Castellón-Aprendizaje-Automático

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:

• Supervised and unsupervised machine learning (prototype analysis, random forests, neural networks, multidimensional scaling, clustering, etc.)
• Statistical analysis of geometric shapes.
• Functional data analysis.
• Image analysis. Closed random sets and point processes.
• Computational neuroscience.
• Applications in medicine, neuroscience, anthropometry, and biomechanics.