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

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.


Current schedule can be found here.

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


  • 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.


Multiple hypothesis testing in persistent homology

We propose a general null model for persistent homology barcodes from a point cloud, to test for example acyclicity in simplicial complexes generated from point clouds. One advantage of the null model we propose is efficiency in generating a null model that applies to a broad set of hypothesis testing procedures. The second key idea in this talk is using the null model to address multiple hypothesis testing via control of family-wise error rates and false discovery rates.

Semester introduction

We will talk about the upcoming semester, look for volunteers to give talks on specific papers and give a refresher overview of persistent homology.


  • Data Science and Applied Topology Seminar
  • Reading list