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 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.

SEMINAR CANCELLED FOR SPRING 2020

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.

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

Efficient call pricing model discovery by backpropagation through time

In this paper, we propose a novel call pricing model discovery method using the Heston model to find the set of parameter values that minimize the square loss between our model’s predictions and observed call prices given by market makers such as Jean Street or Two Sigma. The goal of this article is to formulate a stochastic optimization method for the Heston model over a certain period of time. The novelty of this paper is that we treat the Heston model as a Recurrent Neural Network and derive the Gradient of the Heston model by Backpropagation Through Time[1][2] to reduce the computation time for obtaining the gradient from O(τ 2 ) to O(τ ). Further, to stabilize the gradient, we extend our method by adding min-batch method. To our best knowledge, this is the first paper to propose a SGD based Heston calibration method by min-batch extension. Our method minimizes the square loss twice more than model a trivial call pricing discovery method using the Black-Scholes-Merton model.


Pages

  • Data Science and Applied Topology Seminar
  • Reading list