Tom Peterka strives to enable scalable HPC data analytics of scientific data. In other words, his team studies the use of high-performance supercomputers for the analysis and visualization of scientific data, in addition to supercomputers’ traditional role for simulation and modeling. The data originate in scientific instruments and computer simulations from some of the largest science facilities in the world.
Tom is a computer scientist at Argonne National Laboratory, scientist at the University of Chicago Consortium for Advanced Science and Engineering (CASE), adjunct assistant professor at the University of Illinois at Chicago, and fellow of the Northwestern Argonne Institute for Science and Engineering (NAISE). His research interests are in large-scale parallel in situ analysis of scientific data. Recipient of the 2017 DOE early career award and three best paper awards, Peterka has published in ACM SIGGRAPH, IEEE VR, IEEE TVCG, and ACM/IEEE SC, among other top conferences and journals. Peterka received his Ph.D. in computer science from the University of Illinois at Chicago in 2007, and he currently works actively in several DOE- and NSF-funded projects.