The Data Science Laboratory (DSLab) / Research Group on Automatic Learning and Massive Data Analysis aims to study and analyse the large amount of data currently generated through the everyday use of electronic devices and the digital world. The generation of all this data has as its main element the people who are the generating center through their interaction with the digital world and with the new digitized physical environment that surrounds us (IoT) making the amount of data that is being and will continue to be generated grow exponentially (“big data”). This is a challenge for industry, public institutions, universities and researchers alike. In this context, the DSLab research group aims to understand and model all this large amount of data by customizing our environments, creating new modes of human-machine interaction, and improving our quality of life in a sustainable way.
The members of the group have participated in +15 competitive projects and published +50 scientific publications in their main lines of research:
- Data mining and automatic learning applied to mass data analysis (big data)
- Machine learning methods based on latent and graph models.
- Dimensionality reduction.
- Affective computing, opinion/emotions prediction, and assistance in learning processes.
- Disease prediction from the study of clinical history data and historical information of patients using automatic learning models.
- Social Media Analysis and Recomendation Systems.
- Neuroimaging and neuronal learning.