Dr. Valerio Pascucci is the John R. Parks Inaugural Endowed Chair of the University of Utah and the Founding Director of the Center for Extreme Data Management Analysis and Visualization (CEDMAV). Valerio is also a Faculty of the Scientific Computing and Imaging Institute, a Professor of the School of Computing, University of Utah, and the CEO of ViSUS LLC (visus.net with open source distributions at visus.org). Valerio was named Laboratory Fellow at PNNL and was recently a visiting professor in KAUST University. Before joining the University of Utah, Valerio was the Data Analysis Group Leader of the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory, and an Adjunct Professor of Computer Science at the University of California Davis. Valerio's research interests include Big Data management and analytics, progressive multi-resolution techniques in scientific visualization, High Performance Computing, discrete topology, geometric compression, computer graphics, computational geometry, geometric programming, and solid modeling. Valerio’s interests in fundamental research are also complemented by entrepreneurial activities that facilitate the societal impact of new technology the commercialization of innovative products. Valerio is the coauthor of more than two hundred refereed journal and conference papers and book chapters and is an Associate Editor of the IEEE Transactions on Visualization and Computer Graphics.
Extreme Data Management, Analysis, and Visualization for Scientific Discovery and Economic Development
Effective use of data management techniques for the analysis and visualization of massive data models is crucial for the success of any supercomputing center, for creating a data-intensive scientific investigation cyberinfrastructure, and for a growing number of industrial endeavors. As exascale computing progresses, data movement challenges have fostered innovation leading to complex streaming workflows which take advantage of being able to process in-motion data. This technology is also being used to more broadly investigate sensing data and is being deployed in commercial products, with direct economic and societal impact.
The techniques developed at the Center for Extreme Data Management Analysis and Visualization (CEDMAV) allow for the building of a scalable data movement infrastructure for fast I/O while organizing the data in a way that makes it immediately accessible for analytics and visualization. In this talk I will share not only these techniques, but also a topological analytics framework that allows for processing data in-situ and for achieving massive data reductions while maintaining the ability to explore the full parameter space for a featured selection. Overall, this technological innovation leads to a flexible data streaming workflow that allows for working with massive simulation models without compromising the interactive nature of the exploratory process that is characteristic of the most effective data analytics and visualization environment. These data streaming technologies are increasingly useful in experimental facilities, e.g., microscopes used in neuroscientific research, and synchrotron light sources for materials science. The distributed deployment of these technologies is poised to provide cost effective solutions in fields such as precision agriculture, surveillance, telemedicine, and many others as well.