The main research area is modelization, whose current main issues include: data streams, multi-dimensional supervised classification, multi-label classification, clustering in high-dimensional spaces, feature subset selection using methods as Bayesian networks, regularization, classification by regression.
The second main field of application is Industry 4.0 where we develop machine learning solutions for cyber-physical systems. Other application domains are: biomedicine, agriculture, bioinformatics, bibliometry and environment.
In heuristic optimization, which is the second main line of research, we investigate state-of-the-art questions related to the improvement of heuristic optimization methods and extension of their applicability to more complex problems (e.g. multi-objective, mixed representation, non-continuous objective functions), with special emphasis on estimation of distribution algorithms.
Neuroscience is the main field of application. Some problems that we face include: (a) neuroanatomy issues, like modeling and simulation of dendritic trees and classification of neuron types based on morphological features; (b) neurodegenerative diseases, like predicting health-related quality of life in Parkinson's disease and searching for genetic biomarkers in Alzheimer's disease.