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Geometry and Topology in Data Science (Spring 2024)
Organization
- The Organizers: Alvaro Diaz, Marzieh Eidi, Celia Hacker, Guillermo Restrepo, Daniel Spitz, Diaaeldin Taha, Francesca Tombari
- MPI Seminar Page: Mathematical Methods in Data Science
- Contact: To contact the organizers, email the lab at
lab [at] mis [dot] mpg [dot] de. - Mailing List: To stay informed of Lab activities, including this group's meetings, join the Lab mailing list.
Schedule
| Week | Date | Time | Location | Speaker | Title |
|---|---|---|---|---|---|
| Week 19 | Fri 10.05 | 09:00–10:30 | E1 05 | Justin Curry | “To Predict is NOT to Explain” |
| Week 20 | Fri 17.05 | 09:00–10:30 | — | — | CAG Conference - NO MEETING |
| Week 21 | Fri 24.05 | 09:00–10:30 | G3 10 | Celia Hacker | TBA |
| TBA | TBA | Francesca Tombari | TDA Course 01/06 | ||
| Week 22 | Fri 31.05 | 09:00–10:30 | G3 10 | Jeff Philips | The Geometry of Kernel Methods and Kernel Range Spaces |
| TBA | G3 10 | Francesca Tombari | TDA Course 02/06 | ||
| Week 23 | Fri 07.06 | 09:00–10:30 | E1 05 | Simon Telen | TBA |
| — | — | Francesca Tombari | NO COURSE | ||
| Week 24 | Fri 14.06 | 09:00–10:30 | G3 10 | Parvaneh Joharinad | TBA |
| TBA | G3 10 | Francesca Tombari | TDA Course 03/06 | ||
| Week 25 | Fri 21.06 | 09:00–10:30 | E1 05 | Bei Wang | Topology-Preserving Data Compression |
| TBA | TBA | Francesca Tombari | TDA Course 04/06 | ||
| Week 26 | Fri 28.06 | — | — | — | ScaDS Summer School - NO MEETING |
| TBA | TBA | Francesca Tombari | TDA Course 05/06 | ||
| Week 27 | Fri 05.07 | 09:00–10:30 | E1 05 | Marzieh Eidi | TBA |
| TBA | TBA | Francesca Tombari | TDA Course 06/06 | ||
| Week 28 | Fri 12.07 | 09:00–10:30 | E1 05 | Karel Devriendt | TBA |
| Week 29 | Fri 19.07 | 09:00–10:30 | E2 10 | SPEAKER TBA | TBA |
| Week 30 | Fri 26.07 | 09:00–10:30 | E1 05 | SPEAKER TBA | TBA |
Abstracts
Week 19
Speaker: Justin Curry (University of Albany, USA)
Coordinates: Fri 10.05, 9–10:30 AM, MiS E1 05
Title: “To Predict is NOT to Explain”
Abstract: Modern day neural networks are amazing prediction machines, but to get at explanations one has to understand higher order relations between data as they fiber over their predictions. In this talk I will connect the urgent questions of modern data science with the distinguished history of applied topology by considering simple geometric examples and probing them with increasingly complicated tools. Ideas from dynamics, stratification theory and sheaf theory will be introduced in a loose and intuitive fashion to trace future directions for research.
Week 20
Week 21
Week 22
Week 23
Week 24
Week 25
Speaker: Bei Wang (University of Utah)
Coordinates: Fri 21.06, 9–10:30 AM, MiS E1 05
Title: Topology-Preserving Data Compression
Abstract: Existing error-bounded lossy compression techniques control the pointwise error during compression to guarantee the integrity of the decompressed data. However, they typically do not explicitly preserve the topological features in data. When performing post hoc analysis with decompressed data using topological methods, preserving topology in the compression process to obtain topologically consistent and correct scientific insights is desirable. In this talk, we will discuss a couple of lossy compression methods that preserve the topological features in 2D and 3D scalar fields. Specifically, we aim to preserve the types and locations of local extrema as well as the level set relations among critical points captured by contour trees in the decompressed data. This talk is based on joint works with Lin Yan, Xin Liang, Hanqi Guo, and Nathan Gorski.