“This workshop will focus on the following question: Which promising directions in computational topology can mathematicians and machine learning researchers work on together, in order to develop new models, algorithms, and theory for machine learning? While all aspects of computational topology are appropriate for this workshop, our emphasis is on topology applied to machine learning — concrete models, algorithms and real-world applications.”
More here: http://topology.cs.wisc.edu
This workshop will be devoted to generalizations of persistent homology with a particular emphasis on finding calculable algebraic invariants useful for applications. Applications of persistence — for example, signal processing, drug design, tumor identification, shape classification, and geometric inference — rely on the classification of persistence via barcodes, geometrization of the space of barcodes via metrics or as an algebraic variety, and on efficient algorithms. Accordingly, this workshop will bring together theoriticians, computer scientist, and the users of computational topology.
The main topics for the workshop are:
- Generalizations of persistence: multidimensional persistence, well groups, (co)sheaves
The workshop will differ from typical conferences in some regards. Participants will be invited to suggest open problems and questions before the workshop begins, and these will be posted on the workshop website. These include specific problems on which there is hope of making some progress during the workshop, as well as more ambitious problems which may influence the future activity of the field. Lectures at the workshop will be focused on familiarizing the participants with the background material leading up to specific problems, and the schedule will include discussion and parallel working sessions.
Space and funding is available for a few more participants. If you would like to participate, please apply by filling out the on-line form no later than May 15, 2014. Applications are open to all, and we especially encourage women, underrepresented minorities, junior mathematicians, and researchers from primarily undergraduate institutions to apply.