Healthcare AI and precision medicine, built for clinical decisions.
Architecting precision biotech frameworks that leverage AI (ML), Computer Vision, and multi-omic data integration amongst many other biotech solutions, for multilayered disease profiling—delivering customized solutions that enhance clinical diagnostics, strengthen patient autonomy, and empower decision-making by HCLS institution leaders.
Predicting who is at risk and knowing what to do about it are different questions. I build for the second.
"I believe biotech and healthcare should put people first—using technology not to replace human care, but to deepen it".
My daily work combines personalized medicine, machine learning, "Panomics" and computer vision to restore the essence of the doctor–patient relationship while delivering precise, evidence‒based, and affordable care. I began by supporting health and life sciences startups, discovering the transformative power of AI in real-world healthcare.
As Scientific & Data Officer for the Genetics division at Origen Corp., I define scientific and data strategy at the convergence of genomics, clinical data, and applied machine learning, translating complex biomedical problems into deployable technological solutions for the Health & Life Sciences ecosystem. I help governments, hospitals, and health organizations turn fragmented biomedical data into integrated, decision-ready systems.
My work connects scientific research, data engineering, and product development. I integrate multi-omic datasets with Whole Genome Sequencing (WGS) to investigate disease mechanisms and develop machine learning and deep learning models for high-resolution disease profiling, image-based diagnostics, and biomedical signal interpretation. These models generate clinically actionable insights that support decision-making, including pharmacogenetic treatment optimization.
Our approach combines bioinformatics expertise with specialized biomarker measurement partners. Predictive models are continuously validated by a multidisciplinary scientific committee and updated with emerging evidence from non-coding genomic regions and spatial transcriptomics, enabling discovery of tissue-specific biomarker patterns.
Through collaboration with a leading cloud provider, where I serve as Product Owner for the Health & Life Sciences line, I help develop precision medicine platform that generate personalized recommendations from genomic data, identify biological aging profiles, and support targeted longevity interventions. A domain-specific scientific crawler continuously updates our genomic "knowlake" base with peer-reviewed research.
I also contribute to public sector projects, hospital collaborations, and biotech initiatives, delivering training on practical machine learning and computer vision adoption and participating in technical strategy discussions with institutional stakeholders.
The objective: extend diagnostic capabilities beyond conventional limits, enable personalized therapies, and improve clinical outcomes, with the reasoning behind every recommendation traceable and open to challenge.
Method: what happens before a model exists.
Before a model is built, I help investigators, specialists in the HCLS environment, define the decision that has to change and the effect that must be identified to support it. It sounds like a nuance; it is the difference between a system that describes what already happens and one that can change it — predicting who is at risk and knowing which action reduces that risk are different questions, with different data and different validation.
Explicit phases, explicit criteria
Including what the data cannot answer
I work under a protocol buitl during years of experience, published, continously fed and versioned, that turns this into explicit phases and decision criteria: what is being estimated, where the mechanism is measured, what evidence supports it, and which questions the available data cannot answer. Stating that last part before anything is built is often the most valuable thing delivered. When everything flows together with the same design language, that's exactly how you get those scientific results.
Interdisciplinary professional operating at the intersection of biotechnology, artificial intelligence, and precision healthcare. He holds a fully completed medical school education (6-year curriculum, 360+ ECTS / EQF Level 7 equivalent) along with U.S. academic equivalency evaluated under AACRAO standards — qualifications formally vetted and accepted in public procurement tenders within the Spanish Public Administration. Specialized in genomic data integration, clinical informatics, and precision healthcare systems. As a certified ML Engineer and member of IAAA, LatinXAI, and the Spanish Society of Genetics, I work globally, consulting speaking, and building solutions that bring my vision of truly human-centered healthcare to life. A lifelong interest in music inspired me to help nurture Meloom, a company created to take music learning to a higher level and to help improve health and wellbeing.