The University of Arizona

Events & News

CS Colloquium

DateThursday, March 3, 2016
Time11:00 am
Concludes12:15 pm
LocationGould-Simpson 906
DetailsPlease join us for coffee and light refreshments at 10:45am, Gould-Simpson, 9th Floor Atrium

Faculty Host: Dr. John Kececioglu
SpeakerBei Wang
AffiliationResearch Scientist, Scientific Computing and Imaging Institute, Univ Utah

Understanding the Shape of Data with Topological Data Analysis and Visualization: from Vector Fields to High-Dimensional Point Clouds

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.


Bei Wang 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.