Difference between revisions of "Heidelberg TDA Seminar (Summer 2023)"
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In recent years there has been a somewhat surprising flow of ideas from the mathematical branch of topology towards applications in the natural sciences. The tale of Topological Data Analysis (TDA) has it, that these methods provide a highly flexible, nonparametric approach to data analysis. Indeed, there are by now several well-known mathematical results that make statements of this kind rigorous. However, successful applications of TDA often build on a deep intuition about the system in question and many theoretical aspects of TDA remain active fields of research. | In recent years there has been a somewhat surprising flow of ideas from the mathematical branch of topology towards applications in the natural sciences. The tale of Topological Data Analysis (TDA) has it, that these methods provide a highly flexible, nonparametric approach to data analysis. Indeed, there are by now several well-known mathematical results that make statements of this kind rigorous. However, successful applications of TDA often build on a deep intuition about the system in question and many theoretical aspects of TDA remain active fields of research. | ||
| − | The goal of this seminar is to bring together people from various backgrounds who are interested in TDA. We will have talks on topics ranging from applications of TDA on real world problems to the abstract mathematical foundations of the subject. In addition, we aim to discuss works, which are inspired by TDA but not employing it directly. Contributions by participants are very welcome! | + | The goal of this seminar is to bring together people from various backgrounds who are interested in TDA. We will have talks on topics ranging from applications of TDA on real world problems to the abstract mathematical foundations of the subject. An emphasis is put on synergies with Machine Learning. In addition, we aim to discuss works, which are inspired by TDA but not employing it directly. Contributions by participants are very welcome! |
==Coordinates and Organization == | ==Coordinates and Organization == | ||
Revision as of 14:59, 27 April 2023
In recent years there has been a somewhat surprising flow of ideas from the mathematical branch of topology towards applications in the natural sciences. The tale of Topological Data Analysis (TDA) has it, that these methods provide a highly flexible, nonparametric approach to data analysis. Indeed, there are by now several well-known mathematical results that make statements of this kind rigorous. However, successful applications of TDA often build on a deep intuition about the system in question and many theoretical aspects of TDA remain active fields of research.
The goal of this seminar is to bring together people from various backgrounds who are interested in TDA. We will have talks on topics ranging from applications of TDA on real world problems to the abstract mathematical foundations of the subject. An emphasis is put on synergies with Machine Learning. In addition, we aim to discuss works, which are inspired by TDA but not employing it directly. Contributions by participants are very welcome!
Coordinates and Organization
Time: Thursdays 11h15 - 12h45
Location: Mathematikon (INF205), 00.200 (ground floor)
Organizers: Freya Bretz, Lukas Hahn, Daniel Spitz.
Feel free to get in touch with us in the case of questions:
structures-hiwi@mathi.uni-heidelberg.de
Schedule
The following schedule is preliminary; in particular topics can be still subject to slight changes.
| Date | Topic | Info | Speaker | Slides |
|---|---|---|---|---|
| 27.04. | Preliminary meeting (exceptionally in SR Statistik, 02.104!) | |||
| 04.05. | No TDA Seminar session | |||
| 11.05. | Persistent homology of quantum entanglement (based on arxiv:2110.10214) | Daniel Spitz | ||
| 18.05. | No TDA Seminar session (Himmelfahrt) | |||
| 25.05. | TBA | |||
| 01.06. | Kernels for persistent homology | Tim Mäder | ||
| 08.06. | No TDA Seminar session (Fronleichnam) | |||
| 15.06. | Stability for persistent homology and beyond | Lukas Waas | ||
| 22.06. | TBA | |||
| 29.06. | Literature approaches: TDA in conjunction with Machine Learning | Lukas Hahn | ||
| 06.07. | On-the-hands coding with TDA and Machine Learning | Lukas Hahn | ||
| 13.07. | SPD matrices and Machine Learning | Michael Bleher | ||
| 20.07. | Mayer-Vietoris for persistent homology | Freya Bretz |