News

AI risks
Sep 2026
Dr. Aïda Valls gives interview on artificial intelligence risks
Dr. Aïda Valls was interviewed by Onda Cero Catalunya to discuss the risks and challenges of artificial intelligence. She highlighted the importance of distinguishing real risks from alarmist predictions and stressed the need for human oversight and appropriate regulation as AI continues to evolve. Check the full interview.
IJCAI-ECAI 2026
Aug 2026
iTAKA at IJCAI-ECAI 2026
iTAKA was present at IJCAI-ECAI 2026, the 35th International Joint Conference on Artificial Intelligence, one of the top conferences on AI. It was held in Bremen, Germany, on 15-21 of August 2026. Dr. Valls and Dr. Moreno presented a work in the Qualitative Reasoning workshop. Dr. Valls also participated in the ECAI assembly as representative of the Catalan association on AI (ACIA).
Gresol Award 2026
Apr 2026
ExplainUp receive the Fundació Gresol Award 2026
The Entrepreneuship Talent Award of the Fundació Gresol recognized ExplainUp, a project that uses Artificial Intelligence to transform complex information into easy-to-read text. SRBLab and Inserlab from URV design and validate the tool, while the ITAKA group is involved in its implementation.
TECNATOX Workshop 2026
Apr 2026
Poster at TECNATOX workshop 2026
Our group has participated in the workshop. Pol Pujol has presented a poster about our work with the LSTM team about detection of cognitive impairment with sociodemographic and lifestyle variables, within the DEMDETECT project.

iTAKA: DATA ANALYSIS AND DECISION SUPPORT LAB PRESENTATION

ITAKA (Intelligent Technologies for Advanced Knowledge Acquisition) is a research group of the University Rovira i Virgili (URV). It was founded in January 2007 by Dr Antonio Moreno and Dr Aïda Valls, following the successful trail left by the Working Group on Multi-Agent Systems (GruSMA) from 2000 to 2006. Our research is mainly applied to eHealth, Tourism and Environment Protection.

  • Data analysis under uncertainty (fuzzy logic)
  • Personalized preference modelling and recommendation
  • Data fusion and multiple criteria aggregation
  • Machine learning for sequential analysis of retrospective data and continuous learning systems
  • Social media analysis for brand communication
  • Sentiment analysis in social networks
  • Explainability techniques for classifiers
  • Applications to diagnosis in Medicine, Tourism recommender systems or in Environmental risk analysis