- Information theory, machine learning and optimization theory for application on 'omics' (genomics, proteomics, transcriptomics and post-transcriptomics, epigenomics, etc...) data and systems biology
- Complex systems theory and systems biology: modeling and analysis of complex biological systems integrating omics information
- Machine learning, statistical pattern recognition, and optimization theory for modeling and understanding biological processes at the molecular level with special regard to application in neuroscience, cancer research and regenerative medicine
- Signal and Image processing in omics, neuroscience and regenerative medicine
- Development of artificial intelligence-aided bioreactors for regenerative medicine, based on monitoring and controlling cell growth and differentiation by means of omics analysis.