Shahab Asoodeh

Postdoctoral Scholar, University of Chicago

I am interested in the applications of discrete (differential) geometry and information theory in machine learning and network science. In particular, my main research at the Knowledge Lab focuses on the following two broad questions:  1) How to quantify geometry of graphs, simplicial complexes, and more generally, hypergraphs and to interpret them in real-world networks? And 2) How to use geometry and information theory to define and quantify fairness and privacy in machine learning and data mining?

I am fortunate to work with James Evans at the Knowledge Lab and Ishanu Chattopadhyay at the Institute of Genomics and System Biology (IGSB).

I received my PhD and MSc both in applied mathematics from Queen’s University, Canada, in 2017 and  2011 and a MSc in Electrical Engineering from ETH Zurich and TU Delft in 2010.