Nolan Siegel

Nolan Siegel uses machine learning, observational data analysis, and experiments to study American politics and political communication, focusing on crime-focused media reporting, public opinion, and vote choice.

Nolan is a joint Ph.D. student in the University of Pennsylvania’s Department of Political Science and Annenberg School for Communication. Additionally, he is pursuing a Master’s in Statistics & Data Science at the Wharton School.

Broadly, Nolan’s research focuses on crime and its apprehension in politics: the ways journalism covers crime, how people think about crime, and the extent to which crime influences elections.

Prior to coming to Penn, Nolan worked as data science government contractor – supporting U.S. Intelligence Community entities such as the Office of the Director of National Intelligence, Defense Intelligence Agency, and U.S. Army – and he has also worked directly for the Federal Bureau of Investigation and Defense Finance & Accounting Service.

He holds an M.A. in Political Science and a B.A. in Economics and Political Science, both from Vanderbilt University.