Hi, I’m Tyler! Welcome to my website.
I am a Ph.D. candidate in Political Science and Scientific Computing at the University of Michigan, where I am also completing an M.A. in Statistics. With a background in political science, social demography, comparative-historical analysis, and area studies, I study the relationship between political parties and the populations beneath them. Parties and party systems stand on alignments with particular population groups, and my research asks what happens to those alignments when the groups move, grow, shrink, and re-cluster across space and time, and what parties do to defend, adapt, or even engineer the populations that sustain them.
My dissertation traces these effects across the United States, Malaysia, Taiwan, and Japan, asking how political parties under different electoral systems manipulate and respond to demographic change. Because these questions outrun existing tools, I also build new ones. My flagship methodological contribution is the Bayesian Approximate Computation with Hierarchical framework (BACH), a simulation-based ecological-inference estimator that recovers subgroup voting behavior from aggregate election returns where conventional estimators break down. Supporting that work is a broader toolkit of spatial econometrics for electorates whose district boundaries move and locally run large-language-model pipelines for coding archival text.
My research appears in the Journal of East Asian Studies, and I work with primary sources in Chinese, Malay, and Indonesian. I have taught across Michigan’s quantitative methods sequence, from the graduate statistics core to undergraduate data science, as well as social demography, and have been a teaching assistant at the Inter-university Consortium for Political and Social Research (ICPSR) Summer Program.
I am on the 2026–27 academic job market.
Outside the academy, I volunteer as a cat foster with the Humane Society of Huron Valley.
