I am a third year graduate student in the Biomathematics Ph.D. program at North Carolina State University. I am a Genetics and Genomics Scholar, as well as a recipient of the National Science Foundation's Graduate Research Fellowship. I received a B.S. in Mathematics with highest honors from the University of Maine in 2016 (my undergraduate thesis can be found here). I grew up in Auburn, ME where I developed a fascination for infectious diseases and mathematics, although I spent the first years at my undergraduate institution studying to become a civil engineer! Eventually, I committed myself to a degree in mathematics after my REU experience at the Mathematical and Theoretical Biology Institute at Arizona State University, and a grant project I worked on with Prof. David Hiebeler. This is also around the time when I became interested in programming to assist in some of my computational-oriented work, developing advanced knowledge in R, MATLAB, and NetLogo, to name a few.
This website is a mishmash of some of the things I've been up to, including past and current projects, presentations, etc. I will also occasionally make a blog post every about some of the things I find interesting. These topics tend to be more general and don't always have to do with epidemiology. Also, for some reason or another, the pages may look a little funky if you have an AdBlocker enabled.
Broadly speaking, my research interests are in the mathematical modeling of biological systems, specifically those concerning the spread and control of infectious diseases. As an undergraduate, my formal projects consisted of researching metapopulation analogues of superspreaders, comparing control strategies for the nosocomial transmission of MRSA, applications of topological concepts to aid in the diagnosis of breast cancer, developing a mathematical model of the opioid epidemic, some work modeling simple populations over heterogeneous landscapes, and many more.
As a graduate student, my interests have narrowed to mathematical models that utilize genetic information or that make use of genetic tools in order to control disease spread. Developments in our ability to study and analyze genetic information in recent decades have revolutionized the life sciences. In particular, genetics help shed light on many pertinent issues at the center of our struggle with infectious disease, from antibiotic resistance to zoonotic spillover. This knowledge also informs how we control disease, with gene drive technology being a relevant and promising example. These tools have already fundamentally changed the landscape of our fight with infectious disease. Mathematically, these innovations will change how we model disease spread and give rise to new systems benefiting from quantitative analysis.
It is for these reasons that when I enrolled at NCSU, I became a student in the Genetics and Genomics Initiative. This was in order to enhance my understanding of the genetic mechanisms behind infectious disease, as well as the tools and techniques employed by scientists to study them. I am currently a member of the Lloyd lab, where I do research on gene drive technology and its potential in mitigating disease burden. I am currently studying how density-dependence determines the effectiveness of gene drive performance in mosquito populations, and how this influences disease control efforts. I am also working with Prof. Peter Stechlinski from the University of Maine on a general mathematical model of the opioid epidemic.