I am a Mathematician with a seven-year research career, beginning with a PhD in Engineering Sciences and continuing through several postdoctoral positions. This journey has contributed to improve my ability to formulate research problems, integrate existing knowledge and strive to find solutions.

Parallel to my research endeavors, I have continually found myself expanding this expertise and developing new skills that aligned with my growing interest in the field of AI and technology. I am interested in driving innovation and solving real-world problems.

In my current research project, I am applying SciML methods, e.g. classical end-to-end deep learning approaches, Physics-Informed Neural Networks (PINNs) and Neural Operators (PI-DeepONets) to solve Navier-Stokes equations. For this purpose, we use huge datasets of CFD simulations that serve as ground truth for the training and testing phase of the models. The goal is to predict the evolution of thoracic aortic pathologies at an early stage.

Topics of interest

• Applied Mathematics
• Scientific Machine Learning (SciML)
• Micromechanics
• Modeling of biological and bio-inspired materials
• AI, Machine Learning, Deep Learning
• MLOps

*Feel free to reach me out. I am looking forward to discuss how my experience, skills and motivation can contribute to your team. Connect with me on Linkedin or download my full CV .