My research spans statistical modeling, machine learning, and Bayesian methods applied to diverse domains, with a strong current focus on Precision Medicine and Personalized Treatment Effect Prediction. Below are the main fields that structure my work:
I develop advanced statistical and machine learning methods to support personalized healthcare decisions. My work includes Bayesian Additive Regression Trees, meta-heuristic feature selection, and hybrid AI approaches for predicting individual treatment effects (PITE), particularly in clinical trials for alcohol use disorders.
I explore regression and tree-based models for complex outcome structures and constrained domains, aiming to improve predictive accuracy and interpretability.
I apply probabilistic models and machine learning algorithms to predict vegetation and biomass using multispectral and LiDAR UAV data.
I develop statistical models for ecological and climate data, including compositional regression and hierarchical approaches to understand species distribution and environmental interactions.
I have worked on Bayesian hierarchical models for disease surveillance and epidemic dynamics, as well as modeling COVID-19 vulnerability and protection indices.
Earlier research focused on actuarial science, including collective risk models, heavy-tailed distributions, and Bayesian approaches for pricing and risk measures.
I have proposed methods for clustering and regression with compositional data, applied to wood properties, marine ecology, and other complex systems.
Earlier work includes analyzing scientific impact and visibility through bibliometric methods.