Applied and Computational Topology
Research
Our research group works in the field of applied and computational topology and geometry. The focus lies on questions regarding the connectivity of data on multiple scales. This is global information, concerning the data as a whole, and inaccessible by standard means of data analysis. Popular methods in this context are persistent homology, Vietoris–Rips complexes, and Delaunay complexes (Alpha shapes).
We consider theoretical questions, such as the stability of persistence barcodes or connections to representation theory, but also computational problems, such as finding the Vietoris–Rips persistence barcode of a set of points. We study the hardness of certain computational problems arising in applied topology, and develop fast code that is widely used in applications (http://ripser.org).
Our research is supported by DFG (Collaborative Research Center Discretization in Geometry and Dynamics) and MDSI (Munich Data Science Institute).
People
Professor
Team Assistants
Nadja Vadlau
- room: MA 02.08.052
- phone: +49 (89) 289 17984
- email: vadlau (at) ma.tum.de
Diane Clayton-Winter (SFB/TR109 Discretization in Geometry & Dynamics)
PostDocs
PhD students
David Hien (co-supervised with Oliver Junge)
Maximilian Schmahl (co-supervised with Peter Albers)
Alumni
Abhishek Rathod
Projects
Ripser is a lean C++ code for the computation of Vietoris–Rips persistence barcodes. It can do just this one thing, but does it extremely well.
To see a live demo of Ripser's capabilities, go to live.ripser.org. The computation happens inside the browser (using Emscripten to compile Ripser to WebAssembly, supported on recent browsers).
The aim of this project is to develop methods for constructing coarse models of the global behavior of a system with complicated dynamics. These models will abstract from individual trajectories and rather provide dynamical information on a discretization of the underlying invariant set in form of a directed graph or a finite state Markov chain. In contrast to existing approaches, our models will incorporate information about cycling motion, generalizing the classic notion of periodic or quasiperiodic dynamics.