Phone: +34-943 740 600

E-mail: jdiaz(at)

Regular mail:

Ikergune A.I.E.

San Antolín, 3

20870 Elgoibar, Guipúzcoa, España



R&D; Project Manager. Ph.D. student.

Short Bio

Javier Díaz obtained an M.Eng. degree in Mechanical Engineering from the University of Los Andes, Bogotá, Colombia in 2001. Additionally, he obtained a M.Sc. degree in Advanced Manufacturing Technology and Systems Management from the University of Manchester, UK in 2003. Before joining Ikergune as a R&D; Project Manager responsible for the advanced manufacturing research area, he has gathered near 10 years of industrial experience working in different positions related with R&D;: Senior Consultant in a R&D; consulting firm 2010-2014, R&D; Director in a business group dedicated to the wind energy sector 2008-2010 and Director for the Advanced Manufacturing Area in the ASCAMM Technology Centre 2006-2008. Currently, he is also a Ph.D. student at the UPM’s Artificial Intelligence Department, taking part in the CDTI CARES project.

Research interests

His research interest is focused on machine learning algorithms related with novelty detection and spatio-temporal feature subset selection, with application to machine tool industry within the Industrie 4.0 paradigm.



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, accepted, 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., 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. 

Diaz-Rozo, J., J. Posada, I. Barandiaran, and C. Toro, “Recommendations for sustainability in production from a machine-tool manufacturer”, KES-SDM, 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. 

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