background I am an evolutionary biologist and infectious disease epidemiologist with a focus on using genomic data to better understand the dynamics of human pathogens. Broadly speaking, I am interested in identifying novel data signatures in pathogen genomic data and developing quantitative analytical methods to generate epidemiological and evolutionary insights from those signals. My work spans biological scales, from within-host pathogen evolution to pathogen dynamics on the host population scale. I have a particular interest in analyses that link these scales, e.g. through the transmission bottleneck.

I am currently a post-doctoral research fellow in Dr. M. Kate Grabowski’s lab and a part of the Infectious Disease Dynamics group at the Johns Hopkins Bloomberg School of Public Health. My work is funded in part by the PANGEA-HIV consortium and is largely done in collaboration with the Rakai Health Sciences Program. I am focused on a number of HIV-related research projects including the evolution and transmission of HIV antiretroviral therapy (ART) resistance, long-term evolutionary impacts of ART scale-up, and the identification of HIV mixed infections.

I completed my doctoral training in Dr. Katia Koelle’s group in the Population Biology, Ecology, and Evolution graduate program. My work there was funded in part by an NIH NIAID F31 fellowship to study the evolution of influenza defective viral genomes within- and between-hosts. I also spent significant time researching SARS-CoV-2 during my doctoral training including estimating the transmission bottleneck and phylodynamic epidemiological modeling.

Prior to my doctoral training I earned a master’s in epidemiology at the Harvard T.H. Chan School of Public Health where I did research in the Center for Communicable Disease Dynamics with Dr. Bill Hanage. Here I mainly focused on the within-host genomic diversity of M. tuberculosis.

News

2025

  • Our work on HIV multiple infections in the Rakai Community Cohort Study is now published in PLOS Pathogens. The main results are similar to the pre-print (see below), however, we now incorporate Bayesian post-stratification to account for potential sampling biases in our sequence data. We therefore are able to estimate that ~4% of viremic PLHIV in the population (as opposed to ~6% in the sample) harbor MIs at the time of sampling. Further, we incorporated direct comparison to what I call “synthetic amplicon” data to show that using whole-genome data increases sensitivity of the method more than two-fold. PDF.

    2024

  • Our preprint on HIV multiple infections in the Rakai Community Cohort Study is now available. We develop a Bayesian model to identify MIs in whole genome deep-sequence data. We use the model to show that ~6% of viremic PLHIV harbor MIs at time of sampling and that the risk of MI is ~two-fold higher in high prevalence fishing communities. PDF.
  • We have considerably revised our work on the dynamics of HIV drug resistance in Rakai, Uganda in response to reviewer comments. Hopefully the revised version is more digestible for readers. PDF.
  • Our work on the within- and between-host dynamics of influenza A virus defective viral genomes is now out in Virus Evolution [PDF]. This work was (mostly) completed during my doctoral research in Dr. Katia Koelle’s group.
  • I finally found the motivation to make a professional website. Thank you Clif McKee for the nudge.