معرفی
Dr. Marina Riabiz is a Lecturer in Statistics at King's College London's Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. She holds a PhD from the University of Cambridge and a Master’s in Mathematical Engineering from Politecnico di Milano. Her research focuses on computational statistics, probabilistic machine learning, Bayesian inference, and medical engineering applications. She co-leads the DT4Health Centre for Doctoral Training, which develops digital twins for healthcare innovation. Key projects include uncertainty quantification in cardiac models and virtual patient cohort simulations. Her work spans MCMC optimization, Gaussian approximation, and state space models with stable processes. She has contributed to high-impact journals and conferences, including Annual Review of Statistics and IEEE Transactions.
Education:
- PhD in Signal Processing, University of Cambridge (UK)
- MSc in Mathematical Engineering, Politecnico di Milano (Italy)
- BSc in Mathematical Engineering, Politecnico di Milano (Italy)
Research Interests:
- Bayesian computational methods
- Medical data science
- Uncertainty quantification
- Machine learning integration with statistical inference
Publications Overview:
Riabiz’s recent work emphasizes MCMC postprocessing, optimal thinning algorithms, and Gaussian approximation for stable noise systems. Her cardiac modelling research bridges computational statistics with biomedical applications, including electrophysiology model calibration and virtual patient cohorts. These contributions advance both theoretical statistics and translational healthcare technologies.
Awards: No specific awards listed.
Grants and Collaborations: Active in multidisciplinary teams at King’s College London and the Alan Turing Institute. Leads DT4Health’s recruitment and mentoring initiatives.
Labs/Teams: Cardiac Electro-Mechanics Research Group (BMEIS), Centre for Doctoral Training in DT4Health.
