David Agis Cherta

David Agis Cherta is an academic affiliated with the Universitat Politècnica de Catalunya (UPC), specifically the Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB) and the Departament d'Estadística i Investigació Operativa. He leads research in the CoDAlab - Control, Dades i Intel·ligència Artificial group and the WinTurCoM - Wind Turbine Condition Monitoring subgroup. His work spans structural health monitoring, wind energy systems, environmental health, and statistical modeling. Agis has published extensively in journals such as Sensors, Environmental Health Perspectives, and Methods in Ecology and Evolution.

Education & Research Focus: His doctoral thesis (2019) explored vibration-based structural health monitoring using piezoelectric transducers. Key areas include machine learning applications for fault detection in wind turbines, statistical methodologies for environmental data analysis, and interdisciplinary research at the intersection of engineering and public health.

Awards & Projects: Received the EACS Early Career Award in Structural Control & Health Monitoring (2020). Active in competitive research projects, including EU-funded initiatives on wind turbine condition monitoring and environmental pollution studies. Collaborates internationally, with projects focusing on digital twins, predictive maintenance, and data-driven applications in civil engineering.

Public Engagement: His research bridges technical innovation and societal impact, addressing issues like air quality effects of public transport strikes and long-term health impacts of urban pollution. He contributes to open-source tools like the Good R package for count data analysis.