About
I am an AI Research Scientist and PhD candidate in Artificial Intelligence at Universidad CEU San Pablo, where I develop machine learning methods for computational biology and drug discovery. My work sits at the intersection of artificial intelligence, biology and software engineering. I enjoy building systems that transform large biological datasets into practical tools that help researchers make better decisions and accelerate scientific discovery.
Research Interests
My current research focuses on representation learning and foundation models for biological data. I am particularly interested in developing machine learning systems that can learn meaningful representations from molecular, genomic and biomedical information without relying on extensive manual annotation.
More recently, my work has expanded toward agentic AI systems capable of assisting scientists throughout the research process by integrating literature search, data analysis, hypothesis generation and computational experimentation.
Background
Physics
My background in physics developed a strong quantitative approach to solving complex problems and a solid foundation in mathematical modeling and data analysis.
Synthetic Biology
Laboratory research introduced me to the challenges faced by experimental scientists and sparked my interest in applying AI to accelerate biological research.
Artificial Intelligence
Today I combine deep learning, software engineering and computational biology to build AI systems capable of supporting scientific discovery.
Research Philosophy
I believe the next major advances in biomedical AI will come from systems that combine foundation models, reasoning capabilities and scientific workflows rather than isolated predictive models.
Instead of replacing researchers, these systems should augment scientific thinking by automating repetitive computational tasks, integrating information across multiple data sources and helping scientists explore hypotheses more efficiently while keeping humans firmly in control of the research process.
Current Goals
- Develop foundation models for biological and chemical data.
- Create agentic AI systems for scientific research.
- Bridge modern machine learning with practical biomedical applications.
- Contribute to AI technologies that accelerate drug discovery.
- Collaborate with multidisciplinary research teams tackling complex biological problems.
Outside Research
I enjoy communicating advances in artificial intelligence through technical writing and educational content, particularly around AI for Biology, foundation models and the future of scientific AI. I believe that making complex ideas accessible is an essential part of scientific progress.