at the
American Institute of Mathematics, San Jose, California
organized by
Chad Giusti, Gregory Henselman-Petrusek, and Lori Ziegelmeier
Much of the existing work in topological data analysis relies on the use of persistence diagrams as a feature set for complex data. However, applied topology has the potential to provide much more detailed information about and explicit connections among complex data sets, providing quantitative methods for characterizing and investigating structure in data that go beyond classification or regression. Building these methods will require the efforts of experts in the theory of algebraic and geometric topology, who can adapt existing tools or develop novel approaches inspired by the applications. However, for these new methods to be useful outside of the domain of pure mathematics, they need to be instantiated as software.
Potential Topics:
The workshop schedule.
A report on the workshop activities.