CDMS | Best Poster Award at EuroSys'26: MinatoLoader Recognised in Edinburgh

Following the publication of the scientific paper "MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessing" — developed within the scope of the CDMS – Claim Denials Management Solution project — the research has now achieved a new milestone of international recognition.

Recognition at the 21st ACM European Conference on Computer Systems

The 21st ACM European Conference on Computer Systems (EuroSys'26) took place between 27 and 30 April 2026, in Edinburgh, bringing together some of the world's most prominent researchers in the field of computer systems.

The paper — co-authored by researcher Ricardo Macedo, from the High Assurance Software Laboratory (HASLab) at INESC TEC, and researchers Rahma Nouaji, Stella Bitchebe, and Oana Balmau, from McGill University — was presented at the conference, as previously announced. But the presence in Edinburgh brought with it an unexpected distinction.

Ricardo Macedo of High Assurance Software Laboratory (HASLab) of INESC TEC, and Rahma Nouaji, Stella Bitchebe and Oana Balmau, da McGill University receiving the distinction at EuroSys'26

Best Poster Award

The poster developed as part of this scientific work, sharing the same title as the paper, was awarded the "Best Poster Award" at EuroSys'26 — the conference's top poster prize.

This recognition distinguishes not only the scientific quality of the research, but also its ability to communicate a technically demanding contribution in a clear and impactful way to an international audience of experts.

A milestone that reinforces the impact of the CDMS project

This distinction reinforces what the CDMS project has stood for since its inception: the conviction that innovation with real impact requires rigorous research, multidisciplinary collaboration, and the ambition to present results on stages of excellence.

The recognition at EuroSys'26 is, for the co-promoters and for the entire team involved, a further stimulus to continue building AI solutions that are more efficient, reliable, and prepared to address the concrete challenges of the healthcare sector.

To learn more about MinatoLoader and the research behind it, the full scientific paper is available at: https://arxiv.org/pdf/2509.10712