LinkedIn · Public repositories · Flagship project
I design governed, reusable data systems for AI-enabled biotechnology. My work sits at the intersection of data strategy, scientific computing, data governance, and practical AI/ML enablement.
| Data strategy and governance | Scientific data systems |
|---|---|
| Data products, metadata, ownership, lineage, quality, privacy by design | Genomics, single-cell, spatial, imaging, clinical, and chemical data |
| AI/ML enablement | Engineering |
| Model-ready datasets, retrieval systems, evaluation assets, agent workflows | Python, SQL, APIs, workflow automation, cloud-native tooling |
| Project | Purpose | Stack | Status |
|---|---|---|---|
| Advanced Biomedical Agent | Integrates major public biomedical sources into PostgreSQL and exposes LLM-friendly search tools for drugs, targets, trials, safety, and regulatory data. | Python · PostgreSQL · Biomedical data · AI agents |
Active |
AI-ready data products · data catalogues · metadata automation · single-cell · spatial transcriptomics · biomedical search · agentic tooling
- Treat important datasets as owned, versioned, documented products.
- Co-version data, code, schemas, quality evidence, and provenance.
- Automate repeatable metadata and validation work; keep human accountability explicit.
- Prefer small, testable systems over impressive but fragile demos.
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