- Research Program Mentor
PhD candidate at Johns Hopkins University
genomics, data science, machine learning, reproducible research, coding in R/bash/python
BioI am a Computer Science: Computational Genomics PhD student in Dr. Alexis Battle's lab at Johns Hopkins University. My research is centered on developing analytics and modeling techniques targeting rare variation in the human genome. I am specifically interested in the mathematical/biological assumptions and constraints that must be adapted to optimize the use of data from Under Represented Groups (URG). Prior to my time at JHU, I worked as a data scientist in industry applying machine learning techniques across a variety of subject matter domains, including precision health, fraud detection, and computer vision. I have a B.A. in Mathematics from Goucher College. I was born to be a scientist and a teacher. The only thing better than learning something new is helping someone else do it. Outside of lab I enjoy reading Jane Austen, playing strategy board games (e.g. Codenames, Root, Coup), watching Star Trek: Next Generation, and Bikram Yoga.
Opioid Epidemic, Meet Covid-19 Pandemic
Prior to the covid-19 global pandemic, the US was already struggling with the public health crisis of opioid addiction. Overdose events increased during the pandemic. Close analysis of publicly available datasets might provide some important insights into the interactions of these two catastrophes.
Diversity on the African Continent
There is an incredible amount of genetic diversity within populations of African descent. In fact, the distinctions between African subpopulations are so pronounced that they often need to be treated separately during analysis. I propose a potential project focused on fleshing out the genomic distinctions between some of these populations.