Euranova has 3 fundamental pillars: explore, craft and serve. The explore pillar of Euranova is an independent research centre dedicated to data science, software engineering and AI.
Through the exploration of tomorrow’s engineering and data science to answer today’s problems, our research centre is dedicated to anticipating the challenges that European businesses face. We find solutions to current and future digital challenges with passion, creativity and integrity.
Euranova has 3 fundamental pillars: explore, craft and serve. The explore pillar of Euranova is an independent research centre dedicated to data science, software engineering and AI.
Through the exploration of tomorrow’s engineering and data science to answer today’s problems, our research centre is dedicated to anticipating the challenges that European businesses face. We find solutions to current and future digital challenges with passion, creativity and integrity.
Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings
In this paper, we study graph representation learning and show that data augmentation that preserves semantics can be learned and used to produce interpretations. Our framework, which we named INGENIOUS, creates inherently interpretable embeddings and eliminates the need for costly additional post-hoc analysis.
SANGEA: Scalable and Attributed Network Generation
In this paper, we present SANGEA, a sizeable synthetic graph generation framework that extends the applicability of any SGG to large graphs.
By first splitting the large graph into communities, SANGEA trains one SGG per community, then links the community graphs back together to create a synthetic large graph.
The Building Blocks of a Responsible AI Practice: An Outlook on the Current Landscape
Responsible AI comes with the challenge of implementation. This survey aims to bridge the gap between principles and practice through a study of different approaches taken in the literature and the proposition of a foundational framework.
TS-Relax : Interprétation des représentations apprises pour les séries temporelles
Les modèles d’apprentissage de représentations sont de plus en plus utilisés, mais des modèles d’IA explicables et de confiance sont nécessaires. Ce travail présente l’adaptation aux séries temporelles d’une méthode d’interprétation de représentation initialement conçue pour les images.
Comparison of Machine Learning Approaches for POD24 Prediction
Early identification of patients with relapsing follicular lymphoma (FL) is critical but remains elusive. We initiated a collaboration between the academic CALYM Carnot Institute aiming at developing interpretable artificial intelligence (AI) models based on PET images to predict POD24.
A Fair Classifier Embracing Triplet Collapse
In this paper, we study the behaviour of the triplet loss and show that it can be exploited to limit the biases created and perpetuated by machine learning models.
Kafka Summit 2023: Announcements & Trends
The Kafka Summit brought together industry experts, developers, and enthusiasts to discuss the latest advancements and practical applications of event streaming and microservices. In this article, our CTO Sabri Skhiri delves into some of the keynotes and talks that took place during the summit.
KubeCon + CloudNativeCon Europe: Insights and Experiences
In April, our engineers Maxime and Eva travelled to Amsterdam for the Kubecon Europe 2023, THE conference of the Cloud Native Computing Foundation. In this article, they provide insights from the conference, covering key trends, the conference format, and highlighting some of the standout talks.
Privacy Enhancing Technologies 2023: A Summary
In March 2023, our research director Sabri Skhiri travelled to London to attend the Privacy Enhancing Technologies Summit 2023, dedicated to PETs and their uses (enhance data security, facilitate compliance, and create value).
2022 Wrap Up
We got a deep dive into some of the most memorable moments of 2022.
IEEE Big Data 2022: the key takeaways
In December 2022, our research director Sabri Skhiri travelled to Osaka to attend IEEE Big Data 2022. He sums up the main trends, and shares his favourite talks and papers.
Dynamic Pairwise Wake Vortex Separations For Arrivals Using Predictive Machine Learning Models
Aircraft wake behaviour and meteorological information is monitored and processed using ML algorithms which determine the wake separation minimum reductions that can be safely applied between subsequent arriving aircraft.