Anastasios Stamou

I hold a five-year Diploma in Civil Engineering from the National Technical University of Athens (NTUA), specializing in geotechnical engineering, with a diploma thesis on structural health monitoring using wavelet-based methods. I also earned an MSc cum laude from TU Delft, focusing on hydraulic and offshore structures, with a master’s thesis on improving soil liquefaction assessment through advanced analytical and numerical approaches. During my studies, I developed skills in Data Science and Machine Learning for engineering, bridging physical modeling and data-driven methods. My research experience spans geotechnical and structural dynamics, signal processing, and physics-informed machine learning.

My doctoral research focuses on Artificial Intelligence methods for solving inverse problems and updating digital twins. I work on physics-informed, reliability-aware machine learning approaches that integrate physical laws with data to detect structural damage, quantify uncertainties, and track evolving system parameters. The aim is to develop robust models that remain reliable under sparse, noisy, or partially observed data.

I joined the FOURIER project as a PhD candidate due to its integration of physics-based modeling, uncertainty quantification, and AI within a collaborative European research environment. The project’s emphasis on real-world engineering applications aligns with my interests and long-term goal of advancing trustworthy AI solutions for infrastructure monitoring, assessment, and decision-making.