I fell in love with stochastic processes when I was introduced to this topic in Steve Krone
's class at the University of Idaho, where I was working on my MS in Mathematics. During the same time Paul Joyce
showed me the beauty of mathematical statistics. A bit later I worked with Paul and his evolutionary biology collaborator Jack Sullivan
on a phylogenetics project, which was my first introduction to statistical inference for stochastic processes. Later at UCLA I was lucky to have worked with my PhD advisor Marc Suchard
, who shares my love of statistics and stochastic modeling. My early research was in statistical phylogenetics and I remain active in this area. At UCLA, Marc showed me how phylogenetics can be applied to interesting problems in infectious disease epidemiology. Later, during my first faculty job at the University of Washington, I became interested in infectious disease epidemiology more broadly and now my main research interests are in data integration for Bayesian inference of transmission model parameters, nowcasting, and forecasting. Betz Halloran
was instrumental during this stage of my research interests evolution, because she recruited me to teach Markov chain Monte Carlo in the Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID) and encouraged Jon Wakefield
and me to collaborate and to participate in the Center for Inference and Dynamics of Infectious Diseases (CIDID) . During my first sabbatical, which I spent at the Fred Hutchinson Cancer Research Center, Erick Matsen
got me involved in computational immunology and since then we have been working on and off on statistical methods to analyze high throughput sequence data of B-cell receptors. At the University of Washington and then here at UC Irvine, I worked a bit on fitting mechanistic models of hematopoiesis to various types of experimental data. Mathematically and statistically, these systems biology applications are not too far from my infectious disease projects.