DC 6 – Probabilistic failure risk assessment in ascending thoracic aortic aneurysms
Can you share a bit about your academic and professional journey?
I graduated from the Faculty of Engineering of the University of Porto, Portugal with a Bachelor’s and Master’s degree in Mechanical Engineering. During my studies, I became interested in biomechanics, computer vision, machine learning, and numerical modeling. As part of my bachelor’s thesis, I worked on MRI-based 3D modeling and numerical simulation for breast cancer diagnostics. For my master’s thesis, I pursued an Erasmus+ research project at KU Leuven’s Soft Tissue Biomechanics Group, where I worked on data-driven surrogate models for arterial wall behavior. This experience allowed me to deepen my knowledge and reinforce my interest in the field of computational biomechanics.
What motivated you to pursue a Doctoral candidacy within the Doctoral Network?
My motivation stems from a strong drive to continuously learn. I am also interested not only in the potential advancements in cardiovascular health that could be achieved with this research but also to the broader scientific community. I hope that my work can be useful to others exploring similar challenges in the future. Additionally, the interdisciplinary environment of the Doctoral Network—including collaborations, exchange of ideas, and opportunities for growth as a researcher—was a factor in motivating me to pursue this path as well.
What aspects of this training and research program are you most excited about?
I am especially excited about the hands-on aspects of the project. Working with coding and simulations to better understand biomechanical behaviors, extracting data from these simulations and applying statistical methods is something I find particularly engaging. I am also looking forward to collaborating with researchers from different backgrounds and gaining exposure to new scientific approaches. Additionally, I am eager to gain closer contact with lab-related biomechanical testing and medical imaging, allowing one to better understand how uncertainty propagates through biomechanical computational models and affects simulations.
