Skills
What I am good at, and the work that shows it.
Six areas, each with the tools I use, how deep the experience goes, and the projects where it was earned.
- Deep · daily work since 2023 Time-series ML & anomaly detection Finding the moment something goes wrong in multi-sensor streams, and proving the detector is right before anyone relies on it.
- Applied · notebook to product ML in production & MLOps Getting a model out of the notebook and keeping it honest afterwards: export, inference inside a .NET product, pipelines, tracking and monitoring.
- PhD, Applied Statistics · NTNU Bayesian & spatial statistics Models that know how uncertain they are, and decisions that use that uncertainty: where to sample next, when to replan, what to believe.
- Production · 2.5 years GPU visualisation & interactive UIs Making very large, very fast sensor data explorable: WebGL2 rendering, real-time views and comparison tools that engineers use at the machine.
- Production · identity first Azure cloud & DevOps Cloud storage and services designed around least privilege, provisioned by script and delivered by pipeline.
- ~560 commits · 30 merged PRs Software engineering Two and a half years on a production .NET and TypeScript product: code review, tests, CI/CD and refactoring that keeps a large codebase moving.