front_v2 » Page 1 of 4

I am an Assistant Professor in the math department at Georgetown. My work focuses on models describing the genetic evolution of populations. Mathematically, this translates to the analysis of complex stochastic systems or more generally applied probability. I am interested in modeling how the immune system interacts with an infecting viral population, especially in the case of HIV infection. In that setting, the analysis centers on a highly coupled interaction between immune systems cells such as T or B cells and a highly mutative viral population. Another area of research involves understanding the genetic evolution of subdivided populations. Many populations are divided into small units, and this division has an important impact on the population's genetic composition. My work has focused on how different subdivision geometries affect the genetic diversity of an evolving population.

I work in the fields of population genetics and ecology using mostly tools
from probability. I focus on understanding the genetics of evolving populations and, for those in the know, this takes the form of analyzing the coalescent process of different populations. I also work on analyzing statistics used in evolutionary inference. Typically, I consider a population, work real hard to understand the appropriate coalescent process, and then work real hard to understand the distribution of a certain statistic under that coalescent.

I have done a lot of work on subdivided populations. Here, the goal is to understand the coalescent process for different geometries of subdivision. A simple geometry is that known as the island model with a more subtle geometry being that of the stepping stone model. A well known statistic used in population subdivision inference is Fst. Everybody uses Fst, but nobody knows its sampling distribution. Recently I have worked on understanding the coalescent in a stepping stone model and deriving the sampling distribution of Fst.

Over the past year, I have become focused on understanding the evolution of an HIV population during infection. From a population genetics perspective, this involves understanding the interaction of two populations, the immune system cells and HIV, that are highly coupled. Everything is hard here because their is strong selection, strong mutation, and the entire interaction is not well understood. My focus has been on developing a sampling theory, read that as a coalescent process, for the predator-prey interaction of T cells and HIV.

In a previous life, for my PhD thesis at NYU, I was interested in mathematical physics. I worked on a problem involving the movement of a quantum particle through a stochastic field. Or, to put it another way, I thought about the state of a system that evolves stochastically. Not much has changed.