I graduated in 2014 with a PhD in Biomedical Informatics from ASU. My doctoral work involved developing a new algorithm for structural variant detection in the cancer genome, since the conventional variant detection tools were doing a poor job at variant detection in the cancer genome. I also have a MS in Epidemiology from University of Arizona. Currently, in my post-doctoral research, I am applying my epidemiology and informatics training in understanding the regulatory elements of cancer genome using single nucleotide polymorphism (SNPs) and gene expression data(eQTL-expression quantitative trait loci). The analysis involves running 20 billion linear regression runs on 20,000 gene expression and the corresponding one million SNP calls simultaneously. This process called for some innovative application of parallel computing using the available popular bioinformatics tools. The other part of the analysis involves connecting all this information about the genes and SNPs in a more meaningful way. I am working on machine learning and/or network analysis methodologies to derive information from this data which can help better understand cancer etiology. Traditionally, studying the regulatory elements in the genome has been a laboratory science using mousse models and gene editing. The availability of high computing infrastructure can help understand this complex network of genome regulation and therefore dysregulation in cancer through sophisticated algorithms. These algorithms have been used to understand social networks and I am trying to apply these algorithms in the genomic field.
Arizona State University