Senior Data Scientist - Bioinformatics | Computational Biology | Translational Omics | Reproducible AI/ML Workflows
I am a bioinformatics data scientist with 4+ years of experience building reproducible NGS pipelines, multi-omics analyses, and machine-learning workflows for translational research. My work focuses on turning complex genomic, transcriptomic, and epigenomic data into decision-ready biological insight for target discovery, biomarker exploration, and therapeutic strategy.
I work across bulk and single-cell RNA-seq, CRISPR Perturb-seq, ATAC-seq, ChIP-seq, eCLIP-seq, PRO-seq, long-read sequencing, Hi-C/HiChIP, public data mining, AWS/HPC infrastructure, Nextflow, Docker, R/Bioconductor, Python, and applied AI/ML.
| Area | What I Bring |
|---|---|
| Translational bioinformatics | Target discovery, biomarker analysis, disease-context interpretation |
| Reproducible omics | Nextflow, Docker, Conda, AWS, HPC, GitHub-ready workflows |
| Regulatory genomics | Enhancer-gene links, regulatory RNAs, ASO target prioritization |
| Public data strategy | GEO, SRA, GTEx, TCGA, dbGaP, UK Biobank, ClinVar, GWAS Catalog, ENCODE |
| AI/ML for biology | Feature engineering, classification, clustering, neural networks, biomedical imaging |
| Project | Summary |
|---|---|
| FibroTarget-Liver | Built a reproducible single-cell liver fibrosis workflow to prioritize disease-relevant targets across public MASH/cirrhosis datasets, delivering ranked candidates, validation views, an executive report, Shiny dashboard, and Nextflow demo. |
| Mishra IL10 ATAC-seq Analysis | Reanalyzed public IL10 ATAC-seq data to interpret chromatin-accessibility changes with reproducible reporting, connecting epigenomic signal to practical biological interpretation. |
- Senior Data Scientist - Bioinformatics experience in startup translational research.
- M.S. in Bioinformatics, Northeastern University.
- MIT Professional Education: Applied Data Science - Leveraging AI for Effective Decision-Making.
- Presented Enhancer Identification Using Machine Learning Enables Efficient eRNA Targeting With ASO-Based Therapeutics at Nature Conferences, Boston, 2023.
- Presented Accurate Prediction of Functional Enhancer-Promoter Interactions Using Epigenomic Data at Cold Spring Harbor Laboratory, 2022.
I am building Bioinformatics Field Guide, a public resource for practical bioinformatics methods, reproducible analysis patterns, notebooks, and concise field guides for learners and working scientists.