Projects

Hireves: Interactive Tool for the Relocation of Emergency Medical Vehicles
Hireves is an interactive tool that combines human expertise and AI-driven optimization to improve the allocation of emergency medical vehicles. It enhances decision-making by integrating qualitative and quantitative data, reducing response times, and maximizing coverage.
Hireves: Interactive Tool for the Relocation of Emergency Medical Vehicles
Bayesian Survival Models with Latent Variables Modeled by the Zero-Modified Power Series Distribution
This project leverages Bayesian methods with INLA and Stan to analyze survival data, modeling latent variables with the Zero-Modified Power Series distribution. Applications focus on lung cancer and melanoma to enhance survival predictions and treatment evaluations.
Bayesian Survival Models with Latent Variables Modeled by the Zero-Modified Power Series Distribution
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
Predictive Analytics for Jewelry Sales Using Time Series and Machine Learning
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 Time Series Modeling for Market Shares Using R-INLA
How good is this medical device? Bayesian mixed models for agreement measures
This project develops Bayesian mixed models and robust methods to evaluate agreement metrics between medical devices, focusing on outlier accommodation and clinical applications in COPD diagnostics. It includes software development to enhance accessibility for researchers and clinicians.
How good is this medical device? Bayesian mixed models for agreement measures
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
Bayesian Learning for Microbioma
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.
Statistical Machine Learning in Environmental Health
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.
Methods for Compositional Data using INLA
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.
Bayesian Hierarchical models in Marine Ecology
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.
Spatial Bayesian Learning in Ecology and Climate Change