About Me
Welcome! I am a political scientist who uses computational methods to study big questions about the dynamics of organizational and technological change. I am passionate about digital transformations and leveraging the tools of machine learning and computational techniques. I am currently Postdoctoral Researcher at Duke University. Previously, I was a Postdoctoral Research Fellow at the University of North Carolina at Chapel Hill.
I am excited by the possibilities (and challenges) that data science has opened for social scientists. To that end, several of my projects adapt quantitative and computational methods for nontraditional data in political science, particularly applied Bayesian statistics, network analysis, and text-as-data. Current projects include solo and co-authored work using texts and networks to measure organizational transformations and evolution, as well as a project using Bayesian Item Response Theory to create a tool that allows researchers to produce theoretically-informed measurements of latent attributes.
My work, and work that I have contributed to, has appeared in venues such as International Studies Quarterly and Political Science Research and Methods. I have been supported by grants, including from the National Science Foundation and the Lynde and Harry Bradley Foundation. I have served on the Program Committee for the Political Networks and Politics and Computational Social Science (PaCSS) conferences, and was Communications Chair for the Political Networks Section of APSA from 2020-2023.
I received my Ph.D. from the Department of Political Science at Duke University in May 2020, with primary fields in Security, Peace, and Conflict and quantitative methods. Before starting my academic career, I tracked and analyzed jihadi and far-right propaganda as a senior analyst at the SITE Intelligence Group. The analysis I produced there was widely cited by both media organizations and policymakers. This experience has shaped my approach to developing a theoretically-driven research agenda that is strongly informed by primary sources, tested quantitatively, and which builds from a variety of sources of data.
I am excited by the possibilities (and challenges) that data science has opened for social scientists. To that end, several of my projects adapt quantitative and computational methods for nontraditional data in political science, particularly applied Bayesian statistics, network analysis, and text-as-data. Current projects include solo and co-authored work using texts and networks to measure organizational transformations and evolution, as well as a project using Bayesian Item Response Theory to create a tool that allows researchers to produce theoretically-informed measurements of latent attributes.
My work, and work that I have contributed to, has appeared in venues such as International Studies Quarterly and Political Science Research and Methods. I have been supported by grants, including from the National Science Foundation and the Lynde and Harry Bradley Foundation. I have served on the Program Committee for the Political Networks and Politics and Computational Social Science (PaCSS) conferences, and was Communications Chair for the Political Networks Section of APSA from 2020-2023.
I received my Ph.D. from the Department of Political Science at Duke University in May 2020, with primary fields in Security, Peace, and Conflict and quantitative methods. Before starting my academic career, I tracked and analyzed jihadi and far-right propaganda as a senior analyst at the SITE Intelligence Group. The analysis I produced there was widely cited by both media organizations and policymakers. This experience has shaped my approach to developing a theoretically-driven research agenda that is strongly informed by primary sources, tested quantitatively, and which builds from a variety of sources of data.