Contact Information

Mail Stop – B287, Los Alamos National Laboratory, Los Alamos, NM 87545

(505)-667-5797, ahrens@lanl.gov

Education

• Ph.D. Computer Science, University of Washington, Seattle, 1996.

• M.S. Computer Science, University of Washington, Seattle, 1992.

• B.S. Computer Science, University of Massachusetts, Amherst, 1989.

Professional Experience

• 1996-present, Los Alamos National Laboratory (LANL), Department of Energy (DOE)

• Summer 2020-present, Director of the Information Science and Technology Institute, Los Alamos National Laboratory

• 2018-present, Project Manager / Control Access Manager (CAM) for Data and Visualization for the U.S. Exascale Computing Project (ECP) (~$12 Million in managed projects per year)

• 2016-2018, Project Manager /CAM for Visualization and Data Analysis projects for ECP (~$5 million in managed projects per year)

• 2010 - present, Scientist 5 - Recognized Authority/International Leader

• 2013-2016, Project Manager for Visualization R&D and Production for LANL's Advanced Simulation and Computing (ASC) program ($4.5 million in managed projects per year)

• 2008 - 2010, Scientist 4 - Discipline Authority/National Leader

• 2009 -2013, Project Manager for Visualization R&D for LANL's ASC program ($1.3 million in managed projects per year)

• 2009-present, Principal Investigator and Investigator for peer-reviewed research proposals (~$30 Million total funding) to the DOE Office of Science, Biology and Energy Research (BER) and Advanced Scientific Computing Research (ASCR) Offices and LANL Laboratory Directed Research and Development (LDRD)

• 2005-2008 - Team Lead for the Visualization team

• 1996-2005 - Staff member

• Summer 1993-1994, Graduate Research Assistant

• Summer 1991, Thinking Machines Corporation - Graduate Research Assistant

• Summer 1987-1989, General DataComm Incorporated, Intern

Research Areas and Expertise

• Technical expertise in the areas of data science, visualization and parallel systems.

• Management experience as R&D project manager and team leader.

Contributions to Open-source Software

• Founder and design lead of ParaView, an open-source visualization tool designed to handle extremely large data.

• Developed parallel VTK, a parallel visualization software infrastructure. ParaView is built upon parallel VTK.

• Co-creator of PISTON a cross-platform software library providing hardware accelerated versions of frequently used operations for scientific visualization and analysis. VTK-m is a toolkit of scientific visualization algorithms for emerging processor architectures that incorporated ideas from three visualization acceleration tools, Dax, PISTON, EAVL.

• Founder and design lead of Cinema, an open-source image-based in situ visualization and analysis tool.

Research Funding

Successful proposed peer-reviewed research projects to numerous agencies including Department of Energy (DOE) Office of Science, Biology and Energy Research (BER) and Advanced Scientific Computing Research (ASCR) Offices, and LANL Laboratory Directed Research and Development (LDRD) over the past decade. Successful managed long-term multi-institutional, multi-person visualization research and development projects to produce both research publications and usable open-source software solutions to solve complex problems of interest to the Department of Energy.

24.) Dates of the project: 10/2020-10/2024, Role: Co-Investigator, Annual funding: $300K, Project title: RAPIDS2, Funding organization: DOE, Office of Science, Advanced Scientific Computing Research (ASCR), SciDAC

23.) Dates of the project: 10/2018-10/2021, Role: Investigator, Annual funding: $200K, Project title: Non Negative Tensor Factorization, Funding organization: DOE, Los Alamos National Laboratory, LDRD

22.) Dates of the project: 10/2017-10/2020, Role: Co-Investigator, Annual funding: $300K, Project title: RAPIDS - A SciDAC Institute for Computer Science and Data, Funding organization: DOE, Office of Science, Advanced Scientific Computing Research (ASCR), SciDAC

21.) Dates of the project: 10/2017-10/2020, Role: Principal Investigator, Annual funding: $650K/year (400K to LANL), Project title: Sample-based, Perceptually- and Cognitively-driven Visual Analysis of Massive Scientific Data Using an Asynchronous Tasking Engine, Funding organization: DOE, Office of Science, Advanced Scientific Computing Research (ASCR)

20.) Dates of the project: 1/2020-6/2023, Role: Principal Investigator, Annual funding: $3200K (1000K to LANL) Project title: ECP ALPINE: Algorithms and Infrastructure for In Situ Visualization and Analysis, Funding organization: DOE, Exascale Computing Project, Software Technology

19.) Dates of the project: 1/2017-1/2020, Principal Investigator, Annual funding: Phase 1 - $2000K (650K to LANL), Project title: ECP ALPINE: Algorithms and Infrastructure for In Situ Visualization and Analysis, Funding organization: DOE, Exascale Computing Project, Software Technology

18.) Dates of the project: 1/2017-1/2018, Role: Principal Investigator, Annual funding: $1200K, Project title: Cinema: Image-based Visualization and Analysis, Funding organization: DOE, Exascale Computing Project, Software Technology

17.) Dates of the project: 1/2017-1/2018, Role: Principal Investigator, Annual funding: $1100K, Project title: BEE: Virtual Environments Project, Funding organization: DOE, Exascale Computing Project, Software Technology

16.) Dates of the project: Phase 1 - 10/2016-10/2019, Phase 2 - 10/2019-6/2023 Role: Co-Investigator, Analysis Co-Lead, Annual funding: Phase 1 & 2 - $2500K (500K to LANL), Project title: Exasky: Computing the Sky at Extreme Scales, Funding organization: DOE, Exascale Computing Project, Applications

15.) Dates of the project: 10/2016-10/2019, Role: Principal Investigator, Annual funding: $1650K, Project title: Real-time Adaptive Acceleration of Dynamic Experimental Science, Funding organization: DOE, Los Alamos National Laboratory, LDRD

14.) Dates of the project: 10/2015-10/2016, Role: Co-Investigator, Annual funding: $50K/year, Project title: Big Data and Analytics for Induced Seismicity, Funding organization: DOE, EERE-Geothermal Technologies Office and Office of Fossil Energy

13.) Dates of the project: 10/2015-10/2016, Role: Co-Investigator, Annual funding: $150K/year, Project title: Computing the Sky: Simulation and Analysis for Cosmological Surveys , Funding organization: DOE, Office of Science, SciDAC

12.) Dates of the project: 10/2014-10/2017, Role: Principal Investigator, Annual funding: $500K/year (management total of 1000K: U. Texas, Virginia Tech and UNH), Project title: Optimizing the Energy Usage and Cognitive Value of Extreme Scale Data Analysis, Funding organization: DOE, Office of Science, Advanced Scientific Computing Research (ASCR)

11.) Dates of the project: 10/2011-10/2014, Role: Principal Investigator, Annual funding: $725K/year, Project title: Exploration and Evaluation of Exascale In Situ Visualization and Analysis Approaches, Funding organization: DOE, Office of Science, Advanced Scientific Computing Research (ASCR)

10.) Dates of the project: 10/2012-10/2017, Role: Principal Investigator, Annual funding: $400K/year (co-management role of Visualization portion of grant), Project title: SciDAC Scalable Data Management and Analysis and Visualization Institute, Funding organization: DOE, Office of Science, Advanced Scientific Computing Research (ASCR) , SciDAC

