Physics and Bioengineering. His re-

search interests are in the areas of scientific

computing and scientific visualization. John-

son has a PhD in computer science from the

V I S U A L I Z A T I O N C O R N E R

We recommend investment in a spectrum

of centralized and distributed research programs.

JULY/AUGUST 2006 73

University of Utah. He founded the SCI Re-

search Group in 1992, which has since

grown to become the SCI Institute, employ-

ing more than 100 faculty, staff and stu-

dents. Contact him at crj@sci.utah.edu.

Tamara Munzner is an assistant professor

of computer science at the University of

British Columbia. Her current research in-

terests are information visualization, graph

drawing, and dimensionality reduction.

Munzner has a PhD in computer science

from Stanford University. She was the IEEE

Symposium on Information Visualization (In-

foVis) Program/Papers cochair in 2003 and

2004. Contact her at tmm@cs.ubc.ca.

Hanspeter Pfister is associate director and

senior research scientist at Mitsubishi Elec-

tric Research Laboratories in Cambridge,

MA. He is the chief architect of VolumePro,

Mitsubishi Electric’s real-time volume ren-

dering hardware for PCs. His research inter-

ests include computer graphics, scientific

visualization, and graphics architectures.

Pfister has a PhD in Computer Science from

the State University of New York, Stony

Brook. His work spans a range of topics, in-

cluding point-based graphics, appearance

modeling and acquisition, computational

photography, 3D television, and face mod-

eling. Contact him at pfister@merl.com.

Penny Rheingans is an associate professor

of computer science at the University of

Maryland Baltimore County. Her current re-

search interests include uncertainty in visu-

alization, volume visualization, information

visualization, perceptual and illustration is-

sues in graphics and visualization, dynamic

and interactive representations and inter-

faces, and the experimental validation of vi-

sualization techniques. Rheingans has a PhD

in computer science from the University of

North Carolina, Chapel Hill. Contact her at

rheingan@cs.umbc.edu.

Terry S. Yoo is head of the Program for 3D

Informatics in the Office of High Perfor-

mance Computing and Communications,

National Library of Medicine, NIH. His re-

search explores the processing and visual-

ization of 3D medical data, interactive 3D

graphics, and computational geometry. Yoo

has a PhD in computer science from the Uni-

versity of North Carolina, Chapel Hill. He is

the project officer who conceived and man-

aged the development of ITK, the Insight

Toolkit, under the Visible Human Project.

Contact him at yoo@nlm.nih.gov.

Mid Atlantic (product/recruitment)Dawn BeckerPhone: +1 732 772 0160Fax: +1 732 772 0164Email: db.ieeemedia@ieee.org

New England (product)Jody EstabrookPhone: +1 978 244 0192Fax: +1 978 244 0103Email: je.ieeemedia@ieee.org

New England (recruitment)John RestchackPhone: +1 212 419 7578Fax: +1 212 419 7589Email: j.restchack@ieee.org

Connecticut (product)Stan GreenfieldPhone: +1 203 938 2418Fax: +1 203 938 3211Email: greenco@optonline.net

Midwest (product)Dave JonesPhone: +1 708 442 5633Fax: +1 708 442 7620Email: dj.ieeemedia@ieee.orgWill HamiltonPhone: +1 269 381 2156Fax: +1 269 381 2556Email: wh.ieeemedia@ieee.orgJoe DiNardoPhone: +1 440 248 2456Fax: +1 440 248 2594Email: jd.ieeemedia@ieee.org

Southeast (recruitment)Thomas M. FlynnPhone: +1 770 645 2944Fax: +1 770 993 4423Email: flynntom@mindspring.com

Southeast (product)Bill HollandPhone: +1 770 435 6549Fax: +1 770 435 0243Email: hollandwfh@yahoo.com

Midwest/Southwest (recruitment)Darcy GiovingoPhone: +1 847 498-4520Fax: +1 847 498-5911Email: dg.ieeemedia@ieee.org

Southwest (product)Josh MayerPhone: +1 972 423 5507Fax: +1 972 423 6858Email: jm.ieeemedia@ieee.org

Northwest (product)Peter D. ScottPhone: +1 415 421-7950Fax: +1 415 398-4156Email: peterd@pscottassoc.com

Southern CA (product)Marshall RubinPhone: +1 818 888 2407Fax: +1 818 888 4907Email: mr.ieeemedia@ieee.org

Northwest/Southern CA (recruitment)Tim MattesonPhone: +1 310 836 4064Fax: +1 310 836 4067Email: tm.ieeemedia@ieee.org

JapanTim MattesonPhone: +1 310 836 4064Fax: +1 310 836 4067Email: tm.ieeemedia@ieee.org

Europe (product) Hilary TurnbullPhone: +44 1875 825700Fax: +44 1875 825701Email: impress@impressmedia.com

A D V E R T I S E R / P R O D U C T I N D E X M A Y / J U N E 2 0 0 6

Krell Institute 7

Prentice Hall 44–45

Boldface denotes advertisements in this issue.

Advertising PersonnelAdvertiser Page Number

Marion DelaneyIEEE Media, Advertising DirectorPhone: +1 212 419 7766Fax: +1 212 419 7589Email: md.ieeemedia@ieee.orgMarian AndersonAdvertising CoordinatorPhone: +1 714 821 8380Fax: +1 714 821 4010Email: manderson@computer.org

Sandy BrownIEEE Computer Society,Business Development ManagerPhone: +1 714 821 8380Fax: +1 714 821 4010Email: sb.ieeemedia@ieee.org

Advertising Sales Representatives