I am a Ph.D. Candidate in Sociology at the University of California, Berkeley. I hold Master's Degrees in Sociology and Quantitative Methods in the Social Sciences from UC Berkeley and Columbia University respectively. I research the intersection of work, culture, and inequality using computational and qualitative text analysis, as well as quantitative methods. My dissertation focuses on uncovering the criteria workers use to evaluate their job quality, and analyzes the extent to which these criteria are dependent on working conditions, inequality, and social movements. The dissertation makes use of a broad range of data sets, including Glassdoor, Google Trends, The Shift Project, and an original survey experiment. Specific topics explore how job satisfaction, as well as other outcomes, are associated with differences in how frontline workers discuss their customers, inequality in wages and schedule characteristics within the firm, and the popularity of different social movements.

My primary specialties are in work, culture, and computational methods. I have additional research interests in economic sociology, organizations, stratification, and social networks. In my research, I have employed a variety of techniques, including computational and qualitative text analysis, machine learning, web scraping, geolocation and geographic regression, regression decomposition, and causal analyses such as difference-in-differences, and survey experimentation. My work has appeared in The American Journal of Sociology, American Sociological Review, and Socio-Economic Review. My research has been featured in The New York Times, Politico, Bloomberg, and CNN.