I am a statistician by training, and the biological phenomena captured in high-throughput genomic data motivate me to develop new statistical methodologies. The central aim of my research program is to build machine learning algorithms and statistical tools that aid in the understanding of how nonlinear interactions between genetic features affect the architecture of complex traits and contribute to disease etiology. A consistent theme of my work is to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles. As part of this proposal, I will provide a suite of computational methods that enhance our understanding about the regulatory mechanisms underlying both common and rare diseases.
Awards and Achievements
- The Root: 100 Most Influential African Americans ( 2019)
- Endowed Named Assistant Professorship, Brown University ( 2019)
- Alfred P. Sloan Research Fellowship ( 2019)
- Forbes 30 Under 30 ( 2019)