I work on a broad spectrum from the fundamental mathematical inverse problems theory to applications in near-space remote sensing and subsurface imaging. I collaborate with high-level international research groups both in academia and industry. My research highlight is the development of the methodology of discretisation-invariant and computationally feasible priors for Bayesian inversion of function-valued unknowns. Applications include e.g. tomography (ionospheric, electrical impedance, X-ray) and radar pulse-compression coding and analysis methods.
I am PI in the following Academy of Finland projects
Hypermodels and stable priors for Bayesian inversion with applications in ionospheric tomography and subsurface imaging 01.09.2017 - 31.08.2020
Probabilistic Deep Learning via Hierarchical Stochastic Partial Differential Equations / Consortium: AO 01.01.2018 - 31.12.2019