Projects and networks

In the project “AI-Assisted Optimization of Business Processes”, Dumas and his team will develop automated methods to analyse performance issues in business processes, and to discover and evaluate change options to address these issues. These methods will help businesses to increase service efficiency and enhance customer experience. Public sector organisations, likewise, can use such methods to streamline administrative processes and improve service delivery to citizens.

Main investigator: Prof. Marlon Dumas

The Estonian Centre of Excellence in AI has four focus areas dedicated to the foundations of AI and five focus areas centered on the applications of AI. The consortia includes research groups from the University of Tartu, Tallinn University of Technology and AS Cybernetica.

Head of the Centre: Prof. Meelis Kull
Funding: Estonian Ministry of Education and Research.

Led by the Finnish IT Centre for Science, CSC, one of the world’s most powerful supercomputers, LUMI is being built. Estonia also has a holding in the computer, which gives our researchers and companies access to much higher computing capacity than they have had so far. The High Performance Computing Center of University of Tartu, as a partner in the LUMI consortium, is working to develop the supercomputer. We have developed a resource management solution Puhuri, which gives access to the computing capacity.

ESFRI is a strategic instrument to develop the scientific integration of Europe and to strengthen its international outreach. The Institute of Computer Science of the University of Tartu is a member of several international research infrastructures. ELIXIR, a distributed infrastructure for life-science information ELIXIR ensures that data generated by publicly funded research is accessible for users in academia and industry. ELIXIR enables the exchange and analysis of data and supports knowledge sharing between researchers.

Training representative: Prof. Hedi Peterson

Prioritizing targets for GWAS loci involves three major steps: (1) identifying the mode-of-action by which the causal variant influences the trait of interest, (2) identifying the correct target gene, and (3) identifying the disease-relevant contexts in which the target is active.

Principal investigator: Assoc. Prof. Kaur Alasoo
Funding: Open Targets

The project's goal is to add support for the Estonian language to selected open-source foundation language models, based on which it would later be possible to develop artificial intelligence applications that understand the Estonian language. Currently, support for the Estonian language is available in OpenAI's proprietary GPT models, the use of which is paid and which requires uploading the data into the OpenAI server. In addition, several open-source models exist that do not currently support the Estonian language. The project uses different training methods, full parameter training and parameter-efficient training, to add support for the Estonian language to the foundation models. In addition, the models will be fine-tuned on Estonian language instruction data and human ratings data to achieve better conversational ability. The project contributes to advancing language technological support for the Estonian language and the survival of the Estonian language in the digital age.

Principal investigator: Assoc. Prof. Kairit Sirts
Funder: Ministry of Education and Research
Period: 01.08.2024−31.12.2025

The project is developing a demo version of an open-source solution that enables broader use of data science methods across various domains. The platform is built on four fundamental pillars: 1) Automation, aiming to minimize human intervention in constructing robust data pipelines; 2) Privacy-Preserving focus, employing automated Federated Learning to enable independent data owners to collaboratively train a global model while addressing data heterogeneity and privacy concerns; 3) Adaptability and Self-Tuning, ensuring flexibility to handle rapid data inflows and evolving pipeline requirements, enabling continuous updates; and 4) Explainability, providing robust personalized explanation techniques to enhance user trust.

Principal investigator: Assoc. Prof. Radwa Mohamed El Emam El Shawi
Funder: State Shared Service Centre
Period: 01.01.2024–31.12.2028