Research

Research

I develop mathematical and computational methods for learning from complex temporal and longitudinal data. My research combines the development of new methodology with applications to high-dimensional, multivariate and irregular real-world data.

My work spans time-series and longitudinal modelling, representation and generative learning, graph-based methods, and anomaly detection. A current research direction I am pursuing connects reservoir computing with free probability and dynamical systems, using mathematical structure to understand learning behaviour and develop efficient methods for model and hyperparameter selection.

Across these areas, I am interested in methods that are statistically and mathematically grounded, computationally efficient, and applicable beyond a single domain.

Research interests

  1. Mathematical and computational methods for machine learning
  2. Time-series and longitudinal modelling
  3. Reservoir computing, free probability, and dynamical systems
  4. Representation learning and foundation models for temporal data
  5. Generative and graph-based methods for temporal data
  6. Anomaly detection and learning from high dimensional multivariate data

Projects and research networks

  • ML4ITS — Machine Learning for Irregular Time Series (IKTPLUSS, Research Council of Norway, grant no. 312062)
    Telenor lead in a collaborative project developing machine-learning methods for irregular and heterogeneous temporal data.
    Project website
  • NorwAI — Norwegian Research Center for AI Innovation (SFI, Research Council of Norway, grant no. 309834)
    Telenor representative in the Hybrid AI and Data work package.
    Project website
  • SURE-AI — More sustainable, risk-averse and ethical AI technologies (Norwegian AI centre, Research Council of Norway, project no. 357482)
    Telenor industry partner representative in research on sustainable, reliable and ethical AI.
    Project website