Senior Preceptor in Computational Social Science

1155 East 60th Street, Room 215A
(773) 834-4905

Joshua is a PhD candidate in the Department of Sociology at the University of Chicago. He holds a BA, summa cum laude, from New York University, received his MA at the University of Chicago, and was a summer fellow at Data Science for Social Good. Joshua’s general interests intersect at the boundaries of sociology, political science, and economics, which he explores using computational and statistical methods. He is broadly interested in social stratification and inequality, labor markets, corporate boards and organizations, political ideology and polarization, political and media discourse, and social movements. His particular research interests straddle the intersection of these subjects.

For his dissertation, Joshua considers several interrelated topics relative to his general interests. First, Joshua examines how individual political affiliations shape career trajectories of elites including entry into elite professions and the replacement of corporate board vacancies. Second, he explores how corporate interlocks and networks, affect firm political polarization, unity, and political contributions by corporations and executives to federal election campaigns. Lastly, he evaluates two perspectives to better understand how social movements that challenge inequality affect political and corporate response. The first perspective elucidates the impact of Occupy Wall Street in altering political and media discourse on inequality, the results of which are published in Social Science Research. The second perspective explores how changes in political discourse, particularly by President Obama, affected donations by corporate executives in his 2012 reelection campaign.

Besides the central focus of his dissertation, Joshua has pursued a number of side projects that may relate to students’ interests, including exploring life-course wealth-accumulation, executive compensation, and a number of public policy oriented projects that use machine learning to predict outcomes such as adverse police incidents, high-school dropout, or HIV occurrence. Additionally, Joshua has explored health metrics in relation to crime, college sexual activity relative to campus environment, and energy usage in the city of Chicago.

Methodologically, Joshua has experience with machine learning algorithms, natural language processing, web scraping, and databases using Python, R, and SQL. Statistically, he has experience with a variety of models including time-series, multilevel models, linear, ordinal, and multinomial models, and multiple-imputation, using primarily R or Stata. He enjoys open-source and reproducible research with Git, RMarkdown, and LaTex, and is most comfortable on a Mac or Linux machine.

Further information about Joshua can be found on his website.