Iván Palomares-Carrascosa is a Lecturer in Data Science and Artificial Intelligence with the School of Computer Science, University of Bristol, UK. Since November 2018, he is also a Fellow of The Alan Turing Institute, where he and his team members investigate personalisation methods for assisting citizens to engage with healthy habit engagement and development, and smart cities applications. He currently leads the Decision Support and Recommender Systems research group at the University of Bristol, where he supervises PhD candidates, postdoctoral and visiting researchers. His research interests include data-driven and intelligent approaches for recommender systems, personalisation for leisure and tourism in smart cities, large group decision making and consensus, data fusion, opinion dynamics and human-machine decision support.

Iván received his two MSc degrees in Computer Science (with Faculty and Nationwide Distinctions) and Soft Computing & Intelligent Systems (Hons), from the University of Jaen, Spain and University of Granada, Spain, in 2009 and 2011 respectively. He received his PhD degree in Computer Science with Nationwide distinctions from the University of Jaen, Spain, in 2014. His research results have been published in top journals and conference proceedings, including IEEE Transactions on Fuzzy Systems; Applied Soft Computing; International Journal of Intelligent Systems; Information Fusion, Knowledge-Based Systems; Data and Knowledge Engineering; and Renewable & Sustainable Energy Reviews, amongst others. He serves as a reviewer in numerous top-tier international journals in related areas to decision support systems.

Iván has published two books, the last of which is the first compilation of research on Large Group Decision Making to date.

Research interests

Maintaining a healthy and active lifestyle to prevent the development of chronic diseases, physical or mental problems, constitutes an important societal challenge nowadays. This is particularly evident in large cities, as they are often characterised by a busy and dynamic lifestyle. In such environments, fostering citizen engagement with suitable (and usually tailored) daily activity patterns that they like, would therefore be a key factor to improving their overall wellbeing. Providing personalised recommendations on daily life activities (exercising, eating, etc.) that drive the development of healthy habits, might not only impede the development of diseases but also help maintaining a positive physical and mental state of wellbeing in their daily lives.

Iván's proposed research aims at investigating how Recommender System approaches can help creating personalisation services for engaging users with a healthier and active lifestyle. Techniques involved include intelligent data fusion, preference and behaviour modelling, social and sensor data analysis, and multi-criteria decision making.

Iván has expertise in the following areas:

  • Multi-criteria decision-making
  • Decision support systems
  • Recommender systems
  • Consensus and opinion dynamics

Achievements and awards

Spanish National Award for Best PhD Thesis in AI by the Spanish Association for AI (AEPIA).