9.) Dates of the project: 10/2012-10/2015, Role: Co-Investigator, Annual funding: $115K/year, Project title: SciDAC Computation-Driven Discovery for the Dark Universe, Funding organization: DOE, Office of Science, Advanced Scientific Computing Research (ASCR), SciDAC

8.) Dates of the project: 10/2012-10/2015, Role: Co-Investigator, Annual funding: $80K/year, Project title: SciDAC Plasma Surface Interactions: Bridging from the Surface to the Micron Frontier through Leadership Class Computing, Funding organization: DOE, Office of Science, Advanced Scientific Computing Research (ASCR), SciDAC

7.) Dates of the project: 10/2011-10/2016, Role: Co-Investigator, Annual funding: $100K/year, Project title: Center for Exascale Simulation of Advanced Reactors (CESAR), Funding organization: DOE, Office of Science, Advanced Scientific Computing Research (ASCR), SciDAC

6.) Dates of the project: 10/2011-10/2016, Role: Co-Investigator, Annual funding: $200K/year, Project title: Exascale Co-Design Center for Materials in Extreme Environments, Funding organization: DOE, Office of Science, Advanced Scientific Computing Research (ASCR), Co-Design

5.) Dates of the project: 10/2011-10/2014, Role: Co-Investigator, Annual funding: $200K/year, Project title: Cocomans – Co-design of Next Generation Simulations, Funding organization: DOE, Los Alamos National Laboratory, LDRD

4.) Dates of the project: 6/2010-6/2013, Role: Co-Investigator, Annual funding: $300K/year, Project title: Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT), Funding organization: DOE, Office of Science, Biology and Energy Research (BER)

3.) Dates of the project: 10/2008-10/2011, Role: Principal Investigator, Annual funding: $750K/year, Project title: Remote Visualization for Petascale and Exascale Simulations, Funding organization: DOE, Office of Science, Advanced Scientific Computing Research (ASCR)

2.) Dates of the project: 10/2009-10/2012, Role: Co-Investigator, Annual funding: $350K/year, Project title: The Dark Universe, Funding organization: DOE, Los Alamos National Laboratory, LDRD

1.) Dates of the project: 10/2009-10/2012, Role: Co-Investigator, Annual funding: $100K/year, Project title: Intelligent Wind Turbines, Funding organization: DOE, Los Alamos National Laboratory, LDRD

Professional Service

• Chair, IEEE Visualization and Graphics Technical Committee (VGTC), December 2018-present.

• • The VGTC is the Visualization and Graphics Technical Committee, the governance body that oversees and sponsors all IEEE visualization and virtual reality conferences including VIS, VR, ISMAR, 3DUI, Pacific Vis, and EuroVis (as a co-sponsor). As VGTC Chair, I direct the VGTC and oversees the VGTC budget while also advocating to the upper levels of IEEE on policy decisions. The VGTC Chair is the only elected position within VGTC that is determined directly by the membership.

• Associate Editor, IEEE Transactions on Visualization and Graphics, 2017-present.

• Member of Visualization Executive Committee, 2018-2019.

• Member of SciVIS Executive Committee, 2016-2019.

• General Chair, IEEE Visualization, 2017.

• SciVis Papers Co-Chair, IEEE Visualization, 2015-2016.

• Co-Founder and Executive Committee Member, IEEE Large Data Analysis and Visualization Symposium, 2012-2018.

• Guest Editor, IEEE Computer Graphics and Applications - Special Issue on Ultrascale Visualization, 2010.

• Member of the International Program Committee for IEEE Visualization 2006-8.

• Refereed manuscripts for IEEE Transaction on Visualization and Graphics, 2006 - present.

Professional Activities

27.) Member of the ASCR program committee for the "Community of Interest on the Future of Scientific Methodologies" workshop. This workshop is designed to create a vision for how future computational fabrics will shape, and be shaped by, scientific advances that will occur over the next 30 years.

26.) Invited participant in an ASCR planning meeting on in situ data management to help shape future ASCR research directions in this area. More information can be found at the In Situ Data Management Web Site, January 2019.

25.) Member of organizing committee for workshop entitled, “Gap Analysis: Materials Discovery through Data Science at Advanced User Light Sources", Santa Fe, NM, October 2018.

24.) Member of review committee for Sandia National Laboratory ASC Level 2 milestone, August 2018.

23.) Invited participant in "Restructuring IEEE VIS for the future" workshop, BANFF, Canada, June 2018.

22.) Data science working group lead for Los Alamos' Regional Academic Collaboration (ReACt) Information Science, Santa Fe, March 2018.

21.) Co-Workshop Chair with K. Keahey, DOE-sponsored Future of Online Analysis Platform workshop, Future Platform Workshop Report, April, 2017.

20.) Project lead for U.S. Exascale Computing Project (ECP)’s Data and Visualization area which includes thirteen storage, data management and visualization projects. More information can be found in the ECP Data and Visualization Project Area description and in the ECP Software Technology Capability Assesment Report. 2016-present.

19.) As Los Alamos National Laboratory (LANL) Information, Science and Technology (IS&T) Data Science at Scale lead, met with New Mexico (NM) Senate Staffers and NM University representatives at “HPC, Big Data, CyberSecurity” meeting, 2016.

18.) Led visualization researchers to gather requirements from experimentalists as part of a DOE Experimental and Observational Data Workshop. Workshop activities resulted in a report entitled, “Management, Analysis and Visualization of Experimental and Observational Data”, 2015.

17.) Invited to “Frontiers of Visualization” a NITRD (Networking and Information Technology Research and Development) workshop to help set future funding directions in visualization, 2014.

16.) Lead for Data Visualization and Analytics Exascale Planning for DOE ASC/ASCR. This activity includes help to define an exascale strategy and writing a national white paper that set the scope, schedule and budget of an exascale visualization and data analysis activity for a successful exascale program. Communicated/defended this plan to red team reviewers. Provided leadership for this activity over the past five years, 2011-2015.

15.) Helped Dimitri Kusnezov, Senior Advisor to the Secretary of Energy, prepare DOE’s Big Data and Privacy briefing for John Podesta’s White House 90-day study. This briefing included describing the relationship between big data and exascale. The White House produced a report entitled, "Big Data: Seizing Opportunities, Preserving Values" that summarized their findings, Spring 2014.

14.) Area lead (Data Science at Scale) for the Los Alamos National Laboratory - Information and Knowledge Sciences Capability Review, 2008, 2011, 2014.

13.) Delivered in situ analysis to the ASC program as a Level 2 milestone. Technical Report – J. Patchett, J. Ahrens, B. Nouanesengsy, P. Fasel, P. Oleary, "LANL ASC Level 2 Milestone: Case Study of In Situ Data Analysis in ASC Integrated Codes", 2013.

12.) Co-led a data science summer school to mentor and guide a set of 15+ summer students to develop state of the art data science solutions to laboratory problems of interest. Collaborated with professors from NYU-Poly, JHU and other universities to present to laboratory community about data science topics. Summers 2013-2016.

