About

I'm a principal bioinformatics scientist with a product mindset. I build data pipelines, analytics workflows, and AI-powered tools that help research teams turn messy biological data into actionable insights.

My work sits at the intersection of computational biology, data engineering, and machine learning. I specialize in spatial transcriptomics, single-cell analysis, and building reproducible research infrastructure.

Background

With 5+ years of experience in biotech and pharma, I've worked across the stack—from designing wet lab experiments to deploying ML models in production. I believe the best bioinformatics is invisible: it just works, reproducibly, at scale.

Current Focus

• Spatial omics analysis and infrastructure
• LLM applications in scientific research
• Building data products that scientists actually use
• Making bioinformatics more reproducible

Tools & Technologies

Languages: Python, R, SQL, Bash
ML/AI: PyTorch, scikit-learn, LangChain, Hugging Face
Data: Pandas, Polars, Spark, DuckDB
Bioinfo: Scanpy, Squidpy, Seurat, Nextflow
Infrastructure: AWS, Docker, Kubernetes, Terraform