About me

I’m a machine learning researcher with a Ph.D. in Machine Learning applied to Computational Biology from the University of Navarra. Throughout my doctoral journey, I had the opportunity to dive deep into the world of graph learning—working with knowledge graphs and graph neural networks—and explore a range of representation learning methods, including variational autoencoders and causal frameworks. A big part of my research focused on integrating and analyzing multi-omics data, from bulk RNA-seq and single-cell RNA-seq (scRNA-seq) to spatial transcriptomics and Perturb-seq.

These efforts resulted in numerous publications in top machine learning conferences and journals, such as ICLR and Nature Machine Intelligence. I was also selected as a Fulbright Excellence Fellow 🇺🇸, which allowed me to carry out part of my research at the Center for Data Science at New York University—an incredibly formative and inspiring experience.

These days, my work is centered on the intersection of machine learning and quantitative finance. I’m focused on the design and development of predictive models for MFT (medium-frequency trading) strategies in the cryptocurrency derivatives market.

Outside of work, I love taking on Kaggle challenges and am proud to be a Kaggle Competitions Expert. I’m passionate about solving complex problems and enjoy collaborating across disciplines—whether it’s in biology, healthcare, or finance.