Gunther H. Weber - Topological Analysis of Large-scale Data
Talare: Gunther H. Weber, Assistant Professor of Computer Science at UC Davis, Berkeley, California
Tid: 30 maj, kl 13.15
Many physical phenomena can be modeled using scalar functions. For example, the density distribution of particles highlights the structure of the universe in cosmological simulations, the fuel consumption rate indicates burning/extinct regions in combustion simulations, and the distance from a material boundary characterizes the structure of porous materials. Since data sizes in all applications grow beyond sizes that can be visualized directly, an increasingly important approach is to detect salient features in the data and use these features for subsequent analysis and visualization. Topological analysis has proven to be a powerful tool to detect such features since it supports feature definitions based on the general concept of “connectedness” and provides means to characterize the prominence and stability of features. However, the global nature of topological methods makes their parallelization challenging. In my talk, I will present a distributed representation of the merge tree, an topological structure that arises in the analysis of features that can be described via thresholding of a scalar function. This new distributed representation reduces the amount of communication necessary during parallel merge tree computation and subsequent parallel analysis. In the second half of my talk, I will describe the combination of topological descriptors with associated geometric properties to facilitate the analysis of porous media used for carbon sequestration.
Gunther H. Weber is a Research Scientists in LBNL's Computational Research Division and an Adjunct Assistant Professor of Computer Science at UC Davis. His research interests include computer graphics, scientific visualization, data analysis, topological data analysis methods, parallelization of visualization algorithms, hierarchical data representation methods, and bioinformatics. Prior to joining the LBNL Visualization group and the NERSC Analytics team, Gunther worked first as a postdoctoral scholar and later as a Project Scientist at the Institute for Data Analysis and Visualization (IDAV) at UC Davis focusing on visualization of three-dimensional gene expression data (with researchers of LBNL's Genomics and Life Sciences divisions), topological exploration of scalar data, and visualization of brain imaging data and experimental earthquake data. Gunther earned his Ph.D. in computer science, with a concentration on graphics and visualization, from the University of Kaiserslautern, Germany in 2003.
Senast uppdaterad: Tue May 28 08:58:41 CEST 2013