11.) Review committee member - Review of the Computing Environment and Life Sciences Directorate (CELS) at Argonne National Laboratory, 2012.

10.) Executive Committee Member, SciDAC Scalable Data Management and Analysis and Visualization Institute, 2012-2017.

9.) Visualization collaboration lead, for the DOE NNSA / Commissariat l'Énergie Atomique (CEA) High Performance Computing collaboration. This U.S./French collaboration discusses high-performance topics as part of a treaty agreement between the two nations, 2009-2012.

8.) Responding to a request by Tom Kalil, White House - Office of Science and Technology Policy Deputy Director to create a white paper and presentation on DOE-focused data-intensive science to present to Undersecretary Koonin in August 2010. J. Ahrens, B. Hendrickson, G. Long, S. Miller, R. Ross, D. Williams, "Data Intensive Science in the Department of Energy". A white paper for the Office of Science and Technology Policy, Technical Report LA-UR-10-07088.

7.) Delivered alternative rendering approaches including ray-tracing to the ASC program as a Level 2 milestone. James Ahrens, John Patchett, Li-Ta Lo, David DeMarle, Carson Brownlee, and Christopher Mitchell, "A Report Documenting the Completion of the Los Alamos National Laboratory Portion of the ASC Level II Milestone ”Visualization on the Supercomputing Platform”, 2010.

6.) Organizing a DOE Office of Science Advanced Scientific Computing Research (ASCR) PI meeting for Scientific Data Management, Analysis and Visualization for Lucy Nowell, DOE Office of Science Program Manager in Santa Fe, New Mexico, August 16-19, 2010.

5.) Co-Program Chair, Eurographics Symposium on Parallel Graphics and Visualization, May 2-3, 2010.

4.) DOE SciDAC Ultrascale Institute Advisory Board Member, 2006-2010.

3.) Helped organize the DOE Office of Science SciDAC 2008 conference. This conference is an important Office of Science yearly meeting. Helped arrange a number of events at the conference including a movie night; poster and paper sessions focused on Office of Science visualization activities.

2.) Planned for the future visualization activities by co-chairing a data analysis and visualization breakout at an Office of Science Workshop on “Simulation and Modeling for Advanced Nuclear Energy Systems” in August 2006 and by leading a section of Office of Science ASCR Visualization and Data Discovery Workshop producing a report entitled, “Recommendations for a Visual Analysis and Data Exploration Research Program for the Future Exascale Era” in June 2007.

1.) Reviewed grant proposals for many Offices of the US Department of Energy (DOE), 2005-present.

Presentations

Keynote and Plenary Presentations

7.) Keynote, "Approaches to Massive Scientific Data Visualization and Analysis", 14th International Symposium on Visual Computing (ISVC '19), October 2019.

6.) Keynote, “Supercharging the Scientific Process Via Data Science at Scale”, New York Scientific Data Summit, August 2017.

5.) Keynote, “Supercharging the Scientific Process Via Data Science at Scale”, HPC Day at Virginia Tech, March 2017.

4.) Keynote, “Towards a scalable, platform independent, user-friendly analysis framework for scientific and information oriented applications”, Chesapeake Large Scale Analytics Conference, October 2016.

3.) Plenary presentation, “Supercharging the Scientific Process Via Data Science at Scale”, University of Groningen Centre for Data Science & Systems Complexity Opening Symposium, June 11, 2015.

2.) Plenary presentation, “Implications of Numerical and Data Intensive Technology Trends on Scientific Visualization and Analysis”, SIAM Conference on Computational Science and Engineering, March 14-18, 2015.

1.) Invited Plenary/Luncheon Speaker, "Data-intensive Applications on Numerically-Intensive Supercomputers", Los Alamos Computer Science Symposium, October 2009.

Invited Presentations, Panels and Tutorials

42.) Panel member, "How Ubiquitous Parallel Devices Affect Visualization", EuroGraphics Symposium on Parallel Graphics and Visualization, May 2020, virtual.

41.) Special presentation, "ECP Data and Visualization - Delivering Exascale by 2023 and Beyond", DOE Computer Graphics Forum, April 2020, virtual.

40.) Invited presentation, "Exascale Computing Project (ECP) Data Analytics and Visualization", ECP Deep Dive Workshop for the ECP Industry Council, March 2020, virtual.

39.) Invited presentation, “Real-time Adaptive Acceleration of Dynamic Experimental Science”, Los Alamos Inertial confinement fusion (ICF) and High Energy Density (HED) Seminar, Los Alamos, January 2020.

38.) Invited presentation, “Data science for Space Weather Science”, Coupling, Energetics, and Dynam­ics of the Atmospheric Regions Workshop (CEDAR) - Geospace Data Science Session, Santa Fe, June 2019.

37.) Invited presentation, “Data Science for Material Science - A Database, Data-driven Modeling and Visualization Approach” , Institute for Materials Science - Computational Data Science Approaches for Materials, Los Alamos, April 2019.

36.) Invited presentation, "The development and use of in situ visualization and analysis approaches for the U.S. Exascale Computing Project", SIAM CSE, January 2019.

35.) Invited presentation and white paper with C. Biwer, "A vision for a validated distributed knowledge base of material behavior at extreme conditions using the Advanced Cyberinfrastructure Platform", Big Data and Extreme Scale Computing. Bloomington, Indiana, November 2018.

34.) Invited presentation, “Adaptive Decision Making and Improved Data Understanding for Experimental Science Using Statistical Machine Learning and High Performance Computing”, HPC Italy conference, July 2018.

33.) Invited presentation, "Real-time Adaptive Acceleration of Dynamic Experimental Science”, ANL Mathematics and Computer Science Division, April 2018.

32.) Invited presentation, “Towards a Theory for Massive, Multidimensional Data Analysis and Visualization“, Foundations of Data Visualization Dagsthul conference, January 2018.

31.) Invited presentation and demonstration with R. Thakur, “ECP Software Development Update and Demonstration of the ECP ExaSky Cosmology Application and its Use of ECP Software Technology Capabilities", ECP Industry Council Meeting, October 2017.

30.) Invited presentation, “Visualization, Data Analysis and Data Management for Particle Applications", ECP Co-design Center for Particle Applications (COPA) Project Meeting , September2017.

29.) Invited presentation, “An Institutional Data Program for Los Alamos National Laboratory", AWS Fusion meeting At Sandia National Laboratory, September 2017.

28.) Invited presentation, “Envisioning Human-in-the-loop Interactions with Massive Scientific Simulations and Experiments in the Age of Exascale HPC and Big Data”, HPC Italy, June 2016.

27.) Invited presentation, “Real-time Adaptive Acceleration of Dynamic Experimental Science”, Materials Science and Data Technology Nexus, September 2016.

26.) Invited presentation, “Exascale Data Science”, NYU Center for Data Science, New York, New York, September 2016.

25.) Invited presentation, “Supercharging the Scientific Process Via Data Science at Scale”, International Research Training Group (IRTG) - Physical Modeling for Virtual Manufacturing Systems and Processes, University of Kaiserslautern, June 15, 2015.

