Tarmo Uustalu

Data has become the most valuable resource for the automation and optimization of tasks arising both in the private and public spheres. The proposed research area/project aims at strengthening both the synergy and quality of the current research of Taltech in this area, while significantly enhancing the capabilities of Uni to cooperate with Estonian industry and public sphere by joint work, consultations, continuous and regular education. The focus of the project is on using machine learning for data science: ML, in particular deep learning, has shown the most promise in advancing the capabilities of future software systems and empowering the whole business of software development. The concrete goal is to increase the manpower and competence in machine learning, while enhancing and cooperating with the existing areas of data science like data and rule mining, data semantics and knowledge representation, natural language data queries, data integration, statistics and data management.

"EXCITE brings together the topranked ICT research groups Estonia to work jointly on a focussed, yet broad and extendable, research programme. It will capitalize on the existing expertise to create synergies on the rich but fragmented landscape of the Estonian ICT research. The consortium will advance foundational theories of model verification and data analysis. On this groundwork, it will develop methods and tools for sound practices of designing and analyzing reliable and secure ICT systems processing large data volumes, as demanded by applications to domains of high socioeconomic relevance (cyberphysical and robotic systems, ehealth and biomedical systems). We will start with 10 cooperation themes with clearly defined objectives, methodology and expected results. These themes will be refined and redefined after 3 years. EXCITE will support research sustainability and provide a development opportunity for young researchers by financing 20-30 PhD students and postdocs.

The central research focus of the technology and economics of trust in software theme will be certified software. Topics of research include: • methods and tools for certification of software; program analysis, transformation, generation; in particular for big data, cloud and IoT; • static analysis (model checkers, theorem provers), verification, systematic testing; • contract languages, languages with powerful type systems (refinement types, dependent types); domain-specific languages; • program synthesis and program learning, program understanding; • repositories of certified software, evolution of certified software; • trust in closed-source software, gradual trust-building; • trading trust, pricing of trust, game theory of trust.