Monday, March 14, 2016 - 9:00am to 10:00am
ALS 4001 ** note the different time and place **

Speaker Information

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.

Speaker Bio

Bei Wang (http://www.sci.utah.edu/~beiwang/) is a research scientist at the Scientific Computing and Imaging (SCI) Institute of the University of Utah. She received her Ph.D. in Computer Science from Duke University in 2010. There, she also earned a certificate in Computational Biology and Bioinformatics. From 2010 to 2011, she was a Postdoctoral Fellow at the SCI Institute. Her research interests include data analysis and data visualization, computational topology, computational geometry, computational biology and bioinformatics, machine learning and data mining.