• current flagship projects (Bio-LLM Evaluation Suite, Vision-Language-Action Lab Robotics, Comparative Agentic Research Platform),
• open-source contributions in computational biology ecosystems (Galaxy Project, MAIA, mAIstro, Cognitive Kernel-Pro, Prime Intellect),
• communication assets (lab reports, demos, newsletters),
• and the operational cadence that demonstrates consistent research velocity.

Advance safe, reproducible, and equitable AI systems for life science and healthcare by bridging modern frontier-model capabilities with rigorous bioinformatics practice.

• Multimodal AI for Life Sciences: Vision-language-action pipelines for wet-lab automation, pathology, radiomics, and multi-omics decision support.
• Scalable MLOps & Evaluation: GPU-accelerated genomic workflows, benchmark harnesses, GAIA-style agentic evaluations, and reproducible research infrastructure.
• Responsible Deployment: Privacy-first data handling, HIPAA-aligned pipelines, secure audit trails, and transparent documentation for clinical stakeholders.

Progress will be tracked via project boards and weekly lab reports (see docs/lab-reports/).

• Fortnightly Lab Reports: Summaries of code contributions, experiments, benchmarks, and learnings. (Template in templates/lab-report.md)
• Demo Videos: ≤3 minute walkthroughs for each major PR, embedded into project README badges.
• Monthly Newsletter: “Top 5 AI/Bio Papers & Repos” highlighting contributions and community insights.
• Changelog: Running catalogue of merged PRs, metrics, and community feedback (see CHANGELOG.md).

1. Refresh GitHub profile README, pinned repositories, and contribution graph screenshots.
2. Launch a public project board outlining experiments, benchmarks, and writing tasks.
3. Submit introductory outreach messages to Galaxy, MAIA, and Cognitive Kernel-Pro maintainers (templates in templates/outreach/).
4. Scope Bio-LLM Evaluation Suite architecture and dataset risk assessment.
5. Log first lab report draft covering kickoff activities and initial community touchpoints.

• src/bio_llm_eval/ — Python package scaffolding (data loaders, training/evaluation placeholders, CLI entry point).
• configs/pubmedqa.baseline.json — Sample configuration for the baseline pipeline.
• docs/ — Design docs, dataset notes, issues, lab reports.
• templates/ — Outreach and lab-report templates.
• tools/compliance/ — PHI scanning utility for dataset onboarding.
• tests/ — Pytest smoke tests validating scaffolding.

Run the placeholder pipeline:

python -m bio_llm_eval.cli --config configs/pubmedqa.baseline.json --report reports/baseline.json

The command currently operates in dry-run mode (no model download). Setting "dry_run": false triggers full LoRA-capable fine-tuning when a GPU is detected; if no GPU is available the runner automatically falls back to dry-run mode and logs a warning.

Optional flags:

python -m bio_llm_eval.cli \
  --config configs/pubmedqa.baseline.json \
  --append-changelog \
  --changelog-path CHANGELOG.md \
  --append-lab-report \
  --lab-report-path docs/lab-reports/2025-10-13.md

This appends a bullet summarising accuracy, toxicity, and hallucination heuristics to the specified changelog / lab report after the run.

python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -e .[dev]
pytest

Refer to CONTRIBUTING.md for full workflow details (linting, compliance steps, submission guidelines).

• make lint / make test / make run-sample provide convenient wrappers for common tasks.
• GitHub Actions workflow (.github/workflows/ci.yml) runs lint, tests, and the dry-run pipeline across Python 3.9–3.11 on every push/PR.

• data/samples/pubmedqa.sample.jsonl — minimal 3-example slice used for tests and dry-run validation. Replace with the approved dataset path after completing the compliance checklist in docs/checklists/completed/.

• batch_size, gradient_accumulation_steps — control training throughput for the LoRA pipeline.
• lora.enable — toggle PEFT LoRA adapters; targeting modules configurable.
• dry_run — Skip model downloads; automatically overridden to True if no GPU is detected.

MIT License to encourage collaboration while protecting sensitive datasets and proprietary assets.