Aaron's research develops methodology in Bayesian statistics, machine learning, and applied causal inference for incorporating modern large-scale data into the social sciences.

Prior to joining the University of Chicago, Aaron was a postdoctoral fellow in the Data Science Institute at Columbia University, where he worked with David Blei and Donald Green on digital field experiments to assess the causal effects of friend-to-friend organising on voter turnout in US elections.

Aaron received his PhD in Computer Science in 2019 from UMass Amherst under the guidance of Hanna Wallach.

His dissertation developed tensor factorisation and dynamical systems models for analysing large-scale dyadic data of country-to-country interactions.

During his PhD, Aaron interned at Microsoft Research, Google, and the MITRE Corporation.

Prior to that, he earned his MA in Linguistics and BA in Political Science from UMass Amherst.

www.aaronschein.com

He tweets @AaronSchein