About me

I am a Research Fellow (César Nombela Talent Attraction) at Universidad Carlos III de Madrid (UC3M), where I am a member of the Department of Signal Theory and Communications and the Machine Learning for Data Science (ML4DS) research group. My research focuses on developing interpretable machine learning methods for complex biomedical data, with a particular interest in multimodal learning and structured representations that capture clinically meaningful sources of variability.

Methodologically, my work lies at the intersection of representation learning, probabilistic modelling, and deep learning. Bayesian modelling is a major pillar of my research, providing the means to incorporate prior knowledge, quantifying uncertainty, and learning interpretable latent representations. I am particularly interested in models that integrate heterogeneous sources of information, accommodate missing and longitudinal data, and uncover the underlying factors driving their predictions. More recently, I have been exploring how these ideas can be combined with modern deep learning and foundation models to build more flexible yet still interpretable models for biomedical data.

My research is interdisciplinary, with applications spanning neuroimaging, microbiology, cardiology, and genomics. I work closely with researchers and domain experts to develop machine learning methods driven by real biomedical challenges. This work has led to collaborations with researchers at University College London, ETH Zürich, Aalto University, and the University of Eastern Finland.