CiTIUS, the Research Centre in Information Technology, is part of the Singular Research Centres Network of the Campus Life. Campus Life is the International Campus of Excellence of the University of Santiago de Compostela, recognized in 2009 by the Spanish Ministry of Education and Science.

CiTIUS hosts more than 100 researchers, including 30 senior researchers who have been selected by an external scientific advisory board. The Center has also 14 postdoctoral researchers, including a Ramón y Cajal researcher, a Juan de la Cierva researcher and 3 researchers funded by a postdoctoral programme of the regional government of the Xunta de Galicia. CiTIUS has also been awarded with three projects for Young Researchers (funded by Spanish Ministry of Education and Science) aimed at funding research projects directed by outstanding postdoctoral researchers.

In the period 2015-2017, CiTIUS researchers published more than 200 indexed papers (SCI-Scopus), 50% of them in the first quartile, defended 29 PhD theses and attracted funding of 6.4M€ for R&D activities. In the same period, the Center registered 15 software licenses and created 2 spin-offs, Situm and Imagames. CiTIUS also collaborates with relevant companies of the industry sector such as Indra, IBM, Johnson & Johnson, Babcock International, Finsa and Mestrelab Research and with public entities such as Galician Healthcare Service (SERGAS) and the Directorate-General of Traffic (DGT).

CiTIUS research activity is organized into 7 scientific programmes: 1. Machine learning; 2. Advanced computing; 3. E-health; 4. Approximate processing; 5. Personal Robots; 6. Autonomous sensors and 7. Natural language technologies.

CiTIUS research lines related to Artificial Intelligence

Machine learning

CiTIUS research objectives in this area are the following:

  • Design and application of models or methods for classification, regression and optimization.
  • Symbolic learning (rules, decision trees…) and sub-symbolic (neural networks, support vector machines, deep learning, genetic programming…)
  • On-line continuous learning in multi-device systems
  • Data mining and text mining
  • Process mining
  • Analysis and interpretation of signals, image and videos

Natural Language Technologies

In the area of Natural Language Technologies (NLT), CiTIUS researchers do research on models and develop technologies for many NLP state of the art tasks, such as:

  • NLP for information extraction, language analysis, relation extraction, sentence similarity…
  • Information retrieval and search technologies
  • Semantic representation with ontologies
  • Big data to text systems: NLG from data
  • Intelligent data analytics for NLP: machine learning, data mining, imprecise knowledge and reasoning
  • High performance computing and big data technologies for NLP
  • Explainable Artificial Intelligence


The main research lines of this area are:

  • Development of algorithms to increase the adaptive capacity of robots, allowing them to learn from the user and from their own experience.
  • Training the robot for visual perception: development of artificial vision algorithms in order to enable the robot for Scene Recognition and Feature Identification, as it explores the field.
  • Indoor Mapping and positioning, through multi-sensory information.
  • Autonomy and advanced navigation in complicated, non-structured or predefined areas.


CiTIUS research in this area focuses on:

  • Prevention, through the design and automatisation of processes that stimulate the adoption of healthy life habits, adaptable to patient’s conditions and routines;
  • Diagnosis, based on an analysis of multiple factors to improve current decision-making processes and the establishment of new processes to address diseases;
  • Treatment, through the proposal of new methods, techniques and tools to improve patient’s adherence to the prescribed treatment to attain maximum efficacy, and the analysis of medicine interactions in patients with multiple diseases;
  • Monitoring, focused on the acquisition and processing of physiological signals, both unidimensional and multidimensional, in order to assess the status and pathophysiological evolution of the patient.

Additional information