Marta Núñez García

I am currently a "Ramon y Cajal" fellow at the CiTIUS (USC). My research focuses on medical image analysis and machine learning applied to medical data.

I am a telecommunication engineer from the Universidade de Vigo (Spain), I hold a MsC Degree in Computer Vision and Artificial Intelligence (Computer Vision Center, Barcelona, Spain) and a PhD in Medical Image Processing from the Universitat Pompeu Fabra (Barcelona, Spain). My PhD thesis focused on left atrial parameterization and multi-modal data analysis in the context of atrial fibrillation. From 2019 to 2023 I was a postdoctoral researcher at the IHU Liryc (L'Institut de Rythmologie et Modélisation Cardiaque, Bordeaux, France) and the Institut National de Recherche en Informatique et en Automatique (INRIA, Bordeaux) where I was awarded an INRIA Starting Research Position. I joined the CiTIUS in 2023 as a "Maria Zambrano" postdoctoral researcher. 

I investigate the use of machine learning techniques and classical image processing approaches applied to multimodal cardiac images such as MRI, LGE-MRI, CT, LIE-CT, etc. My main interest focuses on developing standardized multimodal cardiac models for the statistical analysis of cardiac data. I also lead the project Hacia una cardiología asistida por aprendizaje automático seguro y clínicamente relevante (Liña de reforzo de traxectorias emerxentes, Proxectos de Excelencia, 2025).

I co-lead the COLLAGE project (COmprehensive mL-based anaLysis of fetAl Growth trajEctories), which aims at developing safe, fair, and explainable ML tools for automated ultrasound-based fetal growth modeling for early detection of small and vulnerable babies. The project also aims to increase the impact of machine learning in medicine by investigating ways to provide clinicians with reliable, meaningful information that truly supports medical decision-making.