World's Best Scientists 2026 revealed!
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Computer Science
Spain
2025

D-Index & Metrics

Computer Science

D-Index
68
Citations
26803
World Ranking
2037
National Ranking
23

Research.com Recognitions

  • 2025 - Research.com Computer Science in Spain Leader Award
  • 2022 - Research.com Computer Science in Spain Leader Award
  • 2018 - Member of Academia Europaea

Overview

Pedro Larrañaga is affiliated with the Technical University of Madrid in Spain. Their primary field of study is Computer Science, with a focus on subfields such as Artificial Intelligence, Molecular Biology, Signal Processing, Control and Systems Engineering, and Computational Theory and Mathematics.

The scientist's research work centers on topics including Bayesian Modeling and Causal Inference, Bayesian Methods and Mixture Models, Data Stream Mining Techniques, Time Series Analysis and Forecasting, Machine Learning and Data Classification, Fault Detection and Control Systems, and Neural Networks and Applications.

Recent publications by Pedro Larrañaga include:

  • Bayesian networks for interpretable machine learning and optimization, 2021, Neurocomputing
  • Multi-dimensional Bayesian network classifiers: A survey, 2020, Artificial Intelligence Review
  • Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering, 2020, Engineering Applications of Artificial Intelligence
  • Long-term forecasting of multivariate time series in industrial furnaces with dynamic Gaussian Bayesian networks, 2021, Engineering Applications of Artificial Intelligence
  • Identifying Parkinson's disease subtypes with motor and non-motor symptoms via model-based multi-partition clustering, 2021, Scientific Reports

Pedro Larrañaga collaborates frequently with several co-authors, including:

  • Concha Bielza
  • Vicente P. Soloviev
  • Carlos Puerto-Santana
  • David Quesada
  • David Atienza

The scientist has published multiple works in prominent venues, notably:

  • Neurocomputing
  • arXiv (Cornell University)
  • Engineering Applications of Artificial Intelligence
  • Nature Neuroscience
  • International Journal of Intelligent Systems

In addition to journal articles, Pedro Larrañaga has a book publication titled Data-Driven Computational Neuroscience, published in 2020 by Cambridge University Press.

Pedro Larrañaga was recognized as a Member of Academia Europaea in 2018.

Best Publications

  • A review of feature selection techniques in bioinformatics

    Yvan Saeys;Iñaki Inza;Pedro Larrañaga

  • Estimation of Distribution Algorithms

    Pedro Larrañaga;Jose A. Lozano

  • An empirical comparison of four initialization methods for the K-Means algorithm

    J.M Peña;J.A Lozano;P Larrañaga

  • Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators

    P. Larrañaga;C. M. H. Kuijpers;R. H. Murga;I. Inza

  • Machine learning in bioinformatics

    Pedro Larrañaga;Borja Calvo;Roberto Santana;Concha Bielza

  • New insights into the classification and nomenclature of cortical GABAergic interneurons

    Javier DeFelipe;Pedro L. López-Cruz;Ruth Benavides-Piccione;Ruth Benavides-Piccione;Concha Bielza

  • A survey on multi-output regression

    Hanen Borchani;Gherardo Varando;Concha Bielza;Pedro Larrañaga

  • Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters

    P. Larranaga;M. Poza;Y. Yurramendi;R.H. Murga

  • Filter versus wrapper gene selection approaches in DNA microarray domains

    Iñaki Inza;Pedro Larrañaga;Rosa Blanco;Antonio J. Cerrolaza

  • Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)

    Jose A. Lozano;Pedro Larrañaga;Iñaki Inza;Endika Bengoetxea

  • Towards a New Evolutionary Computation

    Jose A. Lozano;Pedro Larrañaga;Iñaki Inza;Endika Bengoetxea

  • Learning Bayesian network structures by searching for the best ordering with genetic algorithms

    P. Larranaga;C.M.H. Kuijpers;R.H. Murga;Y. Yurramendi

  • Feature subset selection by Bayesian network-based optimization

    I. Inza;P. Larrañaga;R. Etxeberria;B. Sierra

  • Discrete Bayesian Network Classifiers: A Survey

    Concha Bielza;Pedro Larrañaga

  • Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks

    Pedro Larrañaga;Ramón Etxeberria;Jose Antonio Lozano;Jose Manuel Peña

  • Multi-dimensional classification with Bayesian networks

    C. Bielza;G. Li;P. Larraòaga

  • A community-based transcriptomics classification and nomenclature of neocortical cell types

    Rafael Yuste;Michael Hawrylycz;Nadia Aalling;Argel Aguilar-Valles

  • An Introduction to Probabilistic Graphical Models

    Pedro Larrañaga

  • A Review on Estimation of Distribution Algorithms

    Pedro Larrañaga

  • A review on evolutionary algorithms in Bayesian network learning and inference tasks

    Pedro LarrañAga;Hossein Karshenas;Concha Bielza;Roberto Santana

Frequent Co-Authors

Concha Bielza
Concha Bielza Technical University of Madrid
Jose A. Lozano
Jose A. Lozano Basque Center for Applied Mathematics
Javier DeFelipe
Javier DeFelipe Technical University of Madrid
Iñaki Inza
Iñaki Inza University of the Basque Country
José M. Peña
José M. Peña Lurtis Rules
Rafael Yuste
Rafael Yuste Columbia University
Manuel Graña
Manuel Graña University of the Basque Country
Oscar Marín
Oscar Marín King's College London
Gábor Tamás
Gábor Tamás University of Szeged

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