Lassi Roininen

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.