My name is Ahmed Shalabi, and I am a PhD candidate at TU Delft within the FOURIER project. I hold a B.Sc. in Civil Engineering from Mansoura University (Egypt) and a joint M.Sc. in Mathematical Modelling from Universität Hamburg and the University of L’Aquila.
Before joining TU Delft, I worked as a machine learning researcher in collaboration with INGV and the Gran Sasso Science Institute (GSSI), developing deep learning methods for seismic applications using CNNs and vision transformers. My work focused on domain-specific data augmentation, uncertainty quantification, and explainability, driven by a broader interest in building models that remain calibrated and trustworthy under noisy, sparse, or biased data.
At TU Delft, my PhD research develops AI-based surrogate modelling procedures to enable probabilistic, time- and state-continuous digital twins for infrastructure systems. Methodologically, I work on representation learning and uncertainty-aware modelling to extract robust information from scarce, noisy, and heterogeneous data. I focus on integrating real-time monitoring with multi-fidelity information sources, quantifying and propagating uncertainty from conflicting or incomplete inputs, and enabling decision support for inspection and maintenance planning.
I joined FOURIER for its strong interdisciplinary environment connecting AI, uncertainty-aware risk modelling, and engineering practice, and for its clear pathway to translating advanced methods into deployable tools for resilient infrastructure management.
