A Practical Workshop on TDA Methods and Applications
February 20-28, 2026
FC Kohli Center, CMI, SIPCOT IT Park
The OneMath World School is a first-of-its-kind global program initiated by BIRS partner institutions. It is designed to connect young mathematical talent with leading experts in emerging fields. This initiative brings together advanced undergraduates, graduate students, and early-career researchers from around the world to collaborate with established leaders and explore cutting-edge topics at the intersection of theory and application. Visit the link for more details.
This School provides a practical introduction to topological data analysis (TDA) with emphasis on hands-on techniques and implementation. Participants will learn to apply TDA methods, particularly Persistent Homology and the Mapper algorithm, to extract meaningful features from complex datasets.
Masters and PhD students, postdocs, and early career researchers in Mathematics and Computer Science
Up to 40 participants with 8 local and invited speakers
Participants should have proficiency in either:
February 20-21, 2026
A comprehensive introduction to bring all participants to the same foundational level:
February 23-26, 2026
Four days of intensive lectures and hands-on tutorials:
February 27-28, 2026
Participants work in groups on real-life applications:
University of Florida
IISc Bangalore
Inria Saclay - Ile-de-France
University of Fribourg
University of Utah
ISI Bangalore
Additional speakers to be announced
All times are tentative and will be updated as speakers confirm.
Venue : LH 202 (2nd floor, multistory building)
| Time | Title / Topic | Speaker | Notes | |
|---|---|---|---|---|
| 20 Feb 2026 | ||||
| 09:30–11:00 | Intro to machine Learning | Madhavan Mukund | slides | |
| 11:30–13:00 | Applied topology | Priyavrat Deshpande | slides | |
| 14:00–15:15 | Python session | TBA | — | |
| 15:45–17:00 | Tutorial session | TBA | — | |
| 21 Feb 2026 | ||||
| 09:30–11:00 | Into to machine learning | Madhavan Mukund | slides | |
| 11:30–13:00 | Algorithmic topology and discrete Morse theory | Siddharth Pritam | slides | |
| 14:00–15:15 | Python session | TBA | — | |
| 15:45–17:00 | Tutorial session | TBA | — | |
| Time | Title / Topic | Speaker | Notes | |
|---|---|---|---|---|
| 23 Feb 2026 | ||||
| 09:30–11:00 | Intro to persistent homology | Steve Oudot | slides | |
| 11:30–13:00 | Applied persistent homology | Henry Adams | slides | |
| 14:00–15:15 | Tutorial session | TBA | — | |
| 15:45–17:00 | Tutorial session | TBA | — | |
| 24 Feb 2026 | ||||
| 09:30–11:00 | Into to persistent homology | Steve Oudot | slides | |
| 11:30–13:00 | Applied persistent homology | Henry Adams | slides | |
| 14:00–15:15 | Tutorial session | TBA | — | |
| 15:45–17:00 | Tutorial session | TBA | — | |
| 25 Feb 2026 | ||||
| 09:30–11:00 | Topology in Visualization | Bei Wang | slides, tutorial, datasets | |
| 11:30–13:00 | Intro to Reeb graphs | Vijay Natarajan | slides | |
| 14:00–15:15 | Topological machine learning | Bastian Rieck | slides | |
| 15:45–17:00 | Tutorial session | TBA | — | |
| 26 Feb 2026 | ||||
| 09:30–11:00 | Topology in visualization | Bei Wang | slides, tutorial, datasets | |
| 11:30–13:00 | Intro to Reeb graphs | Vijay Natarajan | slides | |
| 14:00–15:15 | Topological machine learning | Bastian Rieckn | slides | |
| 15:45–17:00 | Tutorial session | TBA | — | |
| Time | Title / Topic | Speaker | Notes | |
|---|---|---|---|---|
| 27 Feb 2026 | ||||
| 09:30–11:00 | Topology of random point samples | D. Yogeshwaran | — | |
| 11:30–13:00 | Group work | — | ||
| 14:00–15:15 | Group work | — | ||
| 15:45–17:00 | Group work | — | ||
| 28 Feb 2026 | ||||
| 09:30–11:00 | Group Work | — | ||
| 11:30–13:00 | Group work | — | ||
| 14:00–15:15 | Group work | — | ||
| 15:45–17:00 | Group work | — | ||
These hands-on notebooks are designed to complement the boot camp
and mini-course sessions. They walk you through the implemetation of some ML algorithms, computing persistent homology, and visualizing barcodes
and persistence diagrams using standard Python libraries including
Giotto-TDA, ripser, Kepler Mapper, and
scikit-learn. No prior TDA coding experience is
assumed — the notebooks are self-contained and progress from
elementary examples to more advanced pipelines.
The following projects are curated to give participants meaningful exposure to real-world applications of TDA. Each project in the first list comes with several relevant papers, suggested dataset, guiding questions. Participants are encouraged to pick a project based on their background and interests, and to bring their own data if they have a problem in mind.
View ProjectsRegistration for the School is over. Confirmed participants have already been notified.
Chennai Mathematical Institute
IISc Bangalore
Chennai Mathematical Institute