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DESCRIPTION:Title: Summaries and Distances for Topological Data Analysis\n\
nAbstract: Topological data analysis starts with encoding data as a diagram
of topological spaces. To this diagram we apply standard topological\, alg
ebraic\, and combinatorial tools to produce a summary of our data. Next\, w
e would like to be able perform a quantitative analysis. I will show how a
family of distances\, called Wasserstein distances\, arise in a natural way
\, and how an extension of these ideas produces a summary for which we have
not only distances but also angles (in fact\, a Hilbert space) allowing us
to apply statistics and machine learning. Key mathematical ingredients wil
l include optimal transport\, Mobius inversion\, and Morse theory.\n\nBio:
Peter Bubenik obtained his PhD from the University of Toronto in 2003 and i
s Professor in the Department of Mathematics and the University of Florida.
He is a researcher in the NSF-Simons Southeast Center for Mathematics and
Biology and he was the founding director of the Applied Algebraic Topology
Research Network. In his research\, he develops new tools for summarizing a
nd visualizing large\, complex\, high-dimensional data by combining ideas f
rom topology\, algebra\, statistics and machine learning\, and he works wit
h collaborators to use these methods to analyze data.
DTEND:20210219T180000Z
DTSTAMP:20210420T013123Z
DTSTART:20210219T170000Z
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SUMMARY:Peter Bubenik Special Seminar
UID:tag:localist.com\,2008:EventInstance_35918648144166
URL:http://calendar.northeastern.edu/event/peter_bubenik_special_seminar
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