I am currently a postdoctoral researcher at the University of Eastern Finland as part of the inverse problems research group. I currently work on inverse problems in functional and quantitative MRI.

Research Interests:

My general research interests lie in using techniques from mathematical optimization, numerical mathematics and probability theory/statistics in inverse problems, image processing and machine learning. In particular, the theory and application of regularization strategies and prior modeling.

Constrained and sparsity-promoting inference

Constraints naturally occur in many physical applications; incorporating them into the inference process can improve both single reconstructions and uncertainty estimations, which allows for better decision making. Sparsity is common assumption, e.g., the manifold hypothesis, that improve reconstructions in settings where only few measurements or very noisy measurements are observed. Some related work:

• monotonic Gaussian processes (journal)
• projected distributions (journal)
• regularized Gaussians (journal)
• one-bit compressed sensing with generative models (MSc thesis)

Prior modeling strategies

• computational framework for implicit priors (preprint)
• random spanning tree Markov random field priors (preprint)

Imaging applications

• EIT with partial data (journal)
• fast-ion tomography (journal)

Non-Bayesian uncertainty quantification

• self-supervised conformal prediction (conference proceeding)

Other interest: game-based learning

• CTguesser: A computed tomography word guessing game (software)
• having fun in learning formal specifications (conference proceeding)

Previous positions:

Until September 2025, I was a postdoctoral researcher at the Technical University of Denmark as part of the CUQI research project on Computational Uncertainty Quantification for Inverse Problems. I worked on the use of tools from mathematical optimization for modeling and sampling in Bayesian inverse problems.