Projects

I am the lead PI for the Foundational Discovery Models (FDM) thrust of the MAthematics, ComputinG, and NETworking for Resource-Efficient Computational Science (MAGNET) Competitive Portfolios project. MAGNET-FDM leverages the power of machine learning models for scientific visualization to expand the frontier of science and reduce the time to discovery.

I am one of the lead developers for Viskores, a toolkit of scientific visualization algorithms for emerging processor architectures. Viskores supports the fine-grained concurrency for data analysis and visualization algorithms required to drive extreme scale computing by providing abstract models for data and execution that can be applied to a variety of algorithms across many different processor architectures.

I am a member of the RAPIDS3 SciDAC Institute for Computer Science, Data, and Artificial Intelligence. My role in RAPIDS3 is to work with DOE Office of Science application teams in addressing visualization challenges for science discovery.

I am an active member in ParaView development. ParaView is a general-purpose scientific visualization tool. ParaView is designed to analyze extremely large data sets using distributed memory computing resources. It can be run on supercomputers to analyze data sets at extreme scale as well as on laptops for smaller data. Our recent work involves running ParaView in situ with simulation.

I developed the IceT parallel rendering library and continue to maintain it. In addition to providing accelerated rendering for a standard display, IceT provides the unique ability to generate images for tiled displays.

I am one of the organizers of the VisLies event commonly held in conjunction with the IEEE Vis conference. VisLies is an irreverent but informative event where we present (and often ridicule) examples of visual representations that misrepresent the underlying phenomena.

I created several LaTeX file build scripts called UseLATEX.cmake for use with CMake to build my dissertation. I continue to maintain and use these scripts.

(2021-2025) I was a member of the RAPIDS2 SciDAC Institute for Computer Science, Data, and Artificial Intelligence. My role in RAPIDS2 was to work with DOE Office of Science application teams in addressing visualization challenges for science discovery.

(2014–2025) I was the lead for the VTK-m project, which was the predecessor to Viskores. The name was changed to join the High Performance Software Foundation (HPSF).

(2014–2017) I was the lead PI for the XVis project, which brings together the key elements of research to enable scientific discovery at extreme scale. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

(2013–2017) I was a co-PI for the SciDAC Scalable Data Management, Analysis, and Visualization Institute (or SDAV for short). The SDAV mission is to actively work with application teams to assist them in achieving breakthrough science and will provide technical solutions in the data management, analysis, and visualization regimes that are broadly applicable in the computational science community.

(2010–2014) I was leading the Dax project for next-generation visualization tools. Dax provides a framework for designing visualization algorithms with massive amounts of concurrency. The first iteration of this project is focusing on GPU accelerators with a transition plan for supporting future architectures. The Dax software was one of the precursors to VTK-m and Viskores.

(2007–2012) I was a co-PI for the SciDAC Institute for Ultrascale Visualization (or UltraVis for short). The UltraVis mission is to address the petascale visualization challenges facing computational science and engineering.