Invited Talk: Machine Learning for Political Research at Facebook

Date and Location

May 12, 2017 - 2:00pm to 3:00pm
Bldg 434, rm 122

Speaker

Monica Lee
Facebook

Abstract

Conversations about data science and the tech industry tend to be overrun with buzzwords like ‘Big Data,’ ‘Machine Learning,’ ‘Algorithms,’ and ‘AI,’ but few outside technology companies have a clear idea of how this work is conducted and the goals behind building complex mathematical models. How do data scientists leverage machine learning to develop products and solve problems in the tech industry? Dr. Monica Lee, a Data Scientist, Computational Sociologist, and Software Engineer, will discuss various machine learning models that she has built to perform political/elections research at Facebook, including models for predicting political ideology, engagement, and influence, as well as for detecting fake accounts. Ample time will also be reserved to address audience questions about transitioning from a PhD or academic position to industry research, academic-industry collaboration, and other (non-politics) research areas.

Dr. Monica Lee received her PhD in Sociology from the University of Chicago. She is currently a Data Scientist, Computational Sociologist & Software Engineer at Facebook, where she leverages very big data to research political behavior, cultural tastes/beliefs, and social networks. Her work on cultural modeling has been published in journals like PLOS one, Poetics, Sociological Theory, and The American Journal of Cultural Sociology. And her algorithms have been covered by news outlets like the New York Times, Wired, and TechCrunch.