I hold a Ph.D. in Machine Learning applied to Computational Biology (University of Navarra), where I specialized in graph neural networks, representation learning, and causal inference. My work spanned multi-omics data integration (RNA-seq, single-cell, spatial transcriptomics) and led to publications in ICLR and Nature Machine Intelligence.

As a Fulbright Excellence Fellow 🇺🇸 at NYU’s Center for Data Science, I deepened my expertise in scalable ML systems and interdisciplinary research.

Currently, I focus on the design and development of predictive models for medium-frequency trading (MFT) strategies in the cryptocurrency derivatives market — combining statistical learning, time-series modeling, and signal extraction for trading automation.

I’m also a Kaggle Competitions Expert, passionate about solving complex applied ML problems and bridging research and real-world applications across finance, healthcare, and biotech.