GATE Institute is the first dedicated Big Data and AI Centre of Excellence in Eastern Europe co-funded by the EC and the Bulgarian Government and established as a joint initiative with Chalmers University and Chalmers Industrial Technologies, Sweden. Having the vision to enable a Data-Driven Smart Society, GATE is facilitating digital transformation by leveraging the opportunities that Big Data and AI digital technologies create and by accelerating their impact across society. GATE performs research and creates innovations in four application domains – Future Cities, Digital Health, Smart Industry and Intelligent Government.
GATE develops research capacity and potential in Big Data and Artificial Intelligence, cultivating the next generation of leading scientists by expanding the existing research network and establishing long-term agreements with leading global organizations. By providing advanced infrastructure - platform, data, services and testing and experimentation facilities, GATE aims to be the recognizable national Data Space with credible potential. Enabling AI-driven technological collaboration between government, industry and academia, GATE is the heart of a vibrant data-sharing ecosystem to provide value for its partners and members.
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, devicecentric AI and ML crowd training. By combining ontological engineering models and semantic technologies with the specification of logical policies and constraints, GATE develops a new level of intelligence capable of dealing with both static and dynamic data in a variety of business domains.
In the Future Cities application domain, GATE is focused on City Digital Twins and Data Spaces as complementary technologies and innovative models. They enable smart data sharing, simulations, and data-driven and evidencebased decisions in many city aspects – air quality, urban planning and building, energy consumption, infrastructure, city transportation and logistics, mobility, etc.
Air quality monitoring is an important aspect of urban governance. High concentration levels of different gases and particulate matter can have an adverse effect on human health and the standard of living in urban regions. The existing air quality stations measure air pollutants at a finite number of locations, providing pointwise data with different granularity and quality. The GATE team is working on geospatial air quality analysis to provide a way of combining the information from different sensor stations and to develop a platform for the real-time prediction of air quality. To achieve this, methods from the field of uncertainty quantification and statistical learning are used, which enable the analysis of existing sensor data in a probabilistic way. The developed platform is envisioned to serve several purposes, including the monitoring and prediction of data-based air quality metrics, the determination of the optimal placement of future sensor stations and the virtual surrogate for sensors during downtime.
In the Digital Health application domain, GATE research is focused on Alzheimer’s disease. The Amyloid-TauNeurodegeneration (ATN) biomarker framework is a means of evidencing the biological state of Alzheimer’s disease and non-Alzheimer’s pathophysiology. This novel definition of neurodegenerative states has operationalized the recent shift in interventional dementia trials from the syndrome stages of the disease to biologically defined preclinical states. The scalability of the ATN framework that is required for large interventional trials is limited by the availability, invasiveness and cost of the biomarker investigations. GATE proposes a prediction of ATN status through known risk factors and dementia scores, which addresses this limitation through empowering targeted recruitment via internet-based brain health volunteer research registers. The ATN prediction is granted by artificial intelligence in the form of machine learning modelling relative to the best regression models. The rationale for this approach is the evidence that data-driven approaches such as machine learning can outperform classical statistical methods in the field of diagnostics. In addition, they can continuously ‘learn’ and improve over time as data accumulates.
GATE is focusing on a new research priority area dedicated to studying online disinformation in the Balkans, as citizen susceptibility to conspiracies and misinformation in the area is among the worst in Europe. The team develops new AI methods for large-scale disinformation analysis in English, Bulgarian and other Balkan languages. A new regional multi-disciplinary alliance of researchers, factcheckers and other experts has been established, to act as a catalyst and an amplifier of national and regional actions against disinformation.
GATE is the Bulgarian International Data Spaces Association (IDSA) Hub and the catalyst of national endeavour toward Data usage and Data sharing in both industrial and public domains. The IDSA and the Bulgarian hub are on a mission to create the future of the global, digital economy with International Data Spaces – a secure, sovereign system of data sharing in which all participants can realize the full value of their data. GATE aims to be a driver for community engagement of businesses and public and city 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 is building an Urban Data Space for data sharing and Data as a Service. It will support the development of a City Digital Twin of Sofia. The data is a key to informed decision-making, a valuable resource for innovation and development and a prerequisite for making smart urban decisions. The integration of public and private data, as well as citizens’ data for environmental cleanliness, mobility, construction, energy efficiency, etc., is crucial to achieving sustainable cities. The utilization of data space and digital twin technologies allows the city to analyze air pollution, identify measures to optimize energy and natural resources usage, to improve housing and mobility. For example, by using City Digital Twins “what-if scenarios” can be simulated, e.g., what is the effect of the air pollution on the surrounding environment and citizens’ health or how to plan the city in order to be more walkable and more liveable.