Building reproducible tools for biological data and human-health research.
I work at the intersection of neuroscience, data science, and software engineering. My focus is turning complex experimental data into analysis pipelines that are understandable, testable, and useful to researchers.
I care about:
- quantitative models that make assumptions explicit;
- reliable workflows for high-dimensional and time-series data;
- validation that respects the experimental design; and
- documentation that makes research software easier to trust and reuse.
Languages & analysis
Python · SQL · NumPy · pandas · SciPy · scikit-learn
Scientific computing
Neural time series · electrophysiology · behavioral data · statistical modeling
· reproducible research
Engineering
Data validation · nested cross-validation · testing · command-line tools ·
technical documentation
- neural population dynamics and electrophysiology;
- interpretable machine learning for biomedical datasets;
- adaptive and closed-loop modeling of biological systems; and
- better interfaces between scientific analysis and usable software.
Question → measurable endpoint → leakage-safe analysis → reproducible result
I prefer small, well-tested analytical steps over opaque complexity. When a model is not supported by the data, I document that limitation clearly and use it to guide the next experiment.
If you are working on computational neuroscience, biomedical data, or research software, I would be glad to connect.