Research Interests
We develop general-purpose computational approaches that integrate large-scale heterogeneous public datasets that lead to the mechanistic understanding of microbial genotypes, phenotypes, and diseases.
Specifically, we focus on two key questions:
- How do we link microbial genotypes to phenotypic traits?
We use a combination of protein sequence-structure-function relationships, comparative genomics, and machine learning to bridge the genotype-phenotype gap (e.g., phenotypes, antimicrobial resistance, host-specificity, microbial pathogenesis).
- How do we delineate molecular mechanisms underlying host response to infection and discover host-directed therapeutics?
We use comparative transcriptomics, disease-drug signatures, and machine learning to learn about host response and drug repurposing.
Our methods are generally pathogen- and disease-agnostic. We also release open data/software and easy-to-use web applications for wide use by the biomedical community.
I am also actively engaged in training, education, and outreach, and committed to creating and sustaining a diverse and inclusive ecosystem in data science and R for learners and professionals alike, focusing on increasing the participation of underrepresented minorities in data science and R programming. Towards this effort, I founded R-Ladies East Lansing and R-Ladies Aurora, and co-founded Women+ Data Science and AsiaR. I also co-chair the R/Bioconductor Community Advisory Board.