UT is Estonia’s leading centre of research and training. It preserves the culture of the Estonian people and spearheads the country’s reputation in research and provision of higher education. UT belongs to the top 1.2% of the world’s best universities.

The robust research potential of the university is evidenced by the fact that the University of Tartu has been invited to join the Coimbra Group, a prestigious club of renowned research universities.

The University of Tartu and remains today the only classical university in Estonia. Research at UT focuses on subjects as diverse as medicine and philosophy, genetics and computer science.

Within SPATIAL, the UT team will be in charge of analysing in detail the impact of distributed training in AI models, in particular, UT will investigate the influence of different data properties over the performance of AI models, and will develop methods that can aid to reduce the issues.

Principal Investigator

Huber Flores is an Associate Professor of Pervasive Computing at the Institute of Computer Science, University of Tartu, Estonia as well as a Docent at the University of Helsinki, Finland.

Prior to that, he held the prestigious Academy of Finland Postdoctoral Fellowship and the competitive Faculty of Science Postdoctoral Fellowship of the University of Helsinki. He is a former member of UBICOMP at the University of Oulu, Finland, and SyMLab at the Hong Kong University of Science and Technology, Hong Kong. He is also the recipient of the Jorma Ollila Award given by the Nokia Foundation.

Prof. Flores is also an active member of ACM (SIGMOBILE) and IEEE societies. His major research interests include distributed systems, pervasive and mobile computing, and AI.

He has served as an organizer and technical committee member of research venues, which includes IJCAI, ECAI, UMAP, IJCAI-PRECAI, WWW, PerCom, and IUI.


Abdul-Rasheed Ottun is a PhD student at the Institute of the Computer Science University of Tartu. His research interests include autonomous vehicles, explainable AI and pervasive sensing.

  1. What are your expectations in a project of this nature?

Our main expectations at UT are research and educational related. From the research side, we want to further increase our cyber-security expertise through international industrial cooperation, which provides relevant use cases to identify current trustworthiness problems in AI applications oriented to users.  Estonia is very strong in this area as most of public services are digitally available and our obligation if contribute further to it.  From the educational side, our main expectation is that SPATIAL results can become part of the content in our courses, such that we can transfer useful skills to students. In particular, we expect that students could get new insights on how to develop applications that are explainable to the end users.


  1. What can the research community expect from SPATIAL?

The fundamental research challenges that are investigated in the project are not just applicable in the context of cyber-security applications, but overall in any application relying on AI for decision making support. Thus, the project can have a significant impact on multiple scientific communities. Personally, as a researcher interested in mobile and pervasive technologies, I see the research in SPATIAL having huge contributions for autonomous devices, which have reached a technological maturity to support many human tasks, such as food and package delivery, and health monitoring.  Explaining operations of autonomous devices like drones with SPATIAL technology can be key for its large scale adoption in urban areas.


  1. Where do you see SPATIAL results in 10 years?

In the long term, the fundamental results of SPATIAL will contribute towards improving our understanding of AI within online applications and services.  It can be relieving for end users interacting with digital applications that are trustworthy. The initial spark that we are contributing within the ICT landscape, could foster others to follow an agenda oriented to make the black-box execution of AI a thing of the past.