Hassan Yazdanian

I am a postdoctoral researcher in the Inverse Problems Group at the University of Oulu, specializing in computational imaging, Bayesian inverse problems, and uncertainty quantification (UQ). My research involves developing advanced numerical and statistical methods for solving large-scale linear and nonlinear inverse problems, with applications in X-ray imaging, semiconductor imaging, and seismic tomography.

My current research focuses primarily on Bayesian reconstruction and UQ for multi-contrast X-ray tomography, where attenuation, phase, and dark-field scattering information are jointly used to improve image reconstruction under noisy and limited-data conditions. I am particularly interested in scalable inference methods, structurally correlated prior models, and computational techniques that translate rigorous mathematical models into practical and interpretable imaging solutions.

In parallel, I work on PDE-constrained Bayesian inverse problems and seismic tomography, including adjoint-based inference and goal-oriented UQ for ambient-noise tomography. Through this research, I aim to improve the reliability, computational efficiency, and interpretability of imaging and parameter-estimation methods across medical, industrial, and geophysical applications.