24.) Plenary presentation, “Supercharging the Scientific Process Via Data Science at Scale”, University of Groningen Centre for Data Science & Systems Complexity Opening Symposium, June 11, 2015.

23.) Invited presentation, “Accelerating Time to Insight in the Exascale Ecosystem Through the Optimization of Scientific Workflows”, Big Data and Extreme-Scale Computing Conference Barcelona, Spain, January 29-30, 2015.

22.) Invited presentation, "Implications of Data and Numerically Intensive Computing on Scientific Visualization", Texas Advanced Computing Center Seminar, August 2014.

21.) Invited presentation, "Implications of Data and Numerically Intensive Computing on Scientific Visualization", Dagsthul, June 2014. From the Dagsthul website, "Dagstuhl is the world's premier venue for informatics. Dagstuhl enables the international elite, promising young researchers and practitioners alike to gather together to discuss their views and research findings."

20.) Invited presentation, "Increasing Scientific Data Insights About Exascale Class Simulations Under Power and Storage Constraints” Big Data and Extreme-Scale Computing Conference in Fukuoka, Japan, Feb. 26-28,2014. Also described in a podcast interview with HPCWire. http://www.hpcwire.com/soundbite/re-routing-exascale-simulation-storage-power-concerns/, 2014.

19.) Invited presentation to JASON Committee on the Relationship between Data Intensive and Exascale Computing, 2012.

18.) Member of panel, "The Impact of Future Hardware on Visualization", IEEE Visualization 2009.

17.) Panel Organizer, "Challenges in Large Data Visualization: A Visualization Community Call to Action", IEEE Visualization 2009.

16.) Invited presentation, "Visualization of Petascale Data: Data-Intensive Computing on Numerically- Intensive Supercomputers", Los Alamos CCS High-Performance Computing Review, June 2009.

15.) Invited presentation, "Visualization of Petascale Data: Data-Intensive Computing on Numerically-Intensive Supercomputers", Dagsthul, June 2009.

14.) Invited presentation, "Petascale Visualization: Approaches and Initial Results", SOS13 - Sandia, ORNL and the Swiss (SOS) supercomputing meeting, March 2009.

13.) Invited presentation, "Challenges and Opportunities: Data Analysis and Visualization for Materials Science", Decadal Challenges for Predicting and Controlling Materials Performance in Extremes – A Los Alamos MARIE planning workshop, February 2009.

12.) Invited presentation, Roadrunner Technical Seminars, Panel on Future Platforms,"Data Intensive Architectures", June 2008.

11.) Tutorial, CEA/EDF/INRIA summer school, "Methodes Avancees en Visualisation Scientifique", ParaView, 18-21 June 2007, Saint-Lambert-des-Bois, France.

10.) Invited presentation, LANL Earth and Environmental Sciences Division, Frontiers in Geoscience Colloquium, "Scientific Visualization from Desktops to Petaflops", November 2006.

9.) Invited presentation, SC06 Workshop on Ultra-Scale Visualization, "Quantitative and Comparative Visualization Applied to Cosmological Simulations", November 2006.

8.) Invited presentation, Office of Science - Scientific Discovery Through Advanced Computing Conference, "Quantitative and Comparative Visualization Applied to Cosmological Simulations", June 2006.

7.) Member of panel on "Interoperability of Visualization Software and Data Models is Not an Achievable Goal", at IEEE Visualization 2003.

6.) K. Martin, G. Abram, J. Ahrens, R. Frank, P. Moran, "Tutorial on Large Scale Data Visualization and Rendering", IEEE Visualization, October 2001.

5.) Member of panel on "Next-Generation Visualization Displays" at IEEE Visualization 2000.

4.) Invited presentation, "Using Linux Clusters for Parallel Visualization and Rendering", Extreme Linux Workshop, USENIX, June 1999.

3.) Invited presentation, "An Evolving Infrastructure to Support Accelerated Strategic Computing Initiative (ASCI) Multi-Source Visualization and Data Analysis Needs", Ninth SIAM Conference on Parallel Processing for Scientific Computing, March 1999.

2.) Invited article, A. McPherson, J. Painter, P. McCormick, J. Ahrens, C. Ragsdale, "Visualizations of Earth Processes for the American Museum of Natural History", Vol. 33, No. 1, Computer Graphics, February 1999.

1.) Member of panel on "Multi-Source Data Analysis Challenges" at IEEE Visualization 1998.

Publications

Book Chapters and Journal Articles

39.) S. Dutta, T. Turton, J. Ahrens, "A Confidence-Guided Technique for Tracking Time-Varying Features", Computing in Science & Engineering, 2020.

40.) A. Biswas , S. Dutta, E. Lawrence, J. Patchett, J. Calhoun, J. Ahrens, "Probabilistic Data-Driven Sampling via Multi-Criteria Importance Analysis", IEEE Transactions on Visualization and Computer Graphics, 2020.

39.) A. Biswas, J. Ahrens, S. Dutta, J. Musser, A. Almgren, T. Turton,"Feature Analysis, Tracking, and Data Reduction: An Application to Multiphase Reactor Simulation MFiX-Exa for In-Situ Use Case", Computing in Science & Engineering, 2020.

38.) D. Francom, D.J. Walters, J.L. Barber, D.J. Luscher, E. Lawrence, A. Biswas, C.M. Biwer, D. Banesh, J. Lazarz, S.C. Vogel, K. Ramos, C. Bolme, R. Sandberg, J. Ahrens, “Simulation and Emulation of X-Ray Diffraction from Dynamic Compression Experiments”, Journal of Dynamic Behavior of Materials, Springer International Publishing, 1-18, 2020.

37.) H. Childs, S.D. Ahern, J. Ahrens, A.C. Bauer, J. Bennett, E.W. Bethel, P. Bremer, E. Brugger, J. Cottam, M. Dorier, and others, “A terminology for in situ visualization and analysis systems”, The International Journal of High Performance Computing Applications, 2020.

36.) D. Orban, D. Banesh, C. Tauxe, C. M. Biwer, A. Biswas, R. Saavedra, C. Sweeney, R. L. Sandberg, C. A. Bolme, J. Ahrens and D. Rogers, "Cinema:Bandit: a visualization application for beamline science demonstrated on XFEL shock physics experiments", Journal of Synchrotron Radiation, 27 (1), 2020.

35.) H. Carr, G. H. Weber, C. Sewell, O. Rübel, P. Fasel and J. Ahrens, "Scalable Contour Tree Computation by Data Parallel Peak Pruning," IEEE Transactions on Visualization and Computer Graphics, 2019.

34.) D. Banesh, M. Petersen, J. Wendelberger, J. Ahrens, B. Hamann, "Comparison of piecewise linear change point detection with traditional analytical methods for ocean and climate data", Environmental Earth Sciences, 78 (21) 623, 2019.

33.) S. Dutta, A. Biswas, J. Ahrens, "Multivariate Pointwise Information-Driven Data Sampling and Visualization", Entropy, 21 (7), 699, 2019

32.) G. Aldrich, J. Lukasczyk, J. Hyman, G. Srinivasan, H. Viswanathan, C. Garth, H. Leitte, J. Ahrens, B. Hamann, "A Query-based Framework for Searching, Sorting, and Exploring Data Ensembles", Computing in Science & Engineering, 2019.

