Prof. Dr. Ulrich Bauer



Academic Career and Research Areas

Professor Bauer works in the field of applied and computational topology. He focuses on questions regarding the connectivity of data on multiple scales. This is global information, concerning the data as a whole, and inaccessible by traditional methods of data analysis. In addition to investigating the theoretical foundations of these methods, he also develops leading computational methods for the analysis of large-scale data sets.
After graduating in computer science at TUM, Ulrich Bauer worked at Freie Universität Berlin and Georg-August-Universität Göttingen, where he obtained a doctoral degree with distinction in mathematics for his thesis on "Persistence in Discrete Morse Theory". He then joined the Institute of Science and Technology Austria as a postdoctoral researcher, where he worked on topological data analysis. In 2014, he joined TUM, establishing the research group for Applied and Computational Topology. Professor Bauer is a member of the executive board of the collaborative research center Discretization in Geometry and Dynamics and the advisory board of the Centre for Topological Data Analysis.




  • ATMCS Best New Software Award (2016)
  • Best Paper Award TopoInVis (2013)
  • Apple Design Award (2003)
  • O’Reilly Mac OS X Innovators Award (2003)

U. Bauer. Ripser: efficient computation of Vietoris–Rips persistence barcodes. Journal of Applied and Computational Topology, 2021 (in press, available online).


U. Bauer, C. Landi, and F. Mémoli. The Reeb Graph Edit Distance is Universal. Foundations of Computational Mathematics, 2020 (in press, available online).


U. Bauer and H. Edelsbrunner. The Morse theory of Čech and Delaunay complexes. Transactions of the American Mathematical Society, 369(5):3741–3762, 2017.


U. Bauer and M. Lesnick. Induced matchings and the algebraic stability of persistence barcodes. Journal of Computational Geometry, 6(2):162–191, 2015.


J. Reininghaus, S. Huber, U. Bauer, and R. Kwitt. A stable multi-scale kernel for topological machine learning. In Conference on Computer Vision and Pattern Recognition (CVPR 2015), pages 4741–4748. IEEE, 2015.