Genome-wide association studies (GWAS) provide large amounts of data associating single nucleotide polymorphisms (SNPs) with disease phenotypes. However, these associations are noisy, and many of the associated SNPs lie in non-coding regions of the genome, making it difficult to identify the mechanisms giving rise to phenotypic changes. Naive heuristics such as associating a polymorphism with the nearest gene may be inaccurate. My work aims to integrate GWAS information with other publicly available datasets and apply probabilistic models to better infer the genes and regulatory elements affected by SNPs, thereby improving our understanding of the genetic mechanisms of disease.

I worked with Prof. Boyer on analyzing high-throughput sequencing data to study enhancers and lncRNAs in differentiating cardiac cells. I worked at MITs BioMicro Center on comparing various ChIP-seq algorithms to determine their efficacy and biological relevance. I worked at ICGEB in India on cloning a butanol-producing metabolic pathway from C. acetobutylicum into E. coli.