Alberto Diez Olivan

PhD

Alberto Diez Oliván obtained a Computer Science Engineering degree at Universidad del País Vasco (UPV–EHU) in 2006 and a Master of Science in Computer Sciences and Artificial Intelligence in 2009 by the same University. He received his Ph.D. degree in Robotics and Automation from the Universidad Politécnica de Madrid (ETSII-UPM) in 2017, being his thesis related to machine learning methods and applications for data-driven prognostics. He entered Fatronik with Iñaki Goenaga Research Fellowship in 2006 and in 2015 he did an internship of 6 months at the NICTA’s machine learning research group (Sydney, Australia). Nowadays he works as an experienced data scientist in the TECNALIA’s Industry & Transport Division. He has been involved in the design and development of intelligent applications in several important projects, both public-funded R&D at European and National level and with industrial customers, targeting important sectors such as maritime, renewable energy, railway, agro-food, civil structures and machine-tool.


CONTACT INFORMATION:

PUBLICATIONS

  • A. Carrascal, A. Diez-Olivan, and A. Azpeitia, “Unsupervised methods for anomalies detection through intelligent monitoring systems,” in International conference on hybrid artificial intelligence systems, 2009, p. 137–144.
    [Bibtex]
    @inproceedings{carrascal2009unsupervised,
    title={Unsupervised methods for anomalies detection through intelligent monitoring systems},
    author={Carrascal, Alberto and Diez-Olivan, Alberto and Azpeitia, Ander},
    booktitle={International Conference on Hybrid Artificial Intelligence Systems},
    pages={137--144},
    year={2009},
    organization={Springer, Berlin, Heidelberg}
    }
  • A. Diez-Olivan, J. A. Pagan, R. Sanz, and B. Sierra, “Data-driven prognostics using a combination of constrained k-means clustering, fuzzy modeling and lof-based score,” Neurocomputing, vol. 241, p. 97–107, 2017.
    [Bibtex]
    @article{diez2017data,
    title={Data-driven prognostics using a combination of constrained K-means clustering, fuzzy modeling and LOF-based score},
    author={Diez-Olivan, Alberto and Pagan, Jose A and Sanz, Ricardo and Sierra, Basilio},
    journal={Neurocomputing},
    volume={241},
    pages={97--107},
    year={2017},
    publisher={Elsevier}
    }
  • A. Diez-Olivan, M. Penalva, F. Veiga, L. Deitert, R. Sanz, and B. Sierra, “Kernel density-based pattern classification in blind fasteners installation,” in Hybrid artificial intelligent systems, 2017, p. 195.
    [Bibtex]
    @inproceedings{diez2017kernel,
    title={Kernel density-based pattern classification in blind fasteners installation},
    author={Diez-Olivan, Alberto and Penalva, Mariluz and Veiga, Fernando and Deitert, Lutz and Sanz, Ricardo and Sierra, Basilio},
    booktitle={Hybrid Artificial Intelligent Systems},
    pages={195},
    year={2017},
    organization={Springer}
    }
  • A. Diez-Olivan, J. A. Pagan, N. L. D. Khoa, R. Sanz, and B. Sierra, “Kernel-based support vector machines for automated health status assessment in monitoring sensor data,” The international journal of advanced manufacturing technology, vol. 95, iss. 1-4, p. 327–340, 2018.
    [Bibtex]
    @article{diez2018kernel,
    title={Kernel-based support vector machines for automated health status assessment in monitoring sensor data},
    author={Diez-Olivan, Alberto and Pagan, Jose A and Khoa, Nguyen Lu Dang and Sanz, Ricardo and Sierra, Basilio},
    journal={The International Journal of Advanced Manufacturing Technology},
    volume={95},
    number={1-4},
    pages={327--340},
    year={2018},
    publisher={Springer London}
    }
  • J. Azpiazu, S. Siltanen, P. Multanen, A. Mäkiranta, N. Barrena, A. Diez-Olivan, J. Agirre, and T. Smith, “Remote support for maintenance tasks by the use of augmented reality: the manuvar project,” in Carvi 2011: ix congress on virtual reality applications, 2011, p. 1–6.
