EP Math and Data
STRUCTURES | Exploratory Project Mathematics and Data (2019 – present)
The Exploratory Project “Mathematics and Data” (EP Math & Data) brings together researchers interested in the developing interface between topology, geometry, data science, and their applications inside the STRUCTURES Cluster of Excellence at Heidelberg University and beyond.
A wiki collected introductory material, tutorials and case studies that illustrate how topological and geometric ideas inform modern data analysis, and seminar schedules. With the wiki gone, this page acts as the hub for the preserved content. The material on the wiki was authored and curated by: Michael Bleher, Lukas Hahn, Freya Jensen, Maximilian Schmahl, and Daniel Spitz.
Workshops & Talk Series
- GTML 2025: Geometry, Topology, and Machine Learning
- Tea, Coffee, Cake and TDA
- TMDA 2021: 2nd Workshop on Topological Methods in Data Analysis.
- TMDA 2020: 1st Workshop on Topological Methods in Data Analysis.
Journal Club and Seminar
- Winter 2025/26
- Summer 2025
- Summer 2023
- Winter 2022/23
- Summer 2022
- Winter 2021/22
- Summer 2021
- Winter 2020/21
- Summer 2020
- Winter 2019/20
Further Material
What began as an informal welcome packet for new collaborators quickly grew into a small library of background material. The following pieces remain the best starting points for anyone exploring the project today:
- What is Topological Data Analysis? A high-level overview of the field, its goals, and its main methods.
- Mathematical Background A brief overview of the mathematical concepts most relevant to TDA.
- Case studies that explain how persistent homology and related tools surfaced unexpected structure in cosmology, genomics, and materials science.
- Gudhi Tutorial and JavaPlex Tutorial.
Full index of the original wiki pages.