•Automated and improved transcriptomic analysis pipelines in R for GSEA, differential gene expression, meta-analysis, and connecting diseases signatures to drug profiles. Prototyped innovative network biology and AI methods for multi-omics analysis and made them production ready in Python.•Extensive use of R for transcriptomic data processing, manipulation, and visualization to identify novel therapeutic targets.• Developed multiple analysis pipelines for drug discovery using R and Python, including TF network construction method from bulk data, pathway activity scores utilizing knowledge graphs, and implementing GNN to predict unseen CRISPR perturbations.

Computational Systems Biology focusing on transcriptional regulatory networks. Developed novel tools to infer transcriptional regulatory networks from bulk and single-cell RNA-seq. Used mathematical modeling to simulate networks and identify key transcription factors.•Developed pipeline in R and Python to analyze single-cell RNA-seq datasets and identify transcription factors targets critical to differentiation with frequent use of HPC, current bioinformatic tools, ODE-based mathematical modeling, and machine learning•Developed method in R to construct gene regulatory networks from RNA-seq data based on inferred activity of transcription factors. Analyzed bulk data and identified TFs controlling macrophage activation and EMT. •Developed method in R to infer important regulatory interactions from single-cell RNA-seq data through novel statistical comparison of experimental and simulated data, requiring frequent use of HPC, parallelization and bioinformatic tools for data processing.

Laboratory of Beth L. Dumont. Research on recombination through an evolutionary lens in mice. Assisted with optimization, troubleshooting, and designing of experiments as well as writing papers.