Computational biology • Machine learning • Gene regulatory networks • Genetics of complex traits • Integrative models for combining different types of omics data

The production of diverse types of high-dimensional biological data has increased tremendously in the last decade, presenting novel opportunities to develop and apply computational and machine learning approaches to understand the genetics of human diseases. However, the high dimensionality of this data, whereby up to millions of diverse and heterogeneous “features” are measured in a single experiment, coupled with the prevalence of systematic confounding factors present significant challenges in disentangling bona fide associations that are informative of causal molecular events in disease. My research interest lies in designing tailored computational models for integrating multiple types of high-dimensional “omics” data, with the ultimate goal of disentangling meaningful molecular correlations for common diseases such as psychiatric disorders.