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