This project aims to better understand modifications of histone proteins, which are essential in regulating gene expression. Sequencing methods exist to study this process, and determining the covariance structures of the data produced (essentially, correlations between sequencing output from different cells) could provide vital information about the patterns behind histone modifications. First, using epigenomic sequence data, covariance structures will be built across several cell types and hundreds of individuals. Machine learning models will then be used to predict covariance structures more accurately, thus creating a versatile tool to study disease and gene expression. Finally, this project will narrow focus to studying histone modifications in autoimmune diseases.

I see SuperUROP as an exciting next step in my research career. Conducting research since my first semester at MIT has imparted me with an appetite to explore more, and I am very excited about the potential implications of my project. I also believe SuperUROP’ s emphasis on communication through papers, posters, and presentations will prove highly beneficial and allow me to hone several undoubtedly essential skills.