The Computational Intelligence Group (CIG) was created in 2008 and is lead by professors Pedro Larrañaga and Concha Bielza. Research of CIG members, both theoretical and practical, is devoted to modelization (from a statistical and machine learning perspectives), heuristic optimization, and neuroinformatics. The CIG has been involved in more than 100 research projects, mostly in public competitive calls but also for private companies. Current public projects include Human Brain Project, Cajal Blue Brain and several national projects from the Spanish Ministry of Science and Innovation. CIG has collaborated with companies as Telefónica I+D, Abbott, Arthur Andersen, Progenika Biopharma, Bank of Santander and Panda Security.

  • The main research area is modelization, whose current main issues include: data streams, multi-dimensional supervised classification, multi-label classification, clustering in high-dimensional spaces, feature subset selection using methods as Bayesian networks, regularization, classification by regression.
  • In heuristic optimization, which is the second main line of research, we investigate state-of-the-art questions related to the improvement of heuristic optimization methods and extension of their applicability to more complex problems (e.g. multi-objective, mixed representation, non-continuous objective functions), with special emphasis on estimation of distribution algorithms.
  • Neuroscience is the main field of application. Some problems that we face include: (a) neuroanatomy issues, like modeling and simulation of dendritic trees and classification of neuron types based on morphological features; (b) neurodegenerative diseases, like predicting health-related quality of life in Parkinson’s disease and searching for genetic biomarkers in Alzheimer’s disease.
  • The second main field of application is Industry 4.0 where we develop machine learning solutions for cyber-physical systems. Other application domains are: biomedicine, agriculture, bioinformatics, bibliometry and environment.


15th Machine Learning and Advanced Statistics Summer School (MLAS)

An intensive set of courses providing attendees with an introduction to the theoretical foundations as well as the practical applications of some of the modern statistical analysis techniques and machine learning methods currently in use.

Date: Jun 19-30, 2023 – Madrid.

3rd International Workshop on eXplainable Artificial Intelligence in Healthcare (XAI-Healthcare)

Keynote speaker: Prof. Mihaela van der Schaar (University of Cambridge).

Organizing CommitteeConcha Bielza, Pedro Larrañaga, Primoz Kocbek, Jose M. Juarez, Gregor Stiglic, Alfredo Vellido.

Date: Jun 15, 2023 – Portoroz, Slovenia (during AIME 2023).

PhD dissertation defense of Carlos Puerto-Santana

¡Felicitamos a Esteban Puerto que el 2 de marzo defendió su tesis doctoral en la ETSIINF! No se leen todos los días tesis donde el doctorando consigue tantas publicaciones de alto nivel como IEEE PAMI, IEEE Internet of Things and IEEE Transactions on Neural Networks and Learning Systems.


Industrial Applications of Machine Learning

Larrañaga, P., Atienza, D., Diaz-Rozo, J., Ogbechie, A., Puerto-Santana, C., & Bielza, C. (2018). Industrial Applications of Machine Learning. CRC Press.

Data-driven Computational Neuroscience

Bielza, C., & Larrañaga, P. (2020). Data-driven Computational Neuroscience: Machine Learning and Statistical Models. Cambridge University Press.