GATE Centre of Excellence, Sofia University Bulgaria



The Big Data for Smart Society (GATE) is the first Centre of Excellence in Bulgaria and Eastern Europe to provide for scientific excellence and innovation in Big Data and Artificial Intelligence at regional and European level. The Centre is led by Sofia University “St Kliment Ohridski”, in partnership with Sweden’s Chalmers University of Technologies and Chalmers Industrial Technologies.

GATE is supported by EU Horizon 2020 programme, Regional Development Funds and industry to establish a unique research environment and a globally competitive Digital Hub for Big Data and AI Innovations in Future Cities, Intelligent Government, Smart Industry and Digital Health. The research focus is on cutting edge technology areas of Data Management, Data Analysis, Data Visualization and Engineering of Big Data Systems, with emphasis on Explainable AI, Semantic Technologies and Interoperability, Real-Time Data Analytics, Digital Twin and DataOps.

GATE accumulates significant expertise and inspires and cultivates the next generation of AI and Data scientists and professionals in Bulgaria. The research potential in Big Data and Artificial Intelligence is empowered by the strong research international network and partnerships with globally leading organizations.

GATE is the Bulgarian Hub of International Data Spaces Association (IDSA) and the catalyst of national endeavor toward Data usage and Data sharing in both industrial and public domains. GATE aims to be a driver for community engagement of businesses and public administrations and an enabler for the incubation and acceleration of innovations based on data sharing and utilization at the national and regional levels. The hub also facilitates IDS technologies adoption acceleration by providing technical expertise related to IDSA architecture and components application and implementation for the realization of industrial and public Data Spaces. GATE initiated and operates the first Bulgarian Data Space – an Urban Data Space for data sharing and for providing Data as a Service. It supports the development of a City Digital Twin of Sofia.

The Motivation Behind Joining VELES

VELES is an essential next step in GATE’s endeavors, as IDSA hub, to stimulate a data-driven national and regional economy through the initiation of data spaces in strategic sectors such as healthcare.

By providing advanced infrastructure – platform, data, services and testing and experimentation facilities GATE aims to build through VELES a recognizable national and Regional Smart Health Data Space with credible potential. Boosting data driven collaboration between government, health professionals, industry and academia, GATE aims to be the heart of a vibrant and self-sustainable data sharing ecosystem around the Regional Smart Health Data Space to provide value for its partners and stakeholders.

Responsibilities & Contribution

GATE will coordinate the project and will also lead the activities for establishing the foundation of VELES Data Space in terms of advanced architecture and technologies to provide for data quality, security and interoperability and to prepare the seamless integration and interoperability with EU Health Data Space. GATE will also contribute to ensuring a strong ethical and lawful data governance and rules for secure and safe data exchange and will support the realization of new data sharing business models and data driven innovations.

GATE will also contribute towards the realization of the Bulgarian pilot. In the Digital Health application domain, GATE research is focused on Alzheimer’s disease. The Amyloid-Tau- Neurodegeneration (ATN) biomarker framework is a means of evidencing the biological state of Alzheimer’s disease and non-Alzheimer’s pathophysiology. GATE proposes a prediction of ATN status through known risk factors and dementia scores, which addresses this limitation through empowering targeted recruitment via web-based brain health volunteer research registers. The ATN prediction is granted by AI in the form of ML modeling relative to the best regression models. The rationale for this approach is the evidence that data-driven approaches such as ML can outperform classical statistical methods in the field of diagnostics. In addition, they can continuously ‘learn’ and improve over time as data accumulates.

Furthermore, GATE focuses on Responsible AI and trust creation in people and businesses, by providing for multilevel AI Explainability, based on understandable natural language explanations, to simplify and visualize how ML algorithms make decisions and to enable model transparency. In the context of VELES, this will be especially useful for healthcare practitioners and patients.

GATE also aims at a more democratic and widespread AI, building on novel decentralized ML algorithms that can cope with distributed collectives of local models through federated learning and knowledge distillation, device-centric AI and ML crowd training. This eliminates the necessity for transferring sensitive data and provides for data analysis to be performed at the data place of origin.

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