Welcome!

I am the Bingham Postdoctoral Teacher-Scholar at Transylvania University, where I teach in the Political Science program and the Data Analytics program. My work there spans teaching, program development and assessment, and quantitative methods training across the university.

Research

My research examines how politically meaningful group identities form among LGBTQ+ Americans, when those identities shape political behavior, and how researchers can measure those processes credibly in populations that are small, stigmatized, and difficult to sample. I treat the substantive and methodological sides of that agenda as one problem: studying identity as a process requires measures built around how members of a group understand themselves, and models that preserve variation conventional surveys obscure.

I work with survey research, psychometrics, Bayesian multilevel modeling, experimental design, machine learning, and qualitative interviewing.

Current projects include a dual-pathway model of how internalized stigma and identity affirmation shape LGBTQ+ people’s sense of linked fate; a study on the ideology-partisanship link in sexual minorities; Bayesian methods for inference in small populations whose composition changes over time; and small-area estimation of latent political identity for institutional analysis.

Teaching

Teaching is what I most want to be good at. I have taught substantive and methodological courses in person, online synchronous, and online asynchronous, and I lead quantitative methods workshops across disciplines, including R and Python for judicial research, causal inference in econometrics, baseball analytics, survey and interview design, and R for epidemiological research.

Working with students

I supervise undergraduate research and look for projects with a genuine theoretical question, multiple methodological entry points, and components students can carry from design through interpretation. Current student projects examine whether “queer” and “LGBT” function as politically distinct identities, and how machine learning systems represent gender.