Projects
Three main lines characterize my research:
L1. Spatio-Temporal Statistics in Species Distribution Modeling (SDMs), in particular applying spatio-temporal models to plant and marine species.
L2. Bayesian Computational Methods (BC-Methods), particularly developing techniques for solving Compositional Data problems in the context of the Integrated Nested Laplace Approximation (INLA) and Markov Chain Monte Carlo Methods (MCMC).
L3. Bayesian Statistical Learning in Health, Environment and Economics (BSL-HEE). The application of Bayesian learning principles in Health, Environment and Economics has become a substantial part of my current projects.
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Predictive Analytics for Jewelry Sales Using Time Series and Machine Learning
This project leverages time series analysis and machine learning techniques to predict sales for a jewelry company, aiming to enhance forecasting accuracy and optimize inventory management
Bayesian Time Series Modeling for Market Shares Using R-INLA
This project employs Bayesian Compositional Data time series models in INLA to analyze and predict market shares, providing insights for strategic decision-making
Bayesian Learning for Microbioma
This project applies Bayesian Dirichlet models to investigate the effects of stress on vaginal microbiome composition in a Spanish cohort, providing insights into how stress-related changes in microbiota can impact health
Statistical Machine Learning in Environmental Health
Statistical Machine Learning in Environmental Health leverages algorithms to understand the impacts of environmental factors like air pollution on public health outcomes, particularly during the COVID-19 pandemic.
Methods for Compositional Data using INLA
This project focuses on developing Bayesian hierarchical models for compositional data, enabling the analysis of complex ecological and environmental datasets. By using advanced statistical methods, the research aims to improve the understanding of data structures and relationships in various scientific fields.
Bayesian Hierarchical models in Marine Ecology
This project focuses on developing hierarchical Bayesian models to study Marine Ecology, incorporating interactions among species such as dolphins, fish, and seabirds to improve the understanding of their distribution and abundance.
Spatial Bayesian Learning in Ecology and Climate Change
Bayesian learning for Ecology involves applying advanced statistical models to understand ecological data, providing insights into environmental processes and the impacts of climate change.
Mixed Models for Plant Epidemiology
This project uses Bayesian Mixed Models to analyze and predict the spread of plant diseases such as Citrus Black Spot, Circular Leaf Spot, and Xylella fastidiosa, integrating spatial and temporal data to inform effective control strategies.