My research interests focus on incorporating concepts from evolution and ecology to understand how human health and disease is shaped by the microorganisms with which we share our environment. I'm particularly interested in integrating analysis of host, microbe and environmental factors in order to understand multifactorial diseases for which single causal agents are lacking or poorly understood (such as dental caries, gingivitis, preterm birth and metabolic syndromes). Through my participation in two training fellowships, the Genome Science Training Program and the Integrated Training Program in Microbial Systems, I use methods in mathematical modeling and population sciences to analyze high-dimensional and compositional data. 

Math Modeling: I study microbial systems using genomic techniques, including 16S rRNA amplicon based sequencing and metagenomic methods. The data produced by these methods is high-dimensional, sparse, and compositional.  I attempt to reduce the dimensionality of this data while retaining complexity using techniques from mathematics and data science such as weighted network analysis and latent class analysis. As I continue in my PhD I am interested in translating these techniques to time-series analyses, potentially involving dynamic mode decomposition or dynamic Bayesian networks.

Population Science: As an epidemiologist, I work with large cohorts of individuals to draw inference on causal mechanisms in populations. I am particularly involved with Center for Oral Health Research in Appalachia (COHRA) study, investigating microbial agents in oral disease within a large, longitudinal cohort of American children. 

Laboratory Approach: I use techniques from molecular epidemiology to examine the composition, spatial arrangement and functions of microbial communities associated with human health and disease.  Examples of techniques include qPCR, 16S rRNA amplicon sequencing and shotgun genomics.