Bio
Marina Riabiz received her undergraduate and master’s degree in Mathematical Engineering from Politecnico di Milano, Italy, specialising in Applied Statistics. She completed her PhD in the Signal Processing Group, Information Engineering, at the University of Cambridge, UK, with a thesis "On Latent Variable Models for Bayesian Inference with Stable Distributions and Processes", supervised by Prof. Simon Godsill.
In October 2018, she joined the Cardiac Electro-Mechanics Research Group (CEMRG) at King's College London, where she works with Prof. Steven Niederer on uncertainty quantification for myocyte models.
Marina is developing Monte Carlo methods for Bayesian high-dimensional parameter inference in dynamical systems describing calcium transients and their contribution to the action potential. The project is in collaboration with Prof. Chris Oates and other researchers from the Alan Turing Institute.
Marina’s broader research interests are at the intersection of probabilistic machine learning, computational statistics and uncertainty quantification, applied to engineering and medical sciences.
