RESEARCH
His research lies at the intersection of artificial intelligence and biomedicine, with a focus on understanding complex biological systems through computational modeling. He is particularly interested in how machine learning can help uncover the structure and function of cellular organization, especially in the context of spatial transcriptomics and single-cell technologies. His work explores how data integration and representation learning can contribute to a deeper understanding of disease mechanisms, from cancer to neurodegeneration. With a strong interest in both fundamental questions and practical applications, he continues to investigate how computational approaches can support discovery in biology and medicine.

BIOGRAPHY
He holds a Ph.D. in Applied Medicine and Biomedicine from the University of Navarra (2023, Cum Laude), along with Master’s degrees in Data Science and Big Data (2018), Biomedical Engineering (2015), and a Telecommunications Engineering degree specialized in Sound and Image (2013).
He began his research career at Navarrabiomed, where he contributed to a range of biomedical projects spanning proteomics, bioinformatics, and clinical trial informatics. Over the years, his focus has shifted toward AI-driven analysis of high-dimensional biological data, including work in spatial and single-cell transcriptomics. In parallel, he has developed and maintained clinical research infrastructures as a full-stack developer supporting national and international studies.
Currently a postdoctoral fellow at King Abdullah University of Science and Technology (KAUST), he works on applying computational methods to spatial biology. He was a member of the winning team in two of the challenges from the NeurIPS 2021 “Open Problems in Single-cell Analysis” competition, reflecting his continued engagement with cutting-edge developments in AI for life sciences.