I use computational methods – principally text analysis, natural language processing, machine learning, and network analysis techniques – to study social movements, culture, gender, and organizations and institutions. Substantively, her research has examined processes around the formation of collective identities and social movement strategy in feminist and environmental movements, continuities between cycles of activism and the role of place in shaping social movement activity, intersectionality in women’s movements and in the lived experiences during the 19th century in the U.S. South, gender inequality in startups and entrepreneurship, the translation of academic ideas to practice in the National Science Foundation’s ADVANCE program (a program aimed at promoting women in STEM field in higher education), and gender inequality in emergency medicine departments. Methodologically, she has proposed frameworks to combine computational methods and machine learning with qualitative methods, including the computational grounded theory framework and leveraging the alignment between machine learning and the intersectionality research paradigm. She has developed and taught courses introducing social science and humanities students to computational methods and the scripting languages Python and R, data science courses, and graduate-level sociological theory. She is currently a co-PI on a million-dollar grant through the National Science Foundation to study the spread of gender-equity ideas related to STEM fields through higher education networks, primarily in the United States.