    [Bibtex]
    @inproceedings{azpiazu2011remote,
    title={Remote support for maintenance tasks by the use of Augmented Reality: the ManuVAR project},
    author={Azpiazu, J and Siltanen, S and Multanen, Petteri and M{\"a}kiranta, Ari and Barrena, N and Diez-Olivan, A and Agirre, J and Smith, T},
    booktitle={CARVI 2011: IX Congress on virtual reality applications},
    pages={1--6},
    year={2011},
    organization={VTT}
    }
  • A. Diez-Olivan, X. Averós, R. Sanz, B. Sierra, and I. Estevez, “Quantile regression forests-based modeling and environmental indicators for decision support in broiler farming,” Computers and electronics in agriculture, vol. 161, p. 141–150, 2019.
    [Bibtex]
    @article{diez2019quantile,
    title={Quantile regression forests-based modeling and environmental indicators for decision support in broiler farming},
    author={Diez-Olivan, Alberto and Aver{\'o}s, Xavier and Sanz, Ricardo and Sierra, Basilio and Estevez, Inma},
    journal={Computers and Electronics in Agriculture},
    volume={161},
    pages={141--150},
    year={2019},
    publisher={Elsevier}
    }
  • A. Carrascal, A. Diez-Olivan, J. Font, and D. Manrique, “Evolutionary generation of fuzzy knowledge bases for diagnosing monitored railway systems,” in 22nd international congress on condition monitoring and diagnostic engineering management (comadem 2009), 2009, p. 191–198.
    [Bibtex]
    @inproceedings{carrascal2009evolutionary,
    title={Evolutionary generation of fuzzy knowledge bases for diagnosing monitored railway systems},
    author={Carrascal, A and Diez-Olivan, A and Font, JM and Manrique, D},
    booktitle={22nd International congress on condition monitoring and diagnostic engineering management (COMADEM 2009)},
    pages={191--198},
    year={2009}
    }
  • A. Diez-Olivan, J. A. Pagan, R. Sanz, and B. Sierra, “Deep evolutionary modeling of condition monitoring data in marine propulsion systems,” Soft computing, vol. 23, iss. 20, p. 9937–9953, 2019.
    [Bibtex]
    @article{diez2019deep,
    title={Deep evolutionary modeling of condition monitoring data in marine propulsion systems},
    author={Diez-Olivan, Alberto and Pagan, Jose A and Sanz, Ricardo and Sierra, Basilio},
    journal={Soft Computing},
    volume={23},
    number={20},
    pages={9937--9953},
    year={2019},
    publisher={Springer Berlin Heidelberg}
    }
  • A. Diez-Olivan, J. Del Ser, D. Galar, and B. Sierra, “Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0,” Information fusion, vol. 50, p. 92–111, 2019.
    [Bibtex]
    @article{diez2019data,
    title={Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0},
    author={Diez-Olivan, Alberto and Del Ser, Javier and Galar, Diego and Sierra, Basilio},
    journal={Information Fusion},
    volume={50},
    pages={92--111},
    year={2019},
    publisher={Elsevier}
    }
  • J. Camacho, F. Veiga, M. L. Penalva, A. Diez-Olivan, L. Deitert, and N. López de Lacalle, “On-line monitoring of blind fastener installation process,” Materials, vol. 12, iss. 7, p. 1157, 2019.
    [Bibtex]
    @article{camacho2019line,
    title={On-Line Monitoring of Blind Fastener Installation Process},
    author={Camacho, Javier and Veiga, Fernando and Penalva, Mari Luz and Diez-Olivan, Alberto and Deitert, Lutz and L{\'o}pez de Lacalle, Norberto},
    journal={Materials},
    volume={12},
    number={7},
    pages={1157},
    year={2019},
    publisher={Multidisciplinary Digital Publishing Institute}
    }
  • P. Ortego, A. Diez-Olivan, J. Del Ser, F. Veiga, M. Penalva, and B. Sierra, “Evolutionary lstm-fcn networks for pattern classification in industrial processes,” Swarm and evolutionary computation, vol. 54, p. 100650, 2020.
    [Bibtex]
    @article{ortego2020evolutionary,
    title={Evolutionary LSTM-FCN networks for pattern classification in industrial processes},
    author={Ortego, Patxi and Diez-Olivan, Alberto and Del Ser, Javier and Veiga, Fernando and Penalva, Mariluz and Sierra, Basilio},
    journal={Swarm and Evolutionary Computation},
    volume={54},
    pages={100650},
    year={2020},
    publisher={Elsevier}
    }
  • A. Diez-Olivan, “Machine learning for data-driven prognostics: methods and applications,” , 2017.
    [Bibtex]
    @article{diez2017machine,
    title={Machine Learning for Data-driven Prognostics: Methods and Applications},
    author={Diez-Olivan, Alberto},
    year={2017},
    publisher={ETSI Industriales (UPM)}
    }
  • M. Murua-Etxeberria, F. Veiga-Suarez, J. Ortega-Lalmolda, M. Penalva-Oscoz, and A. Diez-Olivan, “Machine learning-based analysis engine to identify critical in multi-stage processes: application to the installation of blind fasteners,” Dyna, vol. 95, iss. 5, p. 534–540, 2020.
    [Bibtex]
    @article{murua2020machine,
    title={Machine learning-based analysis engine to identify critical in multi-stage processes: application to the installation of blind fasteners},
    author={Murua-Etxeberria, Maialen and Veiga-Suarez, Fernando and Ortega-Lalmolda, Juan-Antonio and Penalva-Oscoz, Mariluz and Diez-Olivan, Alberto},
    journal={DYNA},
    volume={95},
    number={5},
    pages={534--540},
    year={2020},
    publisher={FEDERACION ASOCIACIONES INGENIEROS INDUSTRIALES ESPANA ALAMEDA DE MAZARREDO~…}
    }
  • P. Ortego, A. Diez-Olivan, J. Del Ser, and B. Sierra, “Data augmentation for industrial prognosis using generative adversarial networks,” in International conference on intelligent data engineering and automated learning, 2020, p. 113–122.
    [Bibtex]
    @inproceedings{ortego2020data,
    title={Data Augmentation for Industrial Prognosis Using Generative Adversarial Networks},
    author={Ortego, Patxi and Diez-Olivan, Alberto and Del Ser, Javier and Sierra, Basilio},
    booktitle={International Conference on Intelligent Data Engineering and Automated Learning},
    pages={113--122},
    year={2020},
    organization={Springer, Cham}
    }
  • A. Diez-Olivan, J. Del Ser, D. Galar, and B. Sierra, “Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0,” Information fusion, vol. 50, p. 92–111, 2019.
    [Bibtex]
    @article{diez2019data,
    title={Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0},
    author={Diez-Olivan, Alberto and Del Ser, Javier and Galar, Diego and Sierra, Basilio},
    journal={Information Fusion},
    volume={50},
    pages={92--111},
    year={2019},
    publisher={Elsevier}
    }
  • P. Ortego, A. Diez-Olivan, J. Del Ser, F. Veiga, M. Penalva, and B. Sierra, “Evolutionary lstm-fcn networks for pattern classification in industrial processes,” Swarm and evolutionary computation, vol. 54, p. 100650, 2020.
    [Bibtex]
    @article{ortego2020evolutionary,
    title={Evolutionary LSTM-FCN networks for pattern classification in industrial processes},
    author={Ortego, Patxi and Diez-Olivan, Alberto and Del Ser, Javier and Veiga, Fernando and Penalva, Mariluz and Sierra, Basilio},
    journal={Swarm and Evolutionary Computation},
    volume={54},
    pages={100650},
    year={2020},
    publisher={Elsevier}
    }
  • P. Ortego, A. Diez-Olivan, J. Del Ser, and B. Sierra, “Data augmentation for industrial prognosis using generative adversarial networks,” in International conference on intelligent data engineering and automated learning, 2020, p. 113–122.
    [Bibtex]
    @inproceedings{ortego2020data,
    title={Data Augmentation for Industrial Prognosis Using Generative Adversarial Networks},
    author={Ortego, Patxi and Diez-Olivan, Alberto and Del Ser, Javier and Sierra, Basilio},
    booktitle={International Conference on Intelligent Data Engineering and Automated Learning},
    pages={113--122},
    year={2020},
    organization={Springer, Cham}
    }