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Abstract Deep learning has become an extremely important technology in numerous domains such as computer vision, natural language processing, and autonomous systems. As neural networks grow in size and complexity to meet the demands of these applications, the cost of designing and training efficient models continues to rise in computation and energy consumption. Neural Architecture Search (NAS) has emerged as a promising solution to automate the design of performant neural networks. However, conventional NAS methods often require evaluating thousands of architectures, making them extremely resource-intensive and environmentally costly.This thesis introduces a novel, energy-aware NAS pipeline that operates at the intersection of Software Engineering and Machine Learning. We present CNNGen, a domain-specific generator for convolutional architectures, combined with performance and energy predictors to drastically reduce the number of architectures that need full training. These predictors are integrated into a multi-objective genetic algorithm (NSGA-II), enabling an efficient search for architectures that balance accuracy and energy consumption.Our approach explores a variety of prediction strategies, including sequence-based models, image-based representations, and deep metric learning, to estimate model quality from partial or symbolic representations. We validate our framework across three benchmark datasets, CIFAR-10, CIFAR-100, and Fashion-MNIST, demonstrating that it can produce results comparable to state-of-the-art architectures with significantly lower computational cost. By reducing the environmental footprint of NAS while maintaining high performance, this work contributes to the growing field of Green AI and highlights the value of predictive modelling in scalable and sustainable deep learning workflows. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Gilles Perrouin - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Clément Quinton - University of Lille, FranceProf. Paul Temple- University of Rennes, FranceProf. Schin'ichi Satoh - National Institute of Informatics, Japan

BNAIC/BeNeLearn 2025 will be held at the University of Namur under the auspices of the Belgian-Dutch Association for Artificial Intelligence (BNVKI) and the Dutch Research School for Information and Knowledge Systems (SIKS). The conference aims at presenting an overview of state-of-the-art research in artificial intelligence and machine learning in Belgium, The Netherlands, and Luxembourg. More information and registration

Abstract Deep learning has become an extremely important technology in numerous domains such as computer vision, natural language processing, and autonomous systems. As neural networks grow in size and complexity to meet the demands of these applications, the cost of designing and training efficient models continues to rise in computation and energy consumption. Neural Architecture Search (NAS) has emerged as a promising solution to automate the design of performant neural networks. However, conventional NAS methods often require evaluating thousands of architectures, making them extremely resource-intensive and environmentally costly.This thesis introduces a novel, energy-aware NAS pipeline that operates at the intersection of Software Engineering and Machine Learning. We present CNNGen, a domain-specific generator for convolutional architectures, combined with performance and energy predictors to drastically reduce the number of architectures that need full training. These predictors are integrated into a multi-objective genetic algorithm (NSGA-II), enabling an efficient search for architectures that balance accuracy and energy consumption.Our approach explores a variety of prediction strategies, including sequence-based models, image-based representations, and deep metric learning, to estimate model quality from partial or symbolic representations. We validate our framework across three benchmark datasets, CIFAR-10, CIFAR-100, and Fashion-MNIST, demonstrating that it can produce results comparable to state-of-the-art architectures with significantly lower computational cost. By reducing the environmental footprint of NAS while maintaining high performance, this work contributes to the growing field of Green AI and highlights the value of predictive modelling in scalable and sustainable deep learning workflows. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Gilles Perrouin - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Clément Quinton - University of Lille, FranceProf. Paul Temple- University of Rennes, FranceProf. Schin'ichi Satoh - National Institute of Informatics, Japan

Florentin Rochet, a professor of computer security at the University of Namur, specializes in applied cryptography and secure communications. Against the backdrop of rapid advances in artificial intelligence and open-source solutions