Research

I am interested in the theory of variational models for imaging inverse problems, with a particular focus on the functional-analytic properties of spatially varying regularisation methods. In particular, my work lies at the intersection of functional analysis, geometric measure theory, and functions of bounded variation (BV), where I study the analytical properties of reconstruction models and their interaction with modern learning-based techniques.

Spatially Varying Regularisation

Mathematical analysis of spatially adaptive total-variation (TV) and TV-type regularisers for imaging inverse problems and reconstruction tasks.

BV Spaces & Variational Methods

Functional-analytic and measure-theoretic tools for studying existence, stability, and analytical properties of reconstruction models.

Optimisation & Learning

Variational optimisation methods, unrolled iterative schemes, and combined learning-based approaches to image reconstruction.

Publications & Preprints

Selected publications and preprints.

Talks

Selected research talks, presentations and posters.