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Topological Data Analysis and Beyond
Bastian Rieck · Frederic Chazal · Smita Krishnaswamy · Roland Kwitt · Karthikeyan Natesan Ramamurthy · Yuhei Umeda · Guy Wolf

Thu Dec 10 11:00 PM -- 12:00 PM (PST) @
Event URL: https://tda-in-ml.github.io/ »

The last decade saw an enormous boost in the field of computational topology: methods and concepts from algebraic and differential topology, formerly confined to the realm of pure mathematics, have demonstrated their utility in numerous areas such as computational biology, personalised medicine, materials science, and time-dependent data analysis, to name a few.

The newly-emerging domain comprising topology-based techniques is often referred to as topological data analysis (TDA). Next to their applications in the aforementioned areas, TDA methods have also proven to be effective in supporting, enhancing, and augmenting both classical machine learning and deep learning models.

We believe that it is time to bring together theorists and practitioners in a creative environment to discuss the goals beyond the currently-known bounds of TDA. We want to start a conversation between experts, non-experts, and users of TDA methods to debate the next steps the field should take. We also want to disseminate methods to a broader audience and demonstrate how easy the integration of topological concepts into existing methods can be.

Important links:

- Rocket.Chat (for asking questions)
- Slack (for asking questions)

Author Information

Bastian Rieck (ETH Zurich)
Frederic Chazal (INRIA)
Smita Krishnaswamy (Yale University)
Roland Kwitt (University of Salzburg)
Karthikeyan Natesan Ramamurthy (IBM Research)
Yuhei Umeda (Fujitsu)
Guy Wolf (Université de Motréal; Mila)

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