Virgilio is a statistician with deep expertise in Bayesian inference, spatial statistics, and statistical computing. His research focuses on the development and application of Bayesian methods for complex data structures, particularly using the Integrated Nested Laplace Approximation (INLA). He has made significant contributions to the R programming ecosystem through the development of widely used packages and tools for Bayesian modeling and spatial analysis.
Virgilio is the author of Bayesian Inference with INLA, a widely adopted reference in the field that received the 2022 SEIO–BBVA Foundation Award in Data Science and Big Data. He is committed to making advanced statistical methods accessible to a broad audience through clear documentation, open-source software, and active engagement with the research community via platforms like GitHub and ResearchGate.
Education & Career
• PhD in Statistics
• Author of Bayesian Inference with INLA
• Contributor to the R ecosystem with a focus on Bayesian and spatial modeling
Research Focus
Virgilio’s work centres on efficient Bayesian computation and the modelling of spatial and spatio-temporal data. He is particularly interested in applied Bayesian inference using INLA, as well as the integration of statistical methods into reproducible and scalable R workflows.
Current Projects
• Development of R packages for Bayesian and spatial analysis
• Applied research in epidemiology, environmental science, and spatial statistics
• Contributions to the ongoing development and documentation of the INLA methodology
Professional Consultancy
Virgilio provides expert support to academic and applied research teams in the areas of Bayesian modeling, spatial analysis, and statistical computing. His consultancy includes guidance on model design, computational methods, and reproducible workflows.
Teaching & Skills
• Teaches Bayesian inference, spatial statistics, and INLA in R
• Experienced in package development, reproducible research, and scientific communication
• Advocates for open-source tools and transparent, reproducible science
Links
• ResearchGate
• Google Scholar
• ORCID
• GitHub