Greater San Sebastian Area
Developing a data-driven methodology for automatic calibration of structural feature extraction algorithms in Structural Health Monitoring (SHM) systems. • Optimizing Random Decrement Technique (RDT) parameters for robust free-response extraction from ambient vibration data • Improving modal parameter estimation using state-space System Identification (TDE, SVD) • Designing probabilistic and statistical models to automate parameter tuning • Developing online-compatible calibration strategies for scalable SHM deployment • Python-based implementation for batch and real-time data analysis
Developed a data-driven anomaly detection framework for bridge Structural Health Monitoring (SHM) based on Semi-Parametric Bayesian Networks (SPBN). Processed vibration-based modal features (natural frequencies & damping ratios) extracted from real-world infrastructure data and built probabilistic baseline models to detect structural deviations. Implemented structure learning (Hill-Climbing) and parameter learning combining Linear Gaussian and Kernel Density Estimation (KDE) nodes. Designed and evaluated Key Performance Indicators (log-likelihood based) for anomaly detection in operational monitoring scenarios. Contributed to RDM (Random Decrement Method) calibration and feature pipeline validation for real-time SHM deployments.
Worked in a fast-paced restaurant environment during seasonal periods. Responsible for food preparation, service coordination, and maintaining high quality standards under strict time constraints. Developed strong teamwork, discipline, stress management, and operational efficiency in high-pressure situations.
Designed and developed the hardware architecture of the Aingura Distributed Sensing Node, a core module of an industrial Structural Health Monitoring (SHM) system. • Developed high-frequency data acquisition hardware for vibration and dynamic measurements • Designed signal conditioning circuits for industrial sensors (IEPE and dynamic sensors) • Contributed to edge-ready architectures enabling real-time signal processing and analytics • Ensured data integrity and precision for downstream machine learning and probabilistic monitoring models • Collaborated with software and data teams to align hardware design with analytics requirements
Provided private tutoring in French and Basque to students of various levels. Designed personalized learning plans adapted to individual needs and objectives. Developed strong communication and pedagogical skills through one-on-one teaching.
Contributed to the development of a monitoring system for a 1406-panel photovoltaic power plant within a dual academic–industry program. • Designed and implemented data acquisition pipelines for real-time solar performance monitoring • Processed and analyzed operational time-series data to evaluate system efficiency and detect anomalies • Developed data-driven diagnostics to optimize energy production and identify underperforming panels • Integrated Industrial IoT solutions for scalable plant monitoring • Collaborated with engineering teams on performance modeling and system validation