31.) D. Orban, D. Keefe, A. Biswas, J. Ahrens, D. Rogers, “Drag and Track: A Direct Manipulation Interface for Contextualizing Data Instances within a Continuous Parameter Space”, IEEE Transactions on Visualization and Computer Graphics, 25 (1), 256-266, 2019.

30.) D. Walters, A Biswas, E Lawrence, D. Francom, D. Luscher, D. Fredenburg, K. Moran, C. Sweeney, R. Sandberg, J. Ahrens, C. Bolme, “Bayesian calibration of strength parameters using hydrocode simulations of symmetric impact shock experiments of Al-5083”, Journal of Applied Physics 124 (20), 2018.

29.) A.Biswas, C. Biwer, D. Walters, J. Ahrens, D. Francom, E. Lawrence, R. Sandberg, D. Fredenburg, C. Bolme, “An Interactive Exploration Tool for High-Dimensional Datasets: A Shock Physics Case Study”, Computing in Science & Engineering, 2018.

28.) J. Pulido, D. Livescu, K. Kanov, R. Burns, C. Canada, J. Ahrens, B. Hamann, “Remote visual analysis of large turbulence databases at multiple scales”, Journal of Parallel and Distributed Computing 120, 115-126, 2018.

27.) S. Vogel, C. Biwer, D. Rogers, J. Ahrens, R. Hackenberg, D. Onken, J. Zhang, “Interactive visualization of multi-data-set Rietveld analyses using Cinema: Debye-Scherrer”, Journal of applied crystallography 51 (3), 2018.

26.) J Patchett, J Ahrens, “Optimizing scientist time through in situ visualization and analysis”, IEEE computer graphics and applications 38 (1), 119-127, 2018.

25.) R. Bujack, T. Turton, F. Samsel, C. Ware, D. Rogers, J. Ahrens, “The good, the bad, and the ugly: A theoretical framework for the assessment of continuous colormaps”, IEEE transactions on visualization and computer graphics 24 (1), 923-933, 2018.

24.) P. O’Leary, J. Ahrens, S. Jourdain, S. Wittenburg, D. Rogers, M. Petersen, "Cinema image-based in situ analysis and visualization of MPAS-ocean simulations", Parallel Computing 55, pgs. 43-48, 2016.

23.) A. Bauer, H. Abbasi, J. Ahrens, et al, "In situ methods, infrastructures, and applications on high performance computing platforms", Computer Graphics Forum 35 (3), pgs. 577-597, 2016.

22.) K. Myers, E. Lawrence, M. Fugate, C. Bowen, L. Ticknor, J. Woodring, J. Wendelberger, J. Ahrens, "Partitioning a Large Simulation as It Runs", Technometrics, 58, 3, pgs. 329–340, 2016.

21.) J. Pulido, D. Livescu, J. Woodring, J. Ahrens, B. Hamann, "Survey and analysis of multiresolution methods for turbulence data", Computers & Fluids 125, pgs. 39-58, 2016.

20.) J. Ahrens, “Increasing Scientific Data Insights about Exascale Class Simulations under Power and Storage Constraints”, IEEE Computer Graphics and Applications, March/April 2015.

19.) J. Woodring, M. Petersen, A. Schmeier, J. Patchett, J. Ahrens, H. Hagen, “In Situ Eddy Analysis in a High- Resolution Ocean Climate Model”, IEEE Transactions on Visualization and Computer Graphics & IEEE Visualization Conference, Chicago, Illinois, October 2015.

18.) Y. Su, G. Agrawal, J. Woodring, K. Myers, J. Wendelberger, J. Ahrens, "Effective and efficient data sampling using bitmap indices", Cluster Computing (2014).

17.) S. Williams, M. Petersen, M. Hecht, M. Maltrud, J. Patchett, J. Ahrens, B. Hamann, "Interface Exchange as an Indicator for Eddy Heat Transport", Computer Graphics Forum 31(3): 1125-1134 (2012).

16.) S. Williams, M. Hecht, M. Petersen, R. Strelitz, M. Maltrud, J. Ahrens, M. Hlawitschka, B. Hamann, "Visualization and Analysis of Eddies in a Global Ocean Simulation", Computer Graphics Forum 30(3): 991-1000 (2011).

15.) J. Woodring, J. Ahrens, J. Figg, J. Wendelberger, S. Habib, K. Heitmann, "In-situ Sampling of a Large- Scale Particle Simulation for Interactive Visualization and Analysis", Computer Graphics Forum 30(3): 1151-1160 (2011).

14.) S. Williams, M. Petersen, P.T. Bremer, M. Hecht, V. Pascucci, J. Ahrens, M. Hlawitschka, B. Hamann, "Adaptive Extraction and Quantification of Geophysical Vortices", IEEE Transactions on Visualization and Computer Graphics 17(12): 2088-2095 (2011)

13.) J. Ahrens, B. Hendrickson, G. Long, S. Miller, R. Ross, D. Williams: Data-Intensive Science in the US DOE: Case Studies and Future Challenges. Computing in Science & Engineering, 13, 14-24 (2011)

12.) J. Ahrens, K. Heitmann, M. Petersen, J. Woodring, S. Williams, P. Fasel, C. Ahrens, C.H. Hsu, B. Geveci, "Verifying Scientific Simulations via Comparative and Quantitative Visualization". IEEE Computer Graphics and Applications 30(6): 16- 28 (2010).

11.) E. Santos, L. Lins. J. Ahrens, J. Freire, C. Silva, "VISMASHUP: Streamlining the Creation of Custom Visualization Applications", IEEE Transactions on Visualization and Computer Graphics, Volume 15, Issue 6, pp. 1539-46, November-December 2009.

10.) S. Habib, A. Pope, Z. Lukic, D. Daniel, P. Fasel, N. Desai, K. Heitmann, C. Hsu, L. Ankeny, G. Mark, S. Bhattacharya, J. Ahrens, "Hybrid petacomputing meets cosmology: the Roadrunner Universe project”, Journal of Physics: Conference Series - Scientific Discovery Through Advanced Computing, Volume 180, 2009.

9.) M. Graf, J. Ahrens, J. Patchett, H. Dahal, A. Balatsky, D. Modl, L. Monroe, N. Brown, E. Akhadov, “Integrated Nanotechnologies: To See is to Know: Visualization”, SciDAC Review, Issue 10, Winter 2008.

8.) E. Anderson, J. Ahrens, K. Heitmann, S. Habib, C. SIlva, "Provenance in Comparative Analysis: A Study in Cosmology", Computing in Science and Engineering, Volume 10, Issue 3, pgs. 30-37, May-June 2008.

7.) K. Heitmann, Z. Lukic, P. Fasel, S. Habib, M. Warren, M. White, J. Ahrens, L. Ankeny, R. Armstrong, B. O'Shea, P.M. Ricker, V. Springel, J. Stadel, and H. Trac, "The Cosmic Code Comparison Project", Computational Science and Discovery, Volume 1, Issue 1, 2008.

