I’m interested in applying machine learning methods to solve challenges in the biomedical realm.
My background includes a blend of machine (and deep) learning modelling & bioinformatics, and I develop statistical and ML methods to identify blood-based molecular markers of Parkinson’s disease diagnosis and prognosis for my doctoral studies. I was recently awarded a fellowship to do a research visit in Cambridge, where I focused on exciting graph representation learning models (aka graph ML or geometric DL) with Pietro Liò (Cambridge Univeristy) and Petar Veličković (Google DeepMind).
Beyond research, I’m a fellow of the 2023 MIT Catalyst fellowhip program, where I explore the space of un-met needs in health(-tech) and I proudly serve as a global shaper recognized by the World Economic Forum of Geneva.
At work and in life, I thrive in collaborative, engaging atmospheres and enjoy sports, from padel tennis to running, with a special fondness for outdoor activities. While at Cambridge, I raced with the triathlon club the Varsity duathlon against Oxford - quite an experience! In my downtime, I enjoy indulging in a good book and the occasional slice of carrot cake with a glass of white wine.
Disclaimer: this site is currently under (active) construction!
Download my resumé.
PhD in Biomedical Data Science, 2024
University of Luxembourg
MSc in Bioinformatics, 2018
Universitat Autonoma de Barcelona
BSc in Biotechnology (computational), 2017
Universidad Politécnica de Madrid