Now
Currently
Machine learning and software engineer at Sandvik Coromant in Trondheim, on sensorized cutting tools. This autumn that has meant shipping a streaming fault detector that caught every labelled insert fault without a false alarm. A deliberately simple rule beat our deep models. The Azure ML pipeline behind it retrains and re-selects it whenever new labelled data arrives.
Most of the rest is on either side of the model: WebGL2 visual analytics for multi-hour recordings, a live comparison against a reference cut, and the C#/.NET, TypeScript and Azure plumbing that turns a notebook result into something a product team can ship. I am also teaching colleagues, through an ML curriculum that runs from autograd to MLOps.
Before this, a PhD at NTNU with Jo Eidsvik on adaptive sampling for autonomous underwater vehicles: Gaussian random fields over the ocean, value-of-information criteria for deciding where to sample next, and long-horizon path planning that has to respect obstacles and a battery. It ended with a planner running onboard during a fjord trial, which is a different kind of nervous from waiting on a test suite.
Thinking about
The seam between those two jobs is where I would like to spend more time. Adaptive sampling in the literature usually assumes the information criterion is free to evaluate; in production nothing is free. Concretely, the questions I find myself returning to:
- What happens to a value-of-information criterion when it must be computed inside a hard latency budget — and does the resulting decision survive the approximation?
- Informative path planning where the information term itself is a learned surrogate, and the reality gap belongs to the information, not just the dynamics or the perception.
- Sequential change-point detection and value of information as the same problem in different notation — when to acquire, where, and at what cost.
Always glad to talk to people working on any of that, in academia or industry.
Publications
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RRT*-enhanced long-horizon path planning for AUV adaptive sampling using a cost valley
Ge, Y., Eidsvik, J., & Olaisen, A. J. H.
Knowledge-Based Systems, 2025 -
Using expected improvement of gradients for robotic exploration of ocean salinity fronts
Olaisen, A. J. H., Ge, Y., & Eidsvik, J.
Environmetrics, 2025 -
Efficient 3D real-time adaptive AUV sampling of a river plume front
Berild, M. O., Ge, Y., Eidsvik, J., Fuglstad, G.-A., & Ellingsen, I.
Frontiers in Marine Science 10, 2024 -
3-D Adaptive AUV Sampling for Classification of Water Masses
Ge, Y., Eidsvik, J., & Mo-Bjørkelund, T.
IEEE Journal of Oceanic Engineering 48(3), 2023 · doi · pdf -
Long-Horizon Informative Path Planning with Obstacles and Time Constraints
Ge, Y., Olaisen, A. J. H., Eidsvik, J., Jain, R. P., & Johansen, T. A.
IFAC-PapersOnLine 55(31), CAMS 2022 · doi · pdf
Earlier work on information-driven sampling is in the information-gain note (pdf).