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.


Pedro Larrañaga es elegido IEEE Fellow. ​

Pedro Larrañaga, Catedrático de Ciencias de la Computación e Inteligencia Artificial en la Universidad Politécnica de Madrid, ha sido elegido IEEE Fellow en la reunión que anualmente lleva a cabo la junta directiva de las distintas sociedades que forman parte de la Industrial of Electrical and Electronics Engineers (IEEE). La asociación IEEE se fundó en 1963 y cuenta en la actualidad con mas de 400.000 miembros que pertenecen a alguna de sus 39 sociedades.

El Profesor Larrañaga ha sido reconocido como IEEE Fellow por la IEEE Computational Intelligence Society por sus aportaciones novedosas en algoritmos de estimación de distribuciones, metodología de selección de variables y aprendizaje de redes Bayesianas a partir de datos.

Date: Oct 27, 2023 – Madrid.

La Tesis Doctoral de David Atienza obtiene el Premio Extraordinario. ​

La Comisión de Doctorado de la UPM otorga el Premio Extraordinario, en la convocatoria correspondiente al curso 2021/2022, a la Tesis Doctoral de David Atienza González titulada
"Nonparametric Models and Bayesian Networks. Applications to Anomaly Detection" cuyos directores son Pedro Larrañaga y Concha Bielza.

Date: Oct 23, 2023 – Madrid.

Pedro Larrañaga and Concha Bielza ELLIS Fellows.

Last August Pedro Larrañaga and Concha Bielza were elected as ELLIS Fellows.

ELLIS - the European Laboratory for Learning and Intelligent Systems - is a pan-European AI network of excellence which focuses on fundamental science, technical innovation and societal impact. Founded in 2018, ELLIS builds upon machine learning as the driver for modern AI and aims to secure Europe’s sovereignty in this competitive field by creating a multi-centric AI research laboratory.

ELLIS Fellows are leading scientists in the European ML/AI community who advance science, and also act as ambassadors of ELLIS, thus giving a voice to data-driven AI research in Europe. Pedro and Concha are the Directors of the ELLIS Unit Madrid that was created in 2022.

Date: Oct 2, 2023 – Madrid.

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.