I am an ABD graduate student of Political Science and Scientific Computing, with a dual degree in Statistics, at the University of Michigan, Ann Arbor. Previous to my Ph.D. in Political Science I studied mathematics and computer science at the School of Mathematics and Computer Science (ICMC) at the University of Sao Paulo (USP) and social sciences at the School of Philosophy, Literature and Human Sciences (FFLCH) at the same university.

My research interests lie on the intersection of political methodology and comparative political economy of distributive politics. On the methods side, I am interested in developing empirical (statistical) and formal (analytical) models to study the politics of redistribution. I understand empirical modeling as the application of mathematical statistics to develop theory-oriented probabilistic models intended to account for the underlying process that generates the data. I am particularly interested in semi-parametric density estimation, latent variable estimation, models for dependent data, and modeling effect heterogeneity using stochastic processes.

My substantive research agenda includes comparative analyses of preferences for redistribution as well as the relationship between economic development, inequality, and political behavior. I have developed and used models that estimate latent subpopulations with heterogeneous attitudes toward welfare and redistributive policies in OECD countries. I have also worked with models to study the effect of electoral competition and legislative bargain on adoption of redistributive programs in multiparty systems.

(*) Note: this sentence was inspired in Samuel Butler‘s words: “Life is the art of drawing sufficient conclusions from insufficient premises”. I believe we can always broaden our premisses to make them sufficient for our conclusions. We can’t do the same with data.