The CUNY Data Science and Applied Topology Reading Group is joint between the Mathematics and Computer Science programmes. We meet Fridays 11.45 -- 12.45 in GC 3209. You can contact us at cunygc@appliedtopology.nyc.

Our plan is to primarily read and discuss seminal papers in data science, in applied topology and in topological data analysis. Each seminar one participant takes the responsibility to present a paper and prepare items for discussion. We expect occasionally to be able to invite external speakers.

## Schedule

Current schedule can be found here.

We will be sending out announcements through a mailing list; you can subscribe here.

## Organizers

- Mikael Vejdemo-Johansson, Computer Science Programme, CUNY Graduate Center; Department of Mathematics, CUNY College of Staten Island
- Azita Mayeli, Mathematics Programme, CUNY Graduate Center; Department of Mathematics, CUNY Queensborough Community College

## Suggested papers

We have compiled a list of papers that might be interesting to present.

# Schedule

### Obayashi, Hiraoka, Kimura -- Persistence diagrams with linear machine learning models

We will read and discuss the paper Obayashi, Hiraoka, Kimura -- Persistence diagrams with linear machine learning models

### Topological Structure of Linear Manifold Clustering

In the topological data analysis, the first step is a construction of a simplicial complex from a discrete points set D sampled from some manifold. In this paper, we present an algorithm for the efficient computation of such simplicial complex which utilizes clustering structure, comprised of subspace clusters, of the point set for speeding up a complex construction procedure while keeping relevant topological invariants of the underlying sampled manifold. Experiments show that the proposed construction algorithm provides smaller complexes with less noise which gives a better homological picture than other construction methods as well as an improved construction performance and a topological invariant interpretability on a geometrical level.