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 4419. 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.


With the fast developing situation around CUNY and NYCs reactions to the Coronavirus, we have decided to cancel the Data Science and Applied Topology seminar for the remainder of the spring semester 2020.

We expect to welcome you back for the fall seminar early September.


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.


Topology and Dynamics of Biological Networks

Complex networks have been of great interests in many disciplines including mathematics, computer science, biology, and social studies. In molecular biology, networks can be used to model the interacts within a biological system. Such a network often consists of units with various levels of activities that evolve over time, mathematically represented by the dynamics of the network. The interaction between units is represented by the topology of a graph. An interesting problem is to study the connection between topology and dynamics of such networks. In particular, the so called reverse engineering problem asks for the topology of the network given information on its dynamics.

In this talk, we focus on a certain Boolean network model for biological networks. Under this model, the reverse engineering problem is naturally related to the Satisfiability Problem. We will show the following results. (1). The decision problem for network solution can be solved in polynomial time.
That is, given information on dynamics, there is a polynomial time algorithm that determines either (a) there is no network which yields the given dynamics or (b) there is such a network. In the case of (b), the algorithm provides a specific network solution. (2). The problem of finding a minimal network with the given property of dynamics is NP-hard.


  • Data Science and Applied Topology Seminar
  • Reading list