Speaker:

Lori Ziegelmeier - Assistant Professor, Department of Mathematics, Statistics, and Computer Science, Macalester College

Title:

Measuring the Shape of Data with Topology

Abstract:

Data of various kinds is being collected at an enormous rate, and in many different forms. Often, the data are equipped with a notion of distance that reflects similarity in some sense. Using this similarity measure, certain topological features--e.g. the number of connected components, loops, and trapped volumes--can be ascertained and can provide insight into the structure of these complex data sets. In this talk, I will introduce topology and a fundamental tool of topological data analysis, persistent homology. Then, we will consider examples of using this tool in two research projects (1) exploring the relationship between country development and geography and (2) analyzing the collective behavior of biological aggregations.

Speaker Bio:

Lori Ziegelmeier is an Assistant Professor at Macalester College. She began her college education at Colby Community College, receiving A.S. and A.A. degrees there. She then went to Colorado State University, completing B.S. and B.A. degrees in Mathematics and Liberal Arts as well as an M.S. and Ph.D. (in 2013) in Mathematics. Her research is in the area of Geometric and Topological Data Analysis, where she develops and applies techniques and algorithms in order to uncover the structure of a wide variety of data sets ranging from hyperspectral images to biological aggregations to statistics of country development to metric graphs.

Co-Sponsor:

NSF-Simons Center for Quantitative Biology (CQuB)

Live Stream:

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