PhD candidate in Statistics and Optimization at the Universitat Politècnica de València (UPV), specialising in multi-omics data integration, statistical modelling and machine learning for translational bioinformatics, with applications in biomarker discovery, disease mechanism characterisation and precision medicine. His doctoral research integrates metabolomic, microbiome, proteomic, transcriptomic and clinical data to investigate metabolic risk and support biomarker discovery, particularly in Polycystic Ovary Syndrome (PCOS); in parallel, he applies multi-omics approaches (volatilome and transcriptome) to plant systems. MSc degrees in Bioinformatics & Biostatistics and in Biomedical Engineering (Universitat de Barcelona / Universitat Oberta de Catalunya). Bioinformatician at Laboratorio Biomolecular, where he designs and maintains HPC-based whole-exome sequencing (WES) pipelines (R/Bash), including variant prioritisation under ACMG guidelines and clinical interpretation for rare disease diagnostics. Previously he worked in Barcelona (IBEC, IDIBAPS) and in international collaborations (KAUST), on biomedical signal analysis, foodomics and applied machine learning. Part-time professor at the Universidad Politécnica Salesiana (Cuenca), teaching Artificial Intelligence in Biomedical Engineering, Biostatistics, Biology and Molecular Genetics and Bioinformatics. GitHub: https://github.com/eddgeag (Description curated from publicly available data.)
