The two days NUEI Project Workshop, held on March 12–13 2026 at the Exactum Building of the University of Helsinki, successfully brought together researchers, doctoral students, and collaborators from Nordic partner institutions to discuss the latest advances in edge intelligence, distributed AI, and next-generation networked systems.
Hosted in Helsinki under the framework of the Nordic University Cooperation on Edge Intelligence (NUEI) initiative, the two-day workshop provided a dynamic platform for scientific exchange, interdisciplinary collaboration, and future research planning among participants from the University of Helsinki, KTH Royal Institute of Technology, Aarhus University, and other partner institutions.
The workshop opened with welcome remarks by Professor Sasu Tarkoma from the University of Helsinki, followed by a series of technical presentations addressing emerging challenges in efficient AI systems at the edge. Topics covered privacy-preserving transformer fine-tuning, continual transformers for real-time inference on data streams, communication-efficient distributed inference, and hierarchical edge–cloud offloading.
The second day focused strongly on collaboration and long-term research development. Participants engaged in thematic breakout sessions aimed at identifying joint research opportunities, publication ideas, and interdisciplinary collaboration pathways. Student groups worked together to formulate research challenges, explore potential technical solutions, and initiate future cooperative efforts across institutions.
In addition to technical sessions, the workshop included strategic discussions on upcoming project activities, including reporting, proposal development, student exchanges, and the Summer School on Edge AI. The event concluded with group presentations and a shared lunch, reinforcing the collaborative spirit of the NUEI network.
The NUEI Project Workshop 2026 demonstrated the continued strength of Nordic collaboration in edge intelligence research and highlighted the importance of cross-institutional cooperation in advancing trustworthy, scalable, and sustainable AI systems for the future.