I conduct research at the convergence of artificial intelligence, hydrodynamics, and structural engineering to accelerate the digital transformation of marine and offshore structures. My work aims to build the next generation of physics-enhanced machine learning models capable of capturing complex ocean–structure interactions with high accuracy and reliability.
A core part of my research involves developing models that quantify and reduce uncertainty in design, operation, and failure prediction. I specialize in Uncertainty Quantification and predictive modelling, applied in combination with advanced hydrodynamic and structural simulation methods. My research contributes to safer, more robust, and autonomous marine systems, particularly in applications such as floating offshore wind, maritime autonomy, structural health monitoring, and digital twin frameworks.
By integrating domain physics, advanced computation, and data-driven intelligence, my long-term objective is to enhance decision-making, improve reliability, and support the sustainable development of offshore renewable energy and marine technologies.
