Research Lines

  • Text and social media mining

  • Building energy efficiency behaviour analysis

  • Tendency analysis

  • Sentiment analysis

  • Anomaly detection by means of association rules

Description: Computational and Machine Learning application techniques for the analysis of massive data from Biomedicine and omic-sciences for the identification of biomarkers in the clinical and epidemiologic ambits.

  • Computational and statistical analysis of data coming from high performance sequence technologies in Biomedicine
  • Development and application of Machine Learning algorithms for the identification of biomarkers in genomics, transcriptomics and epigenomics.

  • Fuzzy logic technologies for the ADN sequence analysis. Tecnologías de lógica difusa para el análisis de secuencias de ADN.

  • Development of graph-based inference and prioritization algorithms.

  • Text mining and semi-automatic detection of scientific literature.

  • Artificial neural networks and Deep Learning

  • Evolutionary algorithms and metaheuristics

Soft Computing in Data Mining

  • Fuzzy association rule mining

  • Representation and visualization models for crisp and fuzzy association rules.

  • Information fusion by means of Data Mining techniques.

  • IoT and 4.0 industry.

  • Ambient Intelligence.

  • Deep Learning and Deep Reinforcement learning for sustainable development

  • Time series prediction

  • System identification and data-driven simulation

  • Energy efficiency in buildings and smart grids

  • Analysis of disinformation in social networks