Pedro Luis López-Cruz

Contact information

Laboratorio 3306

Departamento de Inteligencia Artificial

Facultad de Informática

Campus de Montegancedo

Universidad Politécnica de Madrid

28660 Boadilla del Monte, Madrid, España

Teléfono: +34-913363675

E-mail: pedro dot lcruz at upm dot es

pedrolcruz

 

Position

PhD in Artificial Intelligence.

Short Bio

Pedro L. López-Cruz received a Bachelor degree in Computer Science on 2008 by the University of Jaén and a Master degree in Artificial Intelligence on 2010 by the Technical University of Madrid (UPM). He received the National Award for University Students in Computer Science on 2010. He holds PhD in Artificial Intelligence by the UPM.

Research interests

  • Neuroscience: Neuronal morphology analysis and simulation. Neuronal classification.
  • Machine Learning: Probabilistic Graphical Models, Optimization and Heuristics. Supervised learning. Partially supervised learning.
  • Statistics: Directional and angular statistics. Mixtures of polynomials. Non-parametric density estimation. Consensus agreement and analysis.

Journal papers

  • López-Cruz PL, Larrañaga P, DeFelipe J and Bielza C (2014). Bayesian network modeling of the consensus between experts: An application to neuron classification, International Journal of Approximate Reasoning 55 (1): 3-22. PDF Supplementary material
  • López-Cruz PL, Bielza C and Larrañaga P (2013). Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation, International Journal of Approximate Reasoning, in press. PDF Supplementary material
  • López-Cruz PL, Bielza C and Larrañaga P (2013). Directional naive Bayes classifiers, Pattern Analysis and Applications, in press. PDF
  • DeFelipe J, López-Cruz PL, Benavides-Piccione R, Bielza C, Larrañaga P et al. (2013). New insights in the classification and nomenclature of cortical GABAergic interneurons, Nature Reviews Neuroscience 14 (3): 202-216. PDF Classification website
  • López-Cruz PL, Bielza C, Larrañaga P, Benavides-Piccione R and DeFelipe J (2011) Models and simulation of 3D neuronal dendritic trees using Bayesian networks, Neuroinformatics, 9(4): 347-369. PDF Supplementary material website

Conference papers

  • López-Cruz PL, Nielsen TD, Bielza C and Larrañaga P (2013). Learning mixtures of polynomials of conditional densities from data. In Advances in Artificial Intelligence, Proceedings of the 15th MultiConference of the Spanish Association for Artificial Intelligence, LNCS 8109, Springer: 363-372.PDF
  • López-Cruz PL, Bielza C and Larrañaga P (2013). Learning conditional linear Gaussian classifiers with probabilistic class labels. In Advances in Artificial Intelligence, Proceedings of the 15th MultiConference of the Spanish Association for Artificial Intelligence, LNCS 8109, Springer: 139-148.PDF
  • López-Cruz PL, Bielza C and Larrañaga P (2012). Learning mixtures of polynomials from data using B-spline interpolation. In Proceedings of the Sixth European Workshop on Probabilistic Graphical Models (PGM 2012): 271-278. Granada, 19-21 september 2012. PDF
  • López-Cruz PL, Bielza C and Larrañaga P (2011). The von Mises naive Bayes classifier for directional data. In Advances in Artificial Intelligence, Proceedings of the 14th Conference of the Spanish Association for Artificial Intelligence, LNCS 7023, Springer: 145-154. PDF Supplementary material website
  • López-Cruz PL, Bielza C, Larrañaga P, Benavides-Piccione R and DeFelipe J (2010) 3D simulation of dendritic morphology using Bayesian networks, In 16th Annual meeting of the Organization for Human Brain Mapping (HBM2010).
  • López-Cruz PL, Rivera AJ, Pérez-Godoy MD, del Jesus MJ and Carmona C (2009) EMORBFN: An Evolutionary Multiobjective Optimization algorithm for RBFN designm, In Proceedings of the 10th International Work-Conference on Artificial Neural Networks (IWANN 2009), Part I, LNCS 5517, Springer: 752–759. PDF

Software

  • Rmop: Multidimensional mixture of polynomials learning from data. R package. Rmop R package

Teaching