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Wednesday, June 07, 2023
1:00 PM - 2:00 PM
CNLS Conference Room (TA-3, Bldg 1690)

Postdoc Seminar

Models of Complex Dependence Structure in Sparse Dyadic Event Data of International Relations

Aaron Schein
University of Chicago - Dept. of Statistics and Data Science

For several decades, political scientists have collected data sets of “dyadic events”—i.e., micro-records of the form “country i took action a to county j at time t”. Such data sets provide an expansive and systematized view of the world that prompts data-driven approaches to the study of international relations. However, despite all the work to collect massive event data sets, there has been comparatively little work in political science to actually use them. This is partly due to the presence of “complex dependence structures” (King 2001) in the data, which violate independence assumptions of many methods in the standard statistical toolkit. In this talk, I will discuss a family of Bayesian models for measuring complex dependence structure in dyadic events. These models blend aspects of tensor decomposition, dynamical systems, and discrete admixtures to capture rich multilayer network structure and excitatory temporal dynamics in country-to-country interactions. While inspired by international relations, these models are tailored to the general statistical properties of sparse and high-dimensional discrete data and are widely applicable to problems where such data sets arise.---Bio: Aaron Schein is an Assistant Professor at the University of Chicago in the Department of Statistics and the Data Science Institute. His 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 organizing 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 factorization and dynamical systems models for analyzing 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.

Host: Juston Moore