Understanding the Shape of Data with Topological Data Analysis and Visualization: from Vector Fields to High-dimensional Point Clouds
ALS 4001 ** note the different time and place ** Mon, 03/14/2016 - 9:00am Bei Wang Research Scientist, Scientific Computing and Imaging (SCI) Institute, University of Utah Abstract: Large and complex data arise in many application domains, such as nuclear engineering, combustion simulation, weather prediction and brain imaging. However, their explosive growth in size and complexity is more than enough to exhaust our ability to apprehend them directly. Topological techniques which capture the "shape of data" have the potential to extract salient features and to provide robust descriptions of large and complex data. My research develops pertinent theoretical and algorithmic advancements in topological data analysis, and establishes their applications in simplifying and accelerating the visualization and analysis of large, complex data sets. In particular, in this talk I will describe a novel visualization framework for the simplification and visualization of vector fields, based on the topological notion of robustness that quantifies their structural stability. I will also discuss several other representative areas in my research that focus on developing novel, scalable and mathematically rigorous ways to rethink about complex forms of data, from high-dimensional point clouds, to large-scale networks and multivariate ensembles. Bio: URL: http://eecs.oregonstate.edu/colloquium/understanding-shape-data-topological-data-analysis-and-visualization-vector-fields-high _______________________________________________ Colloquium mailing list [email protected] https://secure.engr.oregonstate.edu/mailman/listinfo/colloquium
