I’m an undergraduate researcher at Indiana University. My research interests lie at the intersection between politics and data science. This is reflected in my individualized major: Computational Approaches to Political Analysis. I believe the tools and information that we now have at our fingertips should be used to validate and inform theories in the social sciences. The dichotomy between ‘quant’ and ‘qual’ in research is false – domain experts should be using their qualitative knowledge to inform and contextualize quantitative findings, and qualitative theorists should back up their claims with empirical evidence.

Similarly, we must recognize the artificiality of the boundaries between academic disciplines. It is useful for me to identify myself as a ‘political scientist’ only to describe the core of my expertise and interests. However, limiting my research to the domain of political science would limit the contribution that I can make to the scientific community. The insights of a political scientist can be useful to the research of sociologists or psychologists, for example, and vice-versa. One of my major goals as a researcher is to acknowledge where my interests and expertise overlap with other fields, and contribute to science as a whole – not just political science.

This diversity of interest has led me to work on a variety of projects. My biggest current interest is modeling judicial bias. I am using machine learning techniques in order to better understand how and why biased outcomes occur in the U.S. judicial system. My work on the Asynchronous Remote Communities (ARC) method reflects my dedication to improving the tools with which we conduct research. The focus on social support systems for people living with HIV illustrates the intersection between an interest in political science and fields such as sociology and medicine. Check out my projects to see full details on this research and more.