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Ekhi Zugasti Uriguen

Researcher & Lecturer in AI and Data Science

Education

PhD in Civil Engineering

Universitat Politècnica de Catalunya (UPC)
2009 - 2014

Master in New Materials

University of the Basque Country (UPV/EHU)
2009 - 2010

Telecommunications Engineering

Mondragon University
2003 - 2009

Languages

  • Euskera (C2 (Native))
  • Spanish (C2 (Native))
  • English (C1)
  • French (B2)

Research Interests

  • Industrial AI
  • Machine Learning
  • Model Explainability
  • Prognostic Health Management
  • Computer Vision

About Theme

Career Profile

Experienced researcher and lecturer specializing in Data Science and Artificial Intelligence, with a focus on industrial applications. Currently serving as PDI at the University of the Basque Country (UPV/EHU) in the Department of Electronic Technology. Previously worked at Mondragon University (2017-2024) leading AI research projects and teaching in both undergraduate and graduate programs. My research interests span industrial AI, predictive maintenance, and model explainability, with significant experience in developing algorithms for monitoring systems and structural health monitoring.

Professional Experience

PDI (Teaching and Research Staff)

2024 - Present
University of the Basque Country (UPV/EHU)

Department of Electronic Technology at the School of Engineering of Vitoria-Gasteiz.

Researcher and Lecturer

2017 - 2024
Mondragon University
  • Led research in Artificial Intelligence and Data Science
  • Taught AI courses in Computer Science Bachelor’s and Data Analytics Master’s programs
  • Supervised multiple PhD students and research projects
  • Coordinated industrial collaboration projects
  • Courses taught:
    • Advanced Machine Learning (Master’s level)
    • Artificial Intelligence (Bachelor’s level)
    • Fundamentals of Communication Networks
  • Led various research projects in industrial AI and predictive maintenance

Researcher

2015 - 2017
Ikerlan

Focused on industrial monitoring and data analysis projects in the Control and Monitoring Department.

Research Scientist - PhD Candidate

2009 - 2014
Ikerlan

Worked in the Sensors Department, focusing on Structural Health Monitoring research and development.

Notable Research Projects

Selection of significant research projects as Principal Investigator or Key Researcher:

AUTOTRUST - Autonomous Mobility through Explainability and AI Evaluation Technologies
Twin4Crane - Digital Models for Crane Vision Systems
DOMUSAI - Intelligent Biomass Boilers using AI Techniques
QU4LITY - Digital Reality in Zero Defect Manufacturing
PROPHESY - Platform for self-configuring predictive maintenance services

Selected Publications

Google Scholar H-index: 10 Selected peer-reviewed publications in international journals and conferences, access my publications in https://github.com/pekhines/Papers/tree/674466a2a73362ac912a68941ca3d3cb869f8daf/Papers:

  • A Methodology for Advanced Manufacturing Defect Detection through Self-Supervised Learning on X-ray Images
    • Intxausti, E., Skocaj, D., Cernuda, C., Zugasti, E.
  • Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation
    • Barber, S., Izagirre, U., Serradilla, O., Olaizola, J., Zugasti, E., et al.
  • Diagnostic spatio-temporal transformer with faithful encoding
    • Labaien, J., Zugasti, E., Tsuyoshi, I., Chen, P.Y., De Carlos, X.
  • Predicting the effect of voids generated during RTM on the low-velocity impact behaviour by machine learning-based surrogate models
    • Mendikute, J., Baskaran, M., Llavori, I., Zugasti, E., Aretxabaleta, L., Aurrekoetxea, J.
  • Deep learning models for predictive maintenance a survey, comparison, challenges and prospects
    • Serradilla, O., Zugasti, E., Rodriguez, J., Zurutuza, U.
  • Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledge
    • Serradilla, O., Zugasti, E., Ramirez, J., Rodriguez, J., Zurutuza, U.
  • Adaptable and Explainable Predictive Maintenance Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine Data
    • Serradilla, O., Zugasti, E., Ramirez, J., Rodriguez, J., Zurutuza, U.
  • Impregnation quality diagnosis in Resin Transfer Moulding by machine learning
    • Mendikute, J., Plazaola, J., Baskaran, M., Zugasti, E., Aretxabaleta, L., Aurrekoetxea, J.

    Technical Skills

    Machine Learning & Deep Learning

    Industrial AI

    Prognostic Health Management

    Computer Vision

    Explainable AI

    Python

    Data Analysis