Doctoral Candidate
About
Tyler is a Ph.D. candidate in Political Science and Scientific Computing at the University of Michigan, where he is also completing an M.A. in Statistics. He studies the relationship between political parties and the populations beneath them. Parties and party systems stand on alignments with particular population groups, and his 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.
His dissertation works the population side of this relationship, tracing how the changing geographic distribution of critical population groups creates scale effects on parties and party systems in the United States, Malaysia, Taiwan, and Japan. A second strand works the party side: how dominant parties hold their alignments together through state-directed settlement, mobilizational campaigns, and the management of their own elites, and how they fragment when those efforts fail.
On the voter side, his article in the Journal of East Asian Studies shows that Malaysia's celebrated urban–rural divide is largely an artifact of aggregation, leaving ethnicity the dominant axis of competition, and a working paper traces how urbanization dismantled the ruling coalition's patronage machine settlement by settlement, decades before its formal defeat; a dissertation component in development follows Latino voters in the United States as they move and cluster, reshaping the bundle of ethnic identity and partisanship. On the elite side, he shows that elites defect from dominant parties when they lose internal contests, evidenced by career panels he built covering every candidate of Malaysia's, Japan's, and Taiwan's long-dominant parties, and that regime elites coordinate collective abdication through public signals, modeled as a global game and tested on an original day-by-day dataset from Indonesia's 1998 transition.
Because these questions outrun existing tools, Tyler builds new ones. His flagship methodological contribution, the Bayesian Approximate Computation with Hierarchical framework (BACH), recovers subgroup voting behavior from aggregate election returns when survey nonresponse, partisan polarization, and within-group cleavages defeat existing estimators: revealing, among other things, that national origin rather than education organizes Hispanic vote choice. He has taught across Michigan's quantitative methods sequence, from the graduate statistics core to undergraduate data science, and at the Inter-university Consortium for Political and Social Research Summer Program, and he works with primary sources in Chinese, Malay, and Indonesian. The data he builds include election panels, candidate career archives, geocoded settlement layers, constructed for public release.
He also volunteers as a cat foster to the Human Society of Huron Valley.
