About Fernando Rodriguez Avellaneda Fernando Rodriguez Avellaneda Postdoctoral Research Fellow, Marine Science spatio-temporal modeling environmental research Spatial epidemiology Python (Programming Language) data science Fernando Rodriguez Avellaneda is a Bayesian statistician specializing in spatial and spatio-temporal modeling. His research develops computational methods for complex geospatial data, with applications in environmental monitoring, infectious-disease dynamics, coral-reef ecology, fisheries, and uncertainty quantification. Events Presented Events May 10 - May 16, 2026 Bayesian Spatio-Temporal Modeling for Environmental Monitoring and Epidemiology: Disaggregation and Disease Spread Dynamics Fernando Rodriguez Avellaneda, Postdoctoral Research Fellow, Marine Science May 14, 14:00 - 16:00 B3 R5220 This PhD thesis introduces innovative statistical frameworks for modeling and interpreting spatial and spatio-temporal dynamics in geostatistical data and point processes, with applications in air pollution monitoring and infectious disease dynamics. The first project focuses on spatial disaggregation of normally distributed multivariate geostatistical data, motivated by air pollution applications in Portugal and Italy. The second project extends spatial disaggregation to a spatio-temporal setting for univariate normally distributed data, with the methodology illustrated through the monitoring
Bayesian Spatio-Temporal Modeling for Environmental Monitoring and Epidemiology: Disaggregation and Disease Spread Dynamics Fernando Rodriguez Avellaneda, Postdoctoral Research Fellow, Marine Science May 14, 14:00 - 16:00 B3 R5220 This PhD thesis introduces innovative statistical frameworks for modeling and interpreting spatial and spatio-temporal dynamics in geostatistical data and point processes, with applications in air pollution monitoring and infectious disease dynamics. The first project focuses on spatial disaggregation of normally distributed multivariate geostatistical data, motivated by air pollution applications in Portugal and Italy. The second project extends spatial disaggregation to a spatio-temporal setting for univariate normally distributed data, with the methodology illustrated through the monitoring
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