I build rigorous, reproducible tools for problems at the intersection of biology, data, and human health. My work combines quantitative modeling, machine learning, and clear technical communication to turn complex scientific questions into useful software.
Real-data analysis of International Brain Laboratory Neuropixels recordings: population trajectories, neural decoding, cross-temporal dynamics, and region-level comparisons from a pinned public NWB dataset.
View the Decision Geometry analysis dashboard
Python NWB scikit-learn DANDI reproducible research
| Project | Focus | Tools |
|---|---|---|
| CADENCE | Model-based, closed-loop control of glial calcium dynamics through minimal, well-timed intervention. | Python, statistical modeling |
| PharmaBlast | Physics-informed simulation of microplastic transport in water and its interaction with pharmaceuticals. | Python, numerical simulation |
- Transparent, testable computational models for biomedical questions
- High-dimensional biological and time-series data
- Research software that is reproducible, readable, and useful beyond a single notebook
Neural population dynamics, adaptive control for biological systems, and machine-learning methods that make scientific results easier to validate and use.