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.
- Gaussian processes · GMRFs Deep Real-time spatial models onboard an AUV
- Adaptive sampling · value of information Deep PhD, five peer-reviewed papers
- Informative path planning · RRT* Deep Long-horizon planner with obstacles and time limits
- Monte Carlo · experimental design Deep Simulation studies before every field trial
- HPC · Slurm, MPI, OpenMP, CUDA Applied Planner benchmarks on a GPU cluster
My PhD with Jo Eidsvik at NTNU was about decisions under uncertainty: Bayesian models of the ocean updated in real time, and criteria for choosing the next measurement. Each method was tested in simulation before it went to sea and then run onboard autonomous vehicles in Norwegian fjords.
It shapes how I work in industry too. I want to know how confident a model is before I let it make a decision.
Where it shows up
- 2020–23 Teaching an underwater robot where to look Bayesian models and a planner that ran onboard an autonomous underwater vehicle in Trondheimsfjord, steering it along a river-plume front instead of surveying evenly.
- 2022–25 Planning a long way ahead, around obstacles An RRT*-based planner that looks beyond the next waypoint: it trades information against distance over a cost valley while respecting obstacles and a time budget.
- 2021–23 Classifying water masses in three dimensions Adaptive sampling in 3-D: a vehicle porpoises through depth layers to classify river and ocean water, choosing each move by how much it reduces classification error.