Background
I received a BS in Molecular Biology from The College of William & Mary in 2020. Mid-way through, I taught public health classes in Kenya and was drawn towards a career in global health.
I merged my interests by pursuing a PhD in Molecular Epidemiology at the University of Maryland School of Medicine. In 2023, I received an MS in Epidemiology & Clinical Research as an NIH/NCATS TL1 fellow. I am currently an NIH/NIAID F31 fellow with plans to defend my dissertation by Summer 2025.
[Curriculum Vitae]
Research interests
I am interested in the distribution, determinants, and dynamics of infectious diseases. I like to study who exactly is getting sick - and what exactly is making them sick - in vulnerable populations.
Epidemiological data help us understand the demographic and behavioral axes of disease. Biomarkers offer a window into the hidden layers of host and pathogen complexity. I believe that we need both for a nuanced understanding of infectious diseases.
PhD Candidate / Molecular Epidemiology / University of Maryland School of Medicine / 2025
MS / Epidemiology & Clinical Research / University of Maryland School of Medicine / 2023
BS / Biology / College of William & Mary / 2020
Biostatistics
Statistical programming (R, Python, SAS)
High-performance computing (UNIX)
Genomics & bioinformatics
Big data
Machine learning
Database management (SQL)
Epidemiological studies & RWE
Clinical & translational research




Skills
I enjoy challenging data science problems and am experienced in biostatistics and statistical programming (R, Python, SAS). I also have experience in bioinformatics (UNIX) and big data, including genomics, transcriptomics, and serological profiling.
As most of my projects involve data collected from clinical and epidemiological studies, I am well-versed in the design, analysis, strengths, and limitations of these research contexts.
Other interests
Beyond science, I enjoy long walks in search of a great coffee or taco, discovering new music, watching/playing sports, cooking, and sci-fi/fantasy.