Abstract
DATE: |
April 9, 2009 |
TIME: |
1:15-3:00 pm |
LOCATION: |
Center for Clinical Sciences Research (CCSR), Rm 4205 |
TITLE: |
Topology and Data |
SPEAKER: |
Gunnar E. Carlsson |
Many kinds of data can be profitably analyzed by studying a distance function on the data set in question. This notion of distance may come as a result of a precise, highly developed piece of theory (as in classical mechanics) or may simply reflect an intuitive notion of similarity (as in genomics). When studying distance functions of the second kind, it is often desirable to obtain information which has a degree of robustness to changes in the metric. Since theoretical topological methods are designed exactly to be robust to changes in metric, this suggests the possibility that topological methods can be adapted to study qualitative properties of data sets of this type. In this talk, I will discuss several directions in which this general philosophy can be implemented, with examples.
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