Brandon Kramer, Ph.D.

Social Scientist / Data Scientist

Brandon Kramer, Ph.D. is a Postdoctoral Research Associate in the Social and Decision Analytics Division at the University of Virginia’s Biocomplexity Institute. Brandon generally researches ethics and inequality at the intersection of science, technology, politics, and health. While formally trained as a sociologist, Brandon is interested in using aspects of data science and science & technology studies to address a range of social and policy concerns. His work has been published in the Annual Review of Nutrition, Science, Technology & Human Values, BioSocieties, and Annals of Behavioral Medicine. Brandon is currently looking for an Assistant Professor position in academia or for a research/data scientist position in goverment or tech.

Dissertation Research

In his dissertation project (Rutgers University, 2019), Brandon studied how testosterone has been used to racialize populations in biomedical research and the impact that this has on understanding health disparities. By integrating various computational and qualitative strategies, Brandon traced how testosterone researchers advance misleading and dangerous claims about racial differences in health and behavior, ultimately arguing that these discourses contribute to racially-specific healthcare practices that exacerbate prostate cancer disparities between white and black men. As part of this ongoing research, Brandon is developing a project that uses natural language processing, machine learning, and network analysis to study diversity in biomedical research more broadly.

Collaborative Research

In his present role at the UVA Biocomplexity Institute, Brandon spends most of his time working in a multidisciplinary team science environment. Since graduating, Brandon has worked on collaborative projects - both in and outside of UVA - that examine international collaboration networks in open-source software development, the political history of nutrition science, the rise of “diversity” in biomedical research, the use of predictive modeling to evaluate risk of gentrification and emerging employment areas, and a project investing how built infrastructure shapes economic mobility across three states. As a result, I have experience in network/graph analysis, natural language processing, regression, probabalistic record linkage, data visualization, geospatial mapping, survey design and implementation, as well as a number of qualitative strategies like content/discourse analysis and interviewing. You can find links to my past work in the projects tab or check out his coding on GitHub.