Overview
Our AI/ML research spans from fundamental algorithm development to practical applications in engineering systems. We focus on explainable AI, reinforcement learning for control, deep learning for physics simulation, and trustworthy AI for safety-critical applications.
As a member of the OpenAI Lab at NTNU, I collaborate with researchers across disciplines to advance AI methods and their responsible deployment in real-world systems.
Research Topics
Multivariate Methods
Statistical and machine-learning techniques for multivariate data analysis.
Sparse Modeling
Parsimonious representations and sparsity-promoting methods for interpretable models.
Deep Learning
Representation learning and neural architectures for simulation and perception tasks.
Safe Reinforcement Learning
Reinforcement learning approaches with safety constraints for real-world control.
Large Language Models
Research on capabilities and applications of LLMs for engineering tasks.
Agentic AI
Study of autonomous, goal-directed agents and safe deployment strategies.
Key Projects
EXAIGON
Explainable machine learning (16M NOK, 2020-2023)
Autosit
Trajectory prediction using ML (12M NOK, 2019-2022)
Award-Winning Student Projects
