Ph.D., Statistics, University of Wisconsin–Madison
My research involves the development of statistical models to answer biological questions, balancing biological interpretability, theoretical guarantees, and computational tractability. In particular, my research deals with modern big data which are highly interconnected through graphical structures. Examples of my research involve the inference of phylogenetic networks to study reticulate evolution, comparative methods on networks to study the evolution of traits on hybrids, new sampling schemes to improve on Bayesian MCMC tools, as well as the application of such new tools to real-life datasets such as cultivated potato and carrot, Pseudomonas aeruginosa, Staphylococcus aureus, human endogenous retroviruses among others. Next-generation sequencing creates a big data reality that can make current methodologies prohibitive due to computational restrictions. My work produces a collection of new statistical methods with solid theoretical guarantees and efficient computational implementations that are adaptable to analyze the complex characteristics of modern [big] biological data.