Javier (Javi) Del Ser was born in Barakaldo (Spain) in 1979. He joined the Faculty of Engineering (ETSI, www.ingeniaritza-bilbao.ehu.es) of the University of the Basque Country (Spain) to study Electrical Engineering, obtaining his combined B.S. and M.S. degree in May 2003. After finishing this degree, he became a recipient of the Fundacion de Centros Tecnologicos Inaki Goenaga doctoral grant. He received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude, Extraordinary Prize) from the University of Alcala, Spain, in 2013. Currently he is a principal researcher in data analytics and optimization at TECNALIA (Spain), and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 200 scientific articles, co-supervised 6 Ph.D. theses (+ 7 ongoing), edited 4 books, co-authored 6 patents and participated/led more than 35 research projects. He has also been involved in the organization of various national and international conferences. He is a senior member of the IEEE, and a recipient of the Bizkaia Talent prize for his research career.
PUBLICATIONS
- D. Alberto and A. Carrascal, “A multiclassifier approach for drill wear prediction,” in International workshop on machine learning and data mining in pattern recognition, 2012, p. 617–630.
[Bibtex] - 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] - D. Alberto, N. L. D. Khoa, M. M. Alamdari, Y. Wang, F. Chen, and P. Runcie, “A clustering approach for structural health monitoring on bridges,” Journal of civil structural health monitoring, p. 1–17, 2016.
[Bibtex] - D. F. M. A. . . J. M. . . D. Carrascal A., “Generación evolutiva de bases de conocimiento difusas para el diagnóstico de sistemas ferroviarios monitorizados,” Mantenimiento: ingenier\’\ia industrial y de edificios, iss. 234, p. 13–17, 2010.
[Bibtex] - N. Galarza, B. Rubio, D. Alberto, F. Boto, D. Gil, J. Rubio, E. Moreno, and others, “Implementation of signal processing methods in a structural health monitoring (shm) system based on ultrasonic guided waves for defect detection in different materials and structures,” The e-journal of nondestructive testing & ultrasonics, 2016.
[Bibtex] - 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] - 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] - T. Smith, D. A, N. Barrena, J. Azpiazu, and A. J. Ibarbia, “Remote maintenance support in the railway industry,” in Joint virtual reality conference (jvrc2011), 2011, p. 20–21.
[Bibtex] - 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] - 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] - M. Leva, A. Kay, T. Smith, D. A, J. Azpiazu, P. Liston, and S. Cromie, “The use of augmented reality to support maintenance: human factors issues and advantages,” in ‘irish ergonomics society annual conference 2011’, 2011, p. 37–47.
[Bibtex] - A. W. Colombo, J. Barata, L. Ribeiro, J. A. Ibarbia, and D. Alberto, “Una arquitectura orientada a servicios para la automatización flexible,” Imhe: información de máquinas-herramienta, equipos y accesorios, iss. 421, p. 32–39, 2015.
[Bibtex] - 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] - 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] - 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] - 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] - 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] - 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] - A. Diez-Olivan, “Machine learning for data-driven prognostics: methods and applications,” , 2017.
[Bibtex] - 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] - 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]