The Castellón-Aprendizaje node is comprised of researchers from the AFAIAE group at the UJI, as well as collaborators from other research groups and universities who work alongside them. Their common thread is the research and application of statistical learning methods in various branches of the life sciences—primarily biomechanics, medicine, and neuroscience—with a special focus on processing complex data, such as geometric shapes or signals. The node is known for developing cutting-edge mathematical and algorithmic theory (3D shapes, Big Data, bio-inspired algorithms) to solve real-world health problems.
Principal Investigator: Amelia Simó Vidal (Universitat Jaume I)
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The research projects conducted by the node’s members are distinguished by their multidisciplinary nature, combining a solid foundation in advanced statistics and machine learning with direct applications in biomedical engineering, clinical practice, and healthcare. Some of their latest projects include: Development of statistical learning techniques and neural networks for the Riemannian variety of three-dimensional form space. Applications in biomechanics. Statistical learning methods based on archetype analysis for complex data and big data with applications. Intelligent analysis of nursing care progress notes to improve decision-making. Bio-inspired machine learning algorithms.
Current research areas at this node:
Amelia Simó Vidal
Principal InvestigatorUniversitat Jaume IMarina Martínez García
Universitat Jaume IVíctor Costumero Ramos
Universitat Jaume I