6.) P. McCormick, J. Inman, J. Ahrens, J. Mohd-Yusof, G. Roth, S. Cummings, "Scout: A Data Parallel Programming Environment for Graphics Processors", Parallel Computing, 33(10-11), pgs. 648- 662, 2007.

5.) J. Ahrens, K. Heitmann, S. Habib, L. Ankeny, P. McCormick, J. Inman, R. Armstrong and K.L. Ma, "Quantitative and comparative visualization applied to cosmological simulations", Journal of Physics: Conference Series - Scientific Discovery Through Advanced Computing, Volume 46, 2006.

4. ) J. Ahrens, B. Geveci, C. Law, "ParaView: An End User Tool for Large Data Visualization", Visualization Handbook, Academic Press, 2005.

3.) P. McCormick, J. Ahrens, "Large Data Visualization and Rendering: A Problem Driven Approach", Visualization Handbook, Academic Press, 2005.

2.) J. Ahrens, K. Brislawn, K. Martin, B. Geveci, C. Law, M. Papka, "Large Scale Data Visualization Using Parallel Data Streaming", IEEE Computer Graphics and Applications, July-August 2001.

1.) J. Kniss, P. McCormick, A. McPherson, J. Ahrens, J. Painter, A. Keahey, C. Hansen, "TRex: Interactive Texture Based Volume Rendering for Extremely Large Datasets", IEEE Computer Graphics and Applications, July-August 2001.

Conference and Workshop Publications

76.) P. Grosset, J. Pulido, J. Ahrens, "Personalized In Situ Steering for Analysis and Visualization", ISAV'20 In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, 2020.

75.) S. Sjue, J. Ahrens, Ayan Biswas, Devin Francom, E. Lawrence, D. Luscher, D.Walters, "Fast Strength Model Charecterization using Bayesian Statistics", AIP Conference Proceedings, Volume 2272, Issue 1, 2020.

74.) P. Grosset, C. Biwer, J. Pulido, A. Mohan, A. Biswas, J. Patchett, T. Turton, D. Rogers, D. Livescu, J. Ahrens, "Foresight: Analysis That Matters for Data Reduction", Supercomputing 2020.

73.) P. Hristov, G. Weber, H. Carr, O. Rübel, J. Ahrens, "Data Parallel Hypersweeps for In Situ Topological Analysis", 2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV), 12-21, 2020.

72.) J. Lukasczyk, C. Garth, M. Larsen, W. Engelke, I. Hotz, D. Rogers, J. Ahrens, R. Maciejewski, "Cinema Darkroom: A Deferred Rendering Framework for Large-Scale Datasets", 2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV), 37-41, 2020.

71.) [Best Paper Award] K. C. Tsai, R. Bujack, B. Geveci, U. Ayachit, J. Ahrens, “Approaches for In Situ Computation of Moments in a Data-Parallel Environment”, EuroGraphics Symposium on Parallel Graphics and Visualization, 2020.

70.) G. Abram, V. Adhinarayanan, W. Feng, D. Rogers, J. Ahrens, “ETH: An Architecture for Exploring the Design Space of In-situ Scientific Visualization", 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 515-526, 2020.

69.) S. Jin, P. Grosset, C. Biwer, J. Pulido, J. Tian, D. Tao, J. Ahrens, “Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations,” 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2020.

68.) C. Bolme, D. Orban, D. Banesh, C. Tauxe, C. Biwer, A. Biswas, R. Saavedra, C. Sweeney, R. Sandberg, J. Ahrens, and D. Rogers, “Workflow and visual analysis for XFEL shock physics experiments using Cinema: Bandit”, 21st Biennial Conference of the APS Topical Group on Shock Compression of Condensed Matter, Bulletin of the American Physical Society, Volume 64, Number 8, 2019.

67.) A. Biswas, D. Walters, D. Francom, E. Lawrence, D. Luscher, S. Sjue, J. Ahrens, “Fast Strength Model Parameter Optimization and Model Comparison Using Bayesian Statistics”, 21st Biennial Conference of the APS Topical Group on Shock Compression of Condensed Matter, Bulletin of the American Physical Society, Volume 64, Number 8, 2019.

66.) J. Pulido, Z. Lukic, P. Thorman, C. Zheng, J. Ahrens, B. Hamann, "Data Reduction Using Lossy Compression for Cosmology and Astrophysics Workflows", Journal of Physics: Conference Series, 1290, 1, 012008, 2019.

65.) J. Chen, Q. Guan, X. Liang, P. Bryant, P. Grubel, A. McPherson, L. Lo, T. Randles, Z. Chen, J. Ahrens, “Build and Execution Environment (BEE): an Encapsulated Environment Enabling HPC Applications Running Everywhere”, 2018 IEEE International Conference on Big Data (Big Data), 1737-1746, 2018.

64.) X. Chen, Q. Guan, L. Lo, S. Su, Z. Ren, J. Ahrens, T. Estrada, “In situ TensorView: In situ Visualization of Convolutional Neural Networks”, 2018 IEEE International Conference on Big Data (Big Data), 1899-1904, 2018.

63.) [Best Paper Award] J.Lukasczyk, E. Kinner, J. Ahrens, H. Leitte, and C. Garth, "VOIDGA: A View-Approximation Oriented Image Database Generation Approach , IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV), 2018.

62.) A. Biswas, S. Dutta, J. Pulido, J. Ahrens, “In situ data-driven adaptive sampling for large-scale simulation data summarization”, Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization 2018.

61.) J. Chen, Q. Guan, Z. Zhang, X. Liang, L. Vernon, A. McPherson, L. Lo, p. Grubel, T. Randles. Z. Chen, J. Ahrens, “BeeFlow: A Workflow Management System for In Situ Processing across HPC and Cloud Systems”, 2018 IEEE 38th International Conference on Distributed Computing Systems, 1029 - 1038, 2018.

60.) C. Gillmann, T. Wischgoll, B. Hamann, J. Ahrens, “Modeling and Visualization of Uncertainty-Aware Geometry Using Multi-variate Normal Distributions”, 2018 IEEE Pacific Visualization Symposium (PacificVis), 106-110, 2018.

59.) D. Banesh, J. Wendelberger, M. Petersen, J. Ahrens, and B. Hamann. “Change Point Detection for Ocean Eddy Analysis” In Workshop on Visualization in Environmental Sciences (EnvirVis), K. Rink, D. Zeckzer, R. Bujack, and S. Jnicke (Eds.). The Eurographics Association. 2018.

58.) X. Chen, Q. Guan, X. Liang, L. Lo, S. Su, T. Estrada, J. Ahrens, "Tensorview: visualizing the training of convolutional neural network using Paraview", Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, 11-16, 2017.

57.) M. Zeyen, J. Ahrens, H. Hagen, K. Heitmann, S. Habib, "Cosmological particle data compression in practice", Proceedings of the In Situ Infrastructures on Enabling Extreme-Scale Analysis and Visualization, 12-16, 2017.

56.) M. Larsen, J. Ahrens, U. Ayachit, E. Brugger, H. Childs, B. Geveci, C. Harrison, "The ALPINE In Situ Infrastructure: Ascending from the Ashes of Strawman", Proceedings of the In Situ Infrastructures on Enabling Extreme-Scale Analysis and Visualization, 42-46, 2017.

55.) J. Patchett, B. Nouanesengesy, J. Pouderoux, J. Ahrens, H. Hagen, “Parallel multi-layer ghost cell generation for distributed unstructured grids”, IEEE 7th Symposium on Large Data Analysis and Visualization (LDAV), 84-91, 2017.

54.) J. Woodring, J. Ahrens, J. Patchett, C. Tauxe, D. Rogers, "High-dimensional scientific data exploration via Cinema", 2017 IEEE Workshop on Data Systems for Interactive Analysis (DSIA), 1-5, 2017.

53.) V. Adhinarayanan, W. Feng, D. Rogers, J. Ahrens, S. Pakin, "Characterizing and Modeling Power and Energy for Extreme-Scale In-situ Visualization", 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 978 - 987, 2017.

52.) D. Banesh, J. Schoonover, J. Ahrens, B. Hamann, "Extracting, Visualizing and Tracking Mesoscale Ocean Eddies in Two-dimensional Image Sequences Using Contours and Moments", Workshop on Visualisation in Environmental Sciences (EnvirVis), 2017.

51.) A. Berres, T. Turton, M. Petersen, D. Rogers, J. Ahrens, K. Rink, A. Middel, D. Zeckzer, R. Bujack, "Video Compression for Ocean Simulation Image Databases", Workshop on Visualisation in Environmental Sciences (EnvirVis), 2017.

50.) J. Patchett, B. Nouanesengsy, G. Gisler, J. Ahrens, H. Hagen, "In Situ and Post Processing Workflows for Asteroid Ablation Studies", EuroVis 2017-Short Papers, 2017.

49.) T. Turton, A, Berres, D, Rogers, J, Ahrens, "ETK: An Evaluation Toolkit for Visualization User Studies", EuroVis 2017-Short Papers, 109-173, 2017.

48.) [Best Paper Award] H. Carr, G. Weber, C. Sewell, J. Ahrens, "Parallel peak pruning for scalable SMP contour tree computation", IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV), 75-84, 2016.

47.) C. Ware, D. Bolan, R. Miller, D. Rogers, J. Ahrens, "Animated versus static views of steady flow patterns", ACM Symposium on Applied Perception, pgs. 77-84, 2016.

46.) F. Samsel, S. Klaassen, M. Petersen, T. Turton, G. Abram, D. Rogers, J. Ahrens, "Interactive Colormapping: Enabling Multiple Data Range and Detailed Views of Ocean Salinity", CHI - Extended Abstracts on Human Factors, 2016.

45.) C Ware, D Rogers, M Petersen, J Ahrens, E Aygar, "Optimizing for Visual Cognition in High Performance Scientific Computing", Electronic Imaging 2016 (16), 1-9.

44.) C. Sewell, K. Heitmann, H. Finkel, G. Zagaris, S.T. Parete-Koon, P. Fasel, A. Pope, N. Frontiere, L. Lo, B. Messer, S. Habib, J. Ahrens, “Large-Scale Compute-Intensive Analysis via a Combined In-situ and Co-scheduling Workflow Approach. Proceeding of International Conference for High Performance Computing, Networking, Storage, & Analysis (SC), Supercomputing 2015, November 2015.

43.) [Best Scientific Visualization & Data Analytics Showcase], F. Samsel, M. Petersen, G. Abram, T. Turton, D. Rogers, J. Ahrens, ”Visualizing Ocean Currents and Eddies in a High-Resolution Global Ocean-Climate Model”, Proceeding of International Conference for High Performance Computing, Networking, Storage, & Analysis (SC), Supercomputing, 2015. https://vimeo.com/145875477

42.) C. Sewell, L. Lo, K. Heitmann, S. Habib, J. Ahrens, “Utilizing Many-Core Accelerators for Halo and Center Finding within a Cosmology Simulation”, Proceedings of the IEEE Symposium on Large Data Analysis and Visualization (LDAV), October 2015.

41.) W. Widanagamaachchi, K. Hammond, L. Lo, B. Wirth, F. Samsel, C. Sewell, J. Ahrens, V. Pascucci, “Visualization and Analysis of Large-Scale Atomistic Simulations of Plasma-Surface Interactions”. Proceedings of EuroVis (short paper), May 2015.

40.) X. Adhinarayanan, W. Feng, J. Woodring, D. Rogers, J. Ahrens, “On the Greenness of In-Situ and Post- Processing Visualization Pipelines”, The Eleventh Workshop on High-Performance, Power- Aware Computing at IPDPS 2015, May 2015.

39.) F. Samsel, G. Abram, M. Petersen, J. Wendelberger, T. Geld and J. Ahrens, “Colormaps that Improve Perception of High-Resolution Ocean Data”, CHI EA 2015, April 2015.

38.) J. Ahrens, S. Jourdain, P. O'Leary, J. Patchett, D. Rogers, M. Petersen, “An Image-based Approach to Extreme Scale In Situ Visualization and Analysis”, Proceeding of International Conference for High Performance Computing, Networking, Storage, & Analysis (SC), Supercomputing 2014, November 2014.

37.) B. Nouanesengsy, J. Woodring, J. Patchett, K. Myers, and J. Ahrens, “ADR Visualization: A Generalized Framework for Ranking Large-Scale Scientific Data using Analysis-Driven Refinement”, IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), 2014.

36.) W. Widanagamaachchi, P-T. Bremer, C. Sewell, L. Lo, J. Ahrens, V. Pascucci, “Data-Parallel Halo Finding with Variable Linking Lengths”, IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), 2014.

35.) Yu Su, Gagan Agrawal, Jonathan Woodring, Kary Myers, Joanne Wendelberger, James P. Ahrens: Taming massive distributed datasets: data sampling using bitmap indices, HPDC 2013.

34.) Christopher Sewell, Li-Ta Lo, James P. Ahrens: Portable data-parallel visualization and analysis in distributed memory environments, LDAV 2013.

33.) Boonthanome Nouanesengsy, John Patchett, James Ahrens, Andrew Bauer, Aashish Chaudhary, Berk Geveci, Ross Miller, Galen M. Shipman, and Dean N. Williams, "Optimizing File Access Patterns through the Spatio-Temporal Pipeline for Parallel Visualization and Analysis". Proceedings of the Workshop on Ultrascale Visualization at SC13, 2013.

32.) Jonathan Woodring, James Ahrens, Timothy J. Tautges, Tom Peterka, Venkatram Vishwanath, Berk Geveci, "On-Demand Unstructured Mesh Translation for Reducing Memory Pressure during In Situ Analysis". Proceedings of the Workshop on Ultrascale Visualization at SC13, 2013.

31.) Yu Su, Gagan Agrawal, Jonathan Woodring, Kary Myers, Joanne Wendelberger, James Ahrens, "Taming Massive Distributed Datasets: Data Sampling Using Bitmap Indices", HPDC 2013.

30.) Uliana Popov, Eddy Chandra, Katrin Heitmann, Salman Habib, James P. Ahrens, Alex Pang: Analyzing the evolution of large scale structures in the universe with velocity based methods, PacificVis 2012.

