Education

Research Interests

My research interests lie at the intersection of statistical genomics, machine learning, and functional neurobiology, with a primary focus on elucidating the complex genetic architecture underlying psychiatric disorders, particularly Schizophrenia. I am deeply fascinated by how human genetic variation shapes disease liability, and I am eager to explore the full spectrum of this architecture by investigating both the cumulative effects of common, low-effect-size variants and the profound impacts of rare, high-penetrance mutations. While Genome-Wide Association Studies (GWAS) have been monumental in mapping hundreds of risk loci associated with schizophrenia, translating these statistical signals into actionable biological insights remains a critical bottleneck. Consequently, I am highly motivated to focus on post-GWAS functional genomics to bridge this gap, specifically by investigating the vast and largely uncharted non-coding genome where the majority of risk variants reside. I aim to explore how these non-coding variants disrupt cis-regulatory elements, enhancers, promoters, and non-coding RNAs, thereby altering spatial and temporal gene expression profiles in disease-relevant brain tissue. To achieve this, I am interested in leveraging integrative multi-omic approaches—combining large-scale GWAS datasets with transcriptomic, epigenomic, and single-cell sequencing data—to map risk variants to their functional regulatory networks within specific neural cell types.

Parallel to functional validation, I am deeply interested in redefining how we capture psychiatric phenotypes at scale by integrating machine learning approaches with electronic health record (EHR) data in biobanks. Traditional case-control paradigms often fail to capture the biological reality of psychiatric conditions like schizophrenia, which exist as a heterogeneous spectrum rather than a binary state. By applying machine learning frameworks to high-dimensional EHR data, longitudinal clinical notes, and billing codes, I aim to move away from rigid categorical diagnoses and instead derive continuous measures of disease liability. Modeling schizophrenia as a phenotypic spectrum or a probabilistic liability score better reflects its underlying polygenic nature and enhances statistical power in genetic analyses. Ultimately, my objective at VCU is to combine advanced computational phenotyping with functional genomics to contribute to a deeply mechanistic, biologically grounded understanding of schizophrenia, moving past mere genetic mapping toward the discovery of novel pathways and therapeutic targets.

Research Description

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