Juan Placer

Background

About Juan Placer

I work at the intersection of genomics, multi-omics data integration, and applied machine learning in biomedical research.My work focuses on integrating genomic sequencing data (including WGS, exome sequencing, and targeted panels) with other omics and clinical datasets according to study design requirements. These datasets are used to investigate disease mechanisms and to develop machine learning models for disease profiling, biomedical signal analysis, and image-based diagnostics.I contribute to the design of computational and data strategies that combine genomics, clinical data, and statistical learning methods. This work includes developing analytical frameworks and data architectures that support the processing, integration, and analysis of large-scale biomedical datasets.Areas of work include: • Integration of genomic, epigenetic, and clinical datasets • Development and evaluation of machine learning and deep learning models in biomedical contexts • Architectures for large-scale omics data processing • Implementation of MLOps workflows for model monitoring, reproducibility, and lifecycle management • Data governance and responsible use of AI in biomedical research and clinical data environmentsMy role often spans from study or system design to technical implementation, translating research workflows into computational pipelines and analytical tools that can be evaluated in operational environments. My work focuses on building analytical systems and computational models; the clinical utility of these approaches depends on rigorous validation within appropriate clinical study designs.I collaborate with hospitals, public-sector institutions, and biotechnology organizations on projects involving genomic analysis, clinical data integration, and computational modeling. I also contribute to training programs focused on the practical application of machine learning and computer vision in biomedical settings.I participate in technical discussions with clinicians, engineers, research teams, and institutional stakeholders to help align scientific objectives, data infrastructure, and analytical methodologies.