29.) Li-Ta Lo, Christopher Sewell, James P. Ahrens: PISTON: A Portable Cross-Platform Framework for Data- Parallel Visualization Operators, EGPGV 2012.

28.) Christopher Sewell, Jeremy Meredith, Kenneth Moreland, Tom Peterka, Dave DeMarle, Li-ta Lo, James Ahrens, Robert Maynard, and Berk Geveci. "The SDAV Software Frameworks for Visualization and Analysis on Next-Generation Multi-Core and Many-Core Architectures". Proceeding of the Workshop on Ultrascale Visualization at SC12, 2012.

27.) Carson Brownlee, John Patchett, Li-Ta Lo, David E. DeMarle, Christopher Mitchell, James P. Ahrens, Charles D. Hansen: A Study of Ray Tracing Large-scale Scientific Data in Two Widely Used Parallel Visualization Applications, EGPGV 2012.

26.) Christopher Sewell, Jeremy S. Meredith, Kenneth Moreland, Tom Peterka, David E. DeMarle, Li-Ta Lo, James P. Ahrens, Robert Maynard, Berk Geveci: The SDAV Software Frameworks for Visualization and Analysis on Next-Generation Multi-Core and Many-Core Architectures. Proceeding of the Workshop on Ultrascale Visualization at SC12, 2012.

25.) J. Bent, S. Faibish, J. Ahrens, G. Grider, J. Patchett, and J. Woodring, “Jitter-Free Co-Processing on a Prototype Exascale Storage Stack”, Proceedings of 28th IEEE Symposium on Massive Storage Systems and Technologies, Pacific Grove, CA, April 2012.

24.) Christopher M. Brislawn, Jonathan Woodring, Susan M. Mniszewski, David E. DeMarle, James P. Ahrens: Subband coding for large-scale scientific simulation data using JPEG 2000, SSIAI 2012.

23.) Boonthanome Nouanesengsy, James P. Ahrens, Jonathan Woodring, Han-Wei Shen: Revisiting Parallel Rendering for Shared Memory Machines, EGPGV 2011.

22.) Christopher Mitchell, James P. Ahrens, Jun Wang: VisIO: Enabling Interactive Visualization of Ultra- Scale, Time Series Data via High-Bandwidth Distributed I/O Systems, IPDPS 2011.

21.) Jonathan Woodring, Susan M. Mniszewski, Christopher M. Brislawn, David E. DeMarle, James P. Ahrens: Revisiting wavelet compression for large-scale climate data using JPEG 2000 and ensuring data precision, LDAV 2011.

20.) J. Woodring, K. Heitmann, J. Ahrens, P. Fasel, C.-H. Hsu, S. Habib and A. Pope. “Analyzing and Visualizing Cosmological Simulations with ParaView”, The Astrophysical Journal Supplement Series, Volume 195, Issue 11, June 2011.

19.) C. Hsu, J. Ahrens, K. Heitmann, "Verification of the Time Evolution of Cosmological Simulations via Hypothesis-Driven Comparative and Quantitative Visualization", PacificVis 2010.

18.) J. Ahrens, J. Woodring, D. DeMarle, J. Patchett, M. Maltrud, "Interactive Remote Large-Scale Data Visualization via Prioritized Multi-resolution Streaming", Workshop on Ultrascale Visualization, Nov. 2009.

17.) J. Patchett, J. Ahrens, S. Ahern, D. Pugmire, "Parallel Visualization and Analysis with ParaView on a Cray XT4", CUG 2009, May 2009.

16.) J. Ahrens, J. Woodring, D. DeMarle, J. Patchett, M. Maltrud, "Interactive Remote Large-Scale Data Visualization via Prioritized Multi-resolution Streaming", Workshop on Ultrascale Visualization, Nov. 2009.

15.) J. Ahrens, L. Lo, B. Nouanesengsy, J. Patchett and A. McPherson, “Petascale Visualization: Approaches and Initial Results”, Ultrascale Visualization Workshop, November 2008.

14.) Emanuele Santos, Lauro Lins, James P. Ahrens, Juliana Freire and Claudio Silva, " A First Study on Clustering Collections of Workflow Graphs", International Provenance and Annotation Workshop, 2008.

13.) James Ahrens, Nehal Desai, Patrick McCormick, Ken Martin, Jonathan Woodring, "A Modular Extensible Visualization System Architecture For Culled Prioritized Data Streaming", Visualization and Data Analysis 2007, Proceedings of the SPIE - The International Society for Optical Engineering, Volume 6495, Jan. 2007.

12.) Andy Cedilnik, Berk Geveci, Kenneth Moreland, James Ahrens, and Jean Favre, "Remote Large Data Visualization in the ParaView Framework", Eurographics Symposium on Parallel Graphics and Visualization, pg. 163-170, May 2006.

11.) N. Fout, K-L. Ma, and J. Ahrens, "Time-Varying Multivariate Volume Data Reduction", ACM SAC 2005, March 2005.

10.) P. McCormick, J. Inman, J. Ahrens, G. Roth and C. Hansen, "Scout: A Hardware-Accelerated System for Quantitatively Driven Visualization and Analysis" Proceedings of the IEEE Visualization 2004 Conference , October 2004.

9.) A. Stompel, K-L. Ma, E. Lum, J. Ahrens, J. Patchett, "Scheduled Linear Image Compositing for Parallel Volume Rendering", IEEE Symposium on Parallel and Large-Data Visualization and Graphics, October 2003.

8.) C.C. Law, A. Henderson, J. Ahrens, "An Application Architecture for Large Data Visualization: A Case Study", IEEE Symposium on Parallel and Large-Data Visualization and Graphics, October 2001.

7.) T.A. Keahey, P. McCormick, J. Ahrens, K. Keahey, "Qviz: a Framework for Querying and Visualizing Data", Proceedings of SPIE Vol. 4302 - Visual Data and Exploration and Analysis VIII, January 2001.

6.) K. Keahey, P. Beckman and J. Ahrens, "Ligature: Component Architecture for High-Performance Applications", 1st NASA Workshop on Performance-Engineered Information Systems, September 1998.

5.) J. Ahrens and J. Painter, "Efficient Sort-Last Rendering Using Compression-Based Image Compositing", Second Eurographics Workshop on Parallel Graphics and Visualization, September 1998.

4.) J. Ahrens, P. McCormick, J. Bossert, J. Reisner, J. Winterkamp, "Case Study: Wildfire Visualization", Proceedings of IEEE Visualization 1997, pp. 451-454, October 1997.

3.) J. Ahrens and C. Hansen, "Cost-Effective Data-Parallel Load Balancing", International Conference on Parallel Processing, Vol. 2, pp. 218-21, August 1995.

2.) L. Shapiro, S. Tanimoto, J. Brinkley, J. Ahrens, R. Jakobovits and L. Lewis, "A Visual Database System for Data and Experiment Management in Model-Based Computer Vision", Second CAD-Based Vision Workshop, pp. 64-72, February 1994.

1.) F. Ortega, C. Hansen and J. Ahrens, "Fast Data Parallel Polygon Rendering", IEEE Supercomputing, pp. 709-18, November 1993.