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October 16, 2018 | 2:30 p.m. - 4:00 p.m.
Category: Seminar
Location: Undergraduate Library, David Adamany Bernath Auditorium | Map
5155 Gullen Mall
Detroit, MI 48202
Cost: Free
Audience: Academic Staff, Current Graduate Students, Current Undergraduate Students, Faculty

The Office of the Vice President for Research is pleased to host the SciComp@Wayne seminar on Tuesday, October 16, 2018 from 2:30 to 4:00 p.m. at the David Adamany Undergraduate Library in the Bernath Auditorium. The seminar is free and open to the public; registration is requested.

The SciComp@Wayne Seminar Series presents, "Some basic ideas from topological data analysis" with Andrew Salch, Ph.D., associate professor, Department of Mathematics, Wayne State University. Dr. Salch received a B.S. in Mathematics from Portland State University and his M.A. and Ph.D. in Mathematics from the University of Rochester. His work is in algebraic topology, specifically relationships between stable homotopy groups of spheres and special values of L-functions.

Abstract:

This seminar will give an introduction to the relatively new field of topological data analysis (TDA). Algebraic topology is a discipline in mathematics which emerged in the 1890s with the work of Henri Poincare, and since then has been indispensable in pure mathematics, as a tool to solve classification problems in geometry, especially in the geometry of high-dimensional spaces: for example, the spaces of states of a mechanical system subject to various constraints, like conservation laws, is typically a non-Euclidean geometric space with many dimensions. Since we cannot very well use our ordinary geometric intuition to study and classify geometric spaces with more than four dimensions, tools had to be developed in pure mathematics--specifically, in algebraic topology--to handle such spaces. Much later, in the 2000s, it was realized that, because these topological tools were designed for the study of high-dimensional geometric spaces, they could also be fruitfully used to discover patterns and structure in high-dimensional data sets from the applied sciences, without any need for dimensional reduction methods that are common when carrying out data analysis using traditional statistical methods. The talk will survey some of the basic ideas and computational tools in TDA and a bit of how their software implementations work, and it'll show off a few applications of TDA to medical imaging.

A short reception will immediately follow the seminar. 

For more information about this event, please contact Kayla Watson at 3135775600 or ft2868@wayne.edu.