[Colloquium] 3/10: Data Science/Stats Candidate Talk - Aaron Schein (Columbia)

[Rob Mitchum]

*Data Science Institute/Statistics Candidate Seminar*


*Aaron Schein*
*Postdoctoral Fellow*
*Columbia University*

*Thursday, March 10th*
*4:30 p.m. - 5:30 p.m.*
*In Person: John Crerar Library, Room 390*
*Remote: Live Stream <http://live.cs.uchicago.edu/aaronschein/> or Zoom
<https://uchicago.zoom.us/j/97699156050?pwd=MHlVU2twNUJtbzdwTTJmS3d1TWp3dz09>
(details
below)*


*Measurement and Experimentation in Complex Sociopolitical Processes*

Complex social and political processes at many scales—from interpersonal
networks of friends to international networks of countries—are a central
theme of computational social science. Modern methods of data science that
can contend with the complexity of data from such processes have the
potential to break ground on long-standing questions of critical relevance
to public policy. In this talk, I will present two lines of work on 1)
estimating the causal effects of friend-to-friend mobilization in US
elections, and 2) inferring complex latent structure in dyadic event data
of country-to-country interactions. In the first part, I will discuss
recent work using large-scale digital field experiments on the mobile app
Outvote to estimate the causal effects of friend-to-friend texting on voter
turnout in the 2018 and 2020 US elections. This work is among the first to
rigorously assess the effectiveness of friend-to-friend “get out the vote”
tactics, which political campaigns have increasingly embraced in recent
elections. I will discuss the statistical challenges inherent to
randomizing interactions between friends with a “light touch” design and
will describe the methodology we developed to identify and precisely
estimate causal effects. In the second part of this talk, I will discuss
hierarchical Bayesian modeling of dyadic event data sets in international
relations which contain millions of micro-records of the form “country i
took action a to country j at time t”. The models I will discuss blend
elements of tensor decomposition and dynamical systems to capture complex
temporal and network dependence structure in the data. Approximate
posterior inference relies on new auxiliary variable augmentation schemes
and theorems about the properties and relationships between different
discrete distributions. At the end of the talk, I will outline the future
of both lines of work, as well as their intersection, and sketch a broader
vision for how data science can serve computational social science and vice
versa.

*Bio*: Aaron Schein <https://www.aaronschein.com/> is a postdoctoral fellow
in the Data Science Institute at Columbia University, where he is
co-advised by David Blei and Donald Green. His research develops machine
learning and data science methods for computational social science. His
recent work uses large-scale digital field experiments to assess the causal
effects of friend-to-friend mobilization on voter turnout in US elections.
Aaron did his PhD at UMass Amherst in Computer Science, where he was
advised by Hanna Wallach. His doctoral research developed new hierarchical
Bayesian models, tensor decomposition methods, and dynamical systems for
analyzing massive data sets of country-to-country events. Aaron has
interned at Google and Microsoft Research and worked in policy at the MITRE
Corporation. He is also currently a senior technical advisor at Ocurate and
a research affiliate at PredictWise. Prior to doing his PhD, he received a
BA in Political Science and an MA in Computational Linguistics from UMass
Amherst. He is on Twitter @AaronSchein.

*Host*: Victor Veitch

*Zoom Info:*
https://uchicago.zoom.us/j/97699156050?pwd=MHlVU2twNUJtbzdwTTJmS3d1TWp3dz09
ID: 976 9915 6050
Password: ds2022


-- 
*Rob Mitchum*

*Associate Director of Communications for Data Science and Computing*
*University of Chicago*
*rmitchum at uchicago.edu <rmitchum at ci.uchicago.edu>*
*773-484-9890*
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