Javier Diaz-Rozo



Phone: +34-943 740 600

E-mail: jdiaz@ainguraiiot.com

Regular mail:

Aingura IIoT S.L.

San Antolín, 3

20870 Elgoibar, Guipúzcoa, España


Chief Technology Officer, Aingura IIoT

Short Bio

Javier Diaz-Rozo received the M.Eng. degree in mechanical engineering from the University of Los Andes, Bogotá, Colombia, in 2001, the M.Sc. degree in advanced manufacturing technology and systems management from the University of Manchester, Manchester, U.K., in 2003, and the Ph.D. degree in artificial intelligence from Universidad Politécnica de Madrid, Madrid, Spain, in 2019. Currently, he is the Chief Technology Officer at Aingura IIoT, Elgoibar, Spain. He has accumulated nearly 15 years of industrial experience researching in different positions related with research and development: Research and Development Project Manager with Ikergune, the Etxe-Tar Group Research and Development Unit, responsible for the advanced manufacturing research area, Senior Consultant in a research and development consulting firm from 2010 to 2014, Research and Development Director in a business group mainly dedicated to the wind energy sector from 2008 to 2010, and the Director for the Advanced Manufacturing Area with the ASCAMM Technology Centre, Barcelona, Spain, from 2006 to 2008..

Research interests

His research interest is focused on probabilistic-based machine learning algorithms applied to data streams related with novelty detection and spatio-temporal feature subset selection, with application to production systems within the Industrie 4.0 paradigm and Industrial Internet of Things.


Díaz-Rozo, J., C. Bielza, and P.. Larrañaga, "Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering", Engineering Applications of Artificial Intelligence, vol. 89, pp. 103434, mar. 2020.
Díaz-Rozo, J., C. Bielza, and P. Larrañaga, "Clustering of Data Streams with Dynamic Gaussian Mixture Models. An IoT Application in Industrial Processes", IEEE Internet of Things Journal, vol. IEEE Internet of Things Journal, , issue 5, 5, pp. 3533--3547, 2018.
Díaz-Rozo, J., C. Bielza, and P.. Larrañaga, "Machine Learning-based CPS for Clustering High throughput Machining Cycle Conditions", Procedia Manufacturing Elsevier (no JCR), vol. 10, pp. 997-1008, 2017.
Owodunni, O. O., J. Diaz-Rozo, and S. Hinduja, "Development and Evaluation of a Low-Cost Computer Controlled Reconfigurable Rapid Tool", Computer-Aided Design and Applications, vol. 1, issue 1-4, pp. 101--108, 2004.

Workshops Publications

Ogbechie, A., J. Díaz-Rozo, P. Larrañaga, and C. Bielza, "Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment", ML4CPS - Machine Learning for Cyber ​​Physical Systems and Industry 4.0, Karlsruhe, Germany, Springer, 2016.
Diaz-Rozo, J., J. Posada, I. Barandiaran, and C. Toro, "Recommendations for sustainability in production from a machine-tool manufacturer", KES-SDM, 2015.
Diaz-Rozo, J., C. Bielza, J. Luis Ocaña, and P. Larrañaga, "Machine Learning for Cyber Physical Systems ML4CPS", Development of a Cyber-Physical System based on selective Gaussian naive Bayes model for a self-predict laser surface heat treatment process control: Springer Vieweg, 2015.
Ochoa, A., J. Diaz-Rozo, and B. Kamp, "Insights for Industry 4.0 Implementation in machine tool servitization", International Virtual Concept Workshop on INDUSTRIE 4.0, 2015.
Ochoa, A., J. Diaz-Rozo, and B. Kamp, "Challenges and opportunities for servitization in the machine tool industry in the era of Industry 4.0", 4th International Conference on Business Servitization, 2015.
Karshenas, H., R. Santana, C. Bielza, and P. Larrañaga, "Multi-Objective Optimization with Joint Probabilistic Modeling of Objectives and Variables", Lecture Notes in Computer Science, no. 6576: Springer, pp. 298-312, 2011.

Best Paper Award of the Conference Machine Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS) 2015