Neuroscientist → ML Research Engineer
Connectomics‑driven machine learning in PyTorch. I build research‑to‑production pipelines that turn large wiring diagrams into models, insights, and demos.
Data & ML
PyTorch, scikit‑learn, GNNs & RL
SQL, pandas & cloud warehouses
Hugging Face, transformers
Domain
Connectomics & neurobiology
Genomics & single‑cell omics
Graph & gene network priors
Engineering
Reproducible pipelines & CI/CD
Docker, AWS SageMaker & FastAPI
Distributed training & monitoring
Communication
Research papers & blog posts
Clear READMEs & storytelling【10811468707479†L14-L15】
Teaching & open source contributions
Featured Projects
FlyWire Connectomics Toolkit
- 1,000+ neurons analyzed
Programmatic access to FlyWire via fafbseg‑py, standardized coordinate transforms, navis interoperability, and a small PyTorch module for learned morphology/connectivity embeddings.
- Neuron/neuropil retrieval + transforms; reproducible notebooks → Dockerized pipeline
- navis‑compatible objects for downstream morphology/graph analyses
- Unit tests & CI surfaced in repo; paper‑style README
oviIN Input‑Module Analysis
- 1,000+ presynaptic partners
Reproduces the oviIN multi‑circuit hub input analysis: modular structure over presynaptic partners, PyTorch embeddings for similarity metrics, and a Streamlit demo to browse modules.
- Modularity on oviIN inputs; per‑module statistics + synapse maps
- Abstract→Methods→Results→Discussion; figures auto‑rendered
- Streamlit exploratory UI; Docker env; CI badge
Research → Production
I believe high‑impact science comes from pairing curiosity‑driven research with rigorous engineering. Each project follows a structured lifecycle that reflects this philosophy【0†L20-L25】:
Reproducible Pipelines
- Environment files (conda, pip)
- Clear data & code separation (src/, data/, notebooks/)
- Automated data preprocessing scripts
Testing & CI/CD
- pytest suites cover core functions
- GitHub Actions for linting & tests
- Pre‑commit hooks & code quality tools
Deployment & Monitoring
- Docker images & container registries
- FastAPI or SageMaker endpoints
- Logging, metrics & cost optimization
Publications & Teaching
Beyond code, I share knowledge through papers and lectures. Recent highlights include:
- Fruit Fly Decision‑Making: First author on a study exploring how multiple circuits converge in oviIN neurons to govern state‑dependent egg‑laying behaviour – inspiring modular architectures in RL【0†L18-L25】.
- Adult Fly Connectome Teaching: Developed course material and hands‑on labs for mapping the Drosophila connectome using navis/fafbseg‑py and demonstrating graph ML applications.
- Guest Lectures: Hosted workshops on integrating multi‑omics data into ML models and wrote blog posts on neuro‑inspired architectures【224711995524461†L288-L299】.
Let’s collaborate
Whether you’re building the next generation of biomedical AI products or exploring fundamental questions at the intersection of computation and biology, I’d love to help.