معرفی
Dr. Matteo Fasiolo is a Senior Lecturer in the School of Mathematics at the University of Bristol, specializing in Statistical Science. His research focuses on advanced statistical modeling with significant applications in electricity demand forecasting and medical statistics, leveraging Generalized Additive Models (GAMs) as a core methodology.
His primary research interests span:
- Generalized Additive Models and their extensions for complex data structures
- Covariance matrix modeling for high-dimensional energy forecasting
- Probabilistic forecasting techniques for uncertainty quantification
- Statistical machine learning including variational inference and contrastive learning
- Applications in electricity grid management and medical diagnostics
Recent publications (2023-2025) reveal a dual focus: developing scalable statistical methods for electricity net-demand prediction in Great Britain using additive covariance models, and applying distributional regression to medical challenges like kidney function decline and cardiovascular risk prediction. His work on SoftCVI demonstrates innovation in variational inference, while extensions to GAMs address both mean modeling and full distributional forecasting.
Dr. Fasiolo has supervised at least one student as indicated by university records. His research outputs include 16 publications and 2 publicly available datasets, reflecting active contributions to methodological statistics and domain-specific applications in energy systems.
