Department of Mathematics and Statistics

Department of Mathematics and Statistics
Department of Mathematics and Statistics
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Interdisciplinary Topological Data Analysis Learning Seminar

Topological data analysis (TDA) is a class of topological techniques for understanding large, noisy, possibly incomplete data. The techniques have a particular focus on understanding the large-scale coordinate-independent "shape" of the data. While perhaps the most famous application of TDA is the discovery of a new type of breast cancer from an old dataset, the techniques are finding use in everything from medical imaging to progression analysis of disease, signal analysis, viral evolution, complex networks, AI, & much more (wikipedia has a more extensive list of applications). The aim of this seminar is to build a common language for understanding TDA & then hopefully to shift to a focus on applications. The tentative structure involves learning and sharing knowledge on dimensionality reduction, clustering, persistent homology, probabilistic analysis and homology inference in a format that includes different seminar members sharing knowledge through prepared presentations, coupled with group discussion.

Topological Data Analysis - Multiple Speakers

Monday, November 18th, 2019

Time: 2:30 p.m. Place: Goodes Hall 120

Speaker: Jordan Kokocinski, Arne Kuhrs, Catherine Pfaff, David Riegert Luke Steverango

Topics:  The graduate students have a well-prepared presentation on the Bubenik worksheet. This is highly recommended, even if you've missed a few meetings &/or are confused about homology. With leftover time Pfaff will provide some supplement to last week's presentation.

All are welcome!