The research group was created in 1990. At present the group includes 8 senior members. It has published more than 600 scientific articles, supervised 28 doctoral theses, participated in 51 funded research projects and leading 24 of them. It is therefore a consolidated group.

The main research areas are:

  • Techniques for representing and managing uncertain information.
  • Techniques for structure learning and inference with probabilistic graphical models, particularly Bayesian networks and influence diagrams, and their application to different problem types.
  • Techniques for text mining, particularly document classification and clustering.
  • Information retrieval techniques, for both plain and structured text.
  • Recommender systems, content-based, collaborative filtering and hybrid.
  • Techniques for gene expression data analysis.

In general we can contribute solutions to different types of problems (medical, agri-food sector...) that either require the automatization of classification, clustering and inference tasks, or can be directly modelized by means of mining and data analysis techniques. We can also contribute to the design of recommender systems in different contexts (politician, academic, entertainment...), as well as to the design of systems for information retrieval and management of documents.