|Date||One-day workshop on Friday, 11 December 2020|
|Location||The workshop will be held virtually.|
How to participate
- Visit the Gather.Town instance of the workshop to chat with attendees and poster presenters. Check the schedule to ensure that an author is available to answer your questions live.
- Join our Slack workspace TDA in ML for more information about the workshop. Use the channel
#neurips2020_workshopto communicate with the attendees, panellists, and organisers.
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.