As a Ph.D. As a student at the Ecole des Mines de Saint-Etienne, my research revolves around the relationship between machine learning, biomechanics and cardiovascular dynamics. I am deeply interested in two main areas: First, I focus on developing data-driven tools aimed at diagnosing cardiovascular diseases more effectively. I am trying to obtain valuable information from medical data to increase diagnostic accuracy with machine learning by generating large data base. Secondly, I focus on studies in the use of Physics-Informed Neural Networks (PINNs) and the improvement of existing PINN models to simulate and analyze blood flow dynamics within arteries and veins. It involves using advanced computational techniques to model how blood moves through these important pathways. I also research the field of image recording and work in the field of medical imaging and computational analysis. Overall, my research involves a multidisciplinary approach that aims to improve our understanding of cardiovascular health while developing diagnostic and simulation methodologies.

My experience includes advising graduate students in machine learning and image restoration. Through this role, I aim to provide guidance and support as students explore these complex topics. My goal is to create a supportive learning environment where students have the freedom to explore and develop their understanding of these topics.

• Research Gate