Bioinformatics Scientist with 10+ years of experience in research and analysis of genomics data. I am interested in developing computational approaches to glean actionable insights from large-scale multi-omics data. I am currently working on building machine learning models to guide biomarker discovery and assist in clinical decision making. I have extensive experience in written and oral communication to academics and business leaders through contributions to winning grants, 12+ peer reviewed publications, and several conference appearances.

Mining multi-omics for disease-gene and drug discovery

Innovations in high-throughput DNA and RNA sequencing, coupled with increasingly accessible compute capacity, has put many areas in medicine on the brink of data-driven transformation. My goal is to develop computational models that transform genomic data into actionable insights. My research philosphy is based on the fact that genetic elements (coding and non-coding parts of DNA) do not function in isolation but in groups connected as networks of genes and their regulators. My research explores how these gene networks change between tissues, cell types, and between health and disease. We have recently shown that the network rewiring between brain cells of healthy individuals and those diagnosed with Alzheimer’s can lead to identification of drugs that can potentially be used a therapeutic agents of the disease Read more….

Multimodal data integration With quickly evolving technologies that allow genomic, imaging and behavioural data acquisition and storage, I am enthusiastic about the potential of data-driven approaches to effectively integrate such data into models that aid clinicians, doctors, pharmaceutical companies and healthcare providers in better decision making.

I am currently working on a number of exciting projects in genomics of neurological, psychiatric, and developmental disorders.

My previous projects include 1) Transcriptome-based gene networks for systems-level analysis of gene function, 2) Optimization of pipelines to predict high-quality SNVs and InDels from tumor sequencing data, and 3) Methods to integrate GWAS with coexpression networks. Read more….