Regular biography
Alan Muriithi is a Research Fellow in the Department of Mathematical Institute at the University of Oxford. His research interests lie at the interface of inverse problems, optimisation, and deep learning, with a focus on the mathematical foundations of learning-based reconstruction for nonlinear and ill-posed inverse problems under model uncertainty. His work analyses how operator uncertainty affects identifiability, stability, robustness, and solution selection of learned reconstruction schemes and gradient-based algorithms. He develops structural and compatibility conditions that incorporate analytic information from approximate forward models into learning-based methods, ensuring that learned corrections respect the observable and stable components of the inverse problem. His research draws on variational analysis, stochastic and spectral methods, and regularisation theory, with applications in computational imaging.