Bishop

Data is at the heart of digital transformations. However, between data and the business cases of companies there is still a big entry barrier :
the technology

Through our research projects, Euranova wants to develop a cutting-edge service offering providing competitive advantages to European entrepreneurs and businesses.

With BISHOP, the goal is to remove technological barriers related to the complexity of data and ensure data confidentiality and trust in AI models.

BISHOP’s research project was able to see the day due to the co-financing of the Walloon Region. Through its two research tracks, it addresses the following challenges :

Tackling data complexity ( Dilithium )

Building responsible AI ( Brainiac )

Dilithium

DILITHIUM aims at addressing the challenges of time-dependent graphs. Graphs have seen widespread adoption recently, driven by the abundance of complex data of various types and the transition from single-source analysis to deeply heterogeneous, multi-source systems. However, data is not static. To facilitate the adoption of graph models that evolve over time and thus better meet the needs of the market in the medium/long term, we develop methods for evolving graph embedding, based on state-of-the-art work and related disciplines. Finally, as explainability has become the central ingredient in trustworthy AI, we also explore state-of-the-art methods designed for explaining node, link or graph level predictions.

Brainiac

BRAINIAC aims at addressing the challenges of responsible artificial intelligence by developing data generative methods that allow the generation of realistic synthetic data. Those data are very close to the original data and represent the data (including minorities) in a complete way while being robust to known or future privacy attacks. Such agnosticity with respect to patterns and attacks requires attention to a way of quantifying the level of privacy using very general notions.

Data science
Internships 2025
You are looking for an internship in an intellectually-stimulating company?are fond of feedback and continuous personal development?want to participate in the development of solutions to address tomorrow’s challenges?want to develop your expertise in machine learning, artificial intelligence, high-performance computing, software engineering, etc. with a dedicated international team of engineers? Who...
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Data governance
Insights from IAPP AI Governance Global 2024
In early June, Euranova’s CTO Sabri Skhiri, attended the IAPP AI Governance Global 2024 conference in Brussels. In this article, Sabri will delve into some of the keynotes, panels and talks that took place during the summit, highlighting the noteworthy insights and innovations shared by industry leaders.
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Engineering
Kafka Summit 2024: 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.
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Engineering
Privacy Enhancing Technologies 2024: A Summary
For Large Language Models (LLMs), Azure confidential computing offers TEEs to protect data integrity throughout various stages of the LLM lifecycle, including prompts, fine-tuning, and inference. This ensures that all data used by LLMs remains encrypted and protected within the TEE, enhancing overall data security and privacy.The recent Privacy Enhancing...
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Data science
IEEE Big Data 2023 – A Summary
Our CTO, Sabri Skhiri, recently travelled to Sorrento for IEEE Big Data 2023. In this article, Sabri explores for you the various keynotes and talks that took place during the conference, highlighting the noteworthy insights and the practical applications shared by industry leaders.
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Data science
Robust ML Approach for Screening MET Drug Candidates in Combination with Immune Checkpoint Inhibitors
Present study highlights the significance of dataset size in ICI microbiota models and presents a methodology to enhance the performances of a multi-cohort-based ML approach.
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Brainiac
Internships 2024
This document presents internships supervised by our consulting department or by our research & development department. Each project is an opportunity to feel both empowered and responsible for your own professional development and for your contribution to the company.
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Data science
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.
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Brainiac
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.
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Engineering
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.
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Data science
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.
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Data science
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.
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Tracks
Field of expertises
Academic collaboration