High-assurance software laboratory

Many structures that arise in mathematics and computer science may be related to one another both 'functionally' and 'relationally': for instance, sets may be related by functions and by relations; and rings may be related by homomorphisms and by bimodules. Double category theory has proven an effective framework in which to study such structures. However, the existing methods for constructing new double categories are limited, which has restricted the scope of double category theory. Taking inspiration from techniques arising in traditional category theory, we will develop three powerful new methods for constructing double categories – cocompletions, sketches, and monads – and establish that the resulting examples satisfy a wide range of useful properties. This will substantially extend the range of applications of double category theory and resolve longstanding incompatibilities between double category theory and other branches of two-dimensional category theory.

CORESpaces project will enable a new normal in understanding, co-designing and implementing systemic changes for delivering human centric, resilient, climate neutral, revitalized and dynamically managed urban space, in partnership with citizens and stakeholders. CORESpaces rethinks spaces and public realm by developing and deploying flexibly adjusted, transferable tools demonstrated and tested in 9 cities across 9 European countries, generating open knowledge to streamline diffusion to any city building capacity and skills for climate-neutral, safe and smart urban redesign in EU urban spaces. The project will help to change spaces to become cognitive and future ready for climate neutrality.

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.