About
Research software for data-rich engineering systems.
My work sits at the intersection of machine learning, sensor data, simulation and engineering domain knowledge. I focus on tools that help engineers inspect, validate and interpret complex measurements.
What I build
- Digital-twin prototypes that connect sensor data, models and automated reports.
- Anomaly-detection workflows for acoustic, operational and experimental measurements.
- QA/QC tools for engineering data, with a focus on reproducibility and traceability.
- Scientific Python tools for model exploration, diagnostics and communication.
How I work
I prefer repositories that are easy to audit: clear problem statements, quick starts, synthetic or public example data, visual outputs, assumptions and limitations.
For confidential engineering data, I use synthetic or public datasets to demonstrate the workflow without exposing sensitive information.
Core themes
Academic profiles
For publications, institutional profile information and persistent researcher identity, see: