From chromatin biology at Duke to drug discovery in biotech — here's a full account of the work and the tools behind it.
[Add your role description here — 2–3 sentences on the team's mission, your charter, and the biological problems you're working on at Vividion. What does winning look like for your work?]
Investigated chromatin-mediated mechanisms of gene regulation with a focus on transcription. Developed computational methods to decode the regulatory information encoded in chromatin accessibility and nucleosome positioning across the genome. Co-advised by David MacAlpine and Alex Hartemink.
[Add a prior role — internship, rotation, undergrad research, or industry position. What was the scientific question? What did you build or discover?]
| Skill area | Details | Proficiency |
|---|---|---|
| Python | Primary language for data analysis, ML modeling, and pipeline development. Daily use for 8+ years. | |
| R / Bioconductor | Statistical analysis, differential expression, ggplot2 visualization, DESeq2, limma, and custom packages. | |
| ATAC-seq / ChIP-seq | End-to-end analysis from raw reads: alignment, peak calling, footprinting, differential accessibility. | |
| Machine learning | PyTorch, scikit-learn, sequence models, random forests, Bayesian inference. Applied to genomics problems. | |
| Workflow management | Snakemake and Nextflow for reproducible, scalable bioinformatics pipelines on HPC and cloud. | |
| Cloud / HPC | AWS (S3, EC2, Batch), SLURM-based HPC clusters, Docker/Singularity for containerized workflows. | |
| Data visualization | ggplot2, matplotlib, seaborn, Plotly, D3.js. Publication figures, interactive dashboards, and web viz. | |
| Single-cell analysis | Seurat, Scanpy, cell type annotation, trajectory analysis, multi-modal integration (RNA + ATAC). |