地点：Zoom (ID 642 0299 8817)
Topological data analysis (TDA) is a methodology for using tools from algebraic topology to obtain information about the “shape” of data sets. The most popular TDA algorithms provide multiscale invariants that capture subtle geometric properties in data. Although it seems evident that such methods should be of tremendous scientific value, there are many technical and conceptual challenges associated to efforts to use TDA in applications. This talk will provide a gentle overview of ideas from TDA as well as an approach to applying these methods for scientific inference, motivated by applications from genomics.
About the speaker
Andrew Blumberg is a Professor of Mathematics at the University of Texas, Austin and a visiting Professor in the Department of Mathematics and Computer Science at Columbia University and the Irving Institute for Cancer Dynamics. Prior to arriving in Austin, he was an NSF postdoctoral fellow from 2005 to 2009 at Stanford, with a year’s stint as a member at the Institute for Advanced Study in 2007–2008. Prof. Blumberg received his Ph.D. in 2005 from the University of Chicago.