Research line

Development of probabilistic models for the description of population dynamics, like COVID-19 transmision. We are exploring the application of the previously developed MC-based models to other micro-organisms of interest in health and in wine sciences.

We are developing ML-based models to identify the risk factors for the presence of ticks in urban parks, in collaboration with Arantza Portillo and Jose A Oteo (CIBIR).

We are exploring the application of ML-based predictive regression and classification models to the analysis of complex data (biological, chemical, physico-chemical) in different problems related to wine sciences and health, within the frame of One Health (wine origin and quality identification, phytopagoten evolution, nutrition impact in health).