Artificial Intelligence and Big Data are probably the most important technologies related to the digital transformation quest. AI, powered by the massive amounts of data that we have at our disposal today, will be anywhere: In our mobile phones, in our homes, in the cities, in our daily work. It is a technology that will change radically our way of living, which poses not only technical, but societal and legal issues.
The D.I. Lab is researching in AI and data-related subjects to understand how different stakeholders (businesses and governments, citizens and patients, industries and cities) can benefit of these promising technologies.
The main objective of our research is to apply the technology to real scenarios where the innovation on AI and Big Data can be of interest for our customers and the society.
- Artificial Intelligence: From Machine Learning to Deep Learning to the multiple applications of the technology to real-world applications and challenges.
- Big Data: We have a strong background in almost all aspects of the Data Value Chain as well as data architectures.
- Semantics: Proven track record on the application of Cognitive Computing and Knowledge Graphs in collaboration with AI and Big Data.
- Linked Data: Application of the Linked Data paradigm for data publication and linking.
We believe there is no solution that fits-it-all, but general good architectural principles and best practices combined with an excellent knowledge of available tools and new research trends, make the difference between success and mediocrity.
- We develop AI solutions based on machine learning and deep learning in the scope of data analytics projects.
- Of particular interest for us are the architectures, frameworks and techniques that are the foundations of any data-intensive related applications.
- We have developed a Social Network monitoring tool called Capturean that is the cornerstone of our knowledge transfer to commercialize research and innovation results.
- We have an extensive track record in projects and solutions dealing with Semantic Technologies, Cognitive Computing, Knowledge Graphs and NLP.
- Artificial Intelligence
- Understanding the best ML algorithms for different tasks and in different computing platforms.
- Usage of Deep Neural Networks in different application fields.
- Data-bias and responsible AI.
- Big Data
- Understanding how and when to use big data in combination with HPC, Cloud and Edge Computing.
- Architectural approaches to deal with massive amounts of historical and streaming data in a coherent manner.
- Semantic Technologies
- Knowledge Graphs and their application to AI.
- Formal semantics in the Data Value Chain.
- Linked Data and Open Data.
- Automatic deployment of Big Data architectures, components and services.
- Cross-domain data integration.
- Deep learning and Deep Neural Networks.
- Stream processing and stream analytics.
- Interpretation and analysis of unstructured textual resources using Natural Language Processing, Machine Learning and Data Mining techniques.
- Usage of Linked Data open tools for data publishing and linking.
Using robots to increase the number of older adults who engage in a regular and sustained physical activity.
Advanced decision-support in the face of global challenges. It brings together the power of HPC and some of the most promising thinking on global systems in order to improve decisions in business, politics and civil society.
Focused on integrating high volumes of health-related heterogeneous data from multiple sources with the aim of supporting policy making decisions.
Combination of big data analytics with advanced linguistic and visual methods. The results will be suitable for direct application in medical information systems and digital journalism.
Methods to perform cross-sectoral streaming Big Data integration including geographic, transport, meteorological, cross domain and news data, while capitalizing on human feedback channels.