Federico Divina obtained his Ph.D. in Artificial Intelligence from the Vrije Universiteit of Amsterdam, and after that he worked as a postdoc at the University of Tilburg, within the European project NEWTIES. In 2006 he moved to the Pablo de Olavide University, where he is actually an Associate Professor.
He has been working on knowledge extraction since his Ph.D. thesis at the Vrije Universiteit of Amsterdam. He has extensive experience in the application of Machine Learning, especially techniques based on Soft Computing, for the extraction of knowledge from massive data.
His main research interests are:
- Evolutionary Computation
- Machine Learning
- Big Data
Federico Divina has participated in various research project projects, for instance:
- Differential: this project aims to develop new tools and methods to manage and analyse information coming from several sources with the final goal of better understanding how and when energy is consumed in distributed facilities. This project was developed as a coordinated project with three complementary research groups from three different universities (Universidad de Granada, Universidad Pablo de Olavide and Universidad de Castilla La Mancha).
- GALICIAME: project that aimed at applying machine learning tools in order to extract knowledge from genetic data related to spinal muscular atrophy (SMA), in collaboration with the “Centro Andaluz de Biología del Desarrollo” (CABD).
- NEWTIES: EU project that aimed at developing an artificial society. This project involved the Vrije Universiteit van Amsterm, the University of Tilburg, the Napier University, University of Surrey, Napier University and Eötvös Loránd University.
For a complete list of my publications, please visit my Google Scholar Profile or my ORCID.
Evaluación de biclusters mediante intra-fluctuaciones mínimas: un enfoque multi-objetivo Workshop
Jornadas de Algoritmos Evolutivos y Metaheurísticos (JAEM'07), 2007.
Scatter search for high-dimensional feature selection using feature grouping Conference
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 0000.