Colloquia, Seminars and Conference News
Title : Extraction of Salient Structures for Analysis and Visualization of Scientific Data
Date : November 30, 2007. (11:00 am) Tea starts half an hour before each seminar
Location: ITEB 336
Speaker : Prof. Xavier Tricoche
Abstract:
As they strive to deepen their understanding of multifaceted natural phenomena scientists and engineers leverage ever increasing computational resources to study, alongside with experimental data, predictive models of growing size and complexity through numerical simulations. The corresponding information explosion brings about the urgent need for effective visualization and analysis techniques to bridge the widening gap between the sheer amount of available data and the resulting insight.
In this talk I will present several complementary approaches for the characterization and visual representation of the high-level structure and inherent coherence exhibited by numerical data in basic science, engineering, and medicine. After providing a brief introduction to the topological framework in scientific visualization I will explain how topology-based methods permit the automatic extraction of a schematic graph that captures the global structure of vector and tensor fields. I will show several applications of this technique in computational fluid dynamics as well as in fusion research. I will proceed by showing how some practical limitations of the topological formalism can be overcome in the context of transient turbulent flows and brain imaging while resting the analysis upon similar underlying principles of spatial coherence. I will conclude my talk by pointing out exciting new fields of application for this general methodology.
Bio: Xavier Tricoche is an Assistant Professor of Computer Science and a member of the Computer Graphics and Visualization Lab at Purdue University that he joined in 2007. Previously, he was a Research Assistant Professor in the School of Computing and a member of the Scientific Computing and Imaging Institute at the University of Utah that he joined in 2004 as a postdoctoral fellow. He studied computer science and applied mathematics in Grenoble, France and obtained his PhD in Computer Science from the University of Kaiserslautern in Germany for his work on topological methods in vector and tensor visualization. His current research focuses on the structural analysis and effective visual representation of large-scale scientific data in applications including cardiovascular research, neuroscience, computational fluid dynamics, and fusion research.
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