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.

Previously:

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.

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.

Understanding regularization strategies and prior modeling

Examples of recent work:

• Theory of constraints and sparsity-promoting regularization for linear inverse problems, and its applications to uncertainty quantification. (Constraints, Sparsity 1, 2)
• One-bit compressed sensing with generative models (MSc thesis)

Applications and software

Examples of recent work (all of which come with code):

• A computational framework and implementation of implicit priors in Bayesian inverse problems
• Gaussian Processes under Monotonicity Constraints
• Electrical impedance tomography (EIT) with partial data
• Contributions to the CUQIpy software package for Computational Uncertainty Quantification for Inverse problems in Python: .

Uncertainty quantification methodologies

Examples of recent work:

• Self-supervised Conformal prediction
• Sparsity-promoting implicit priors (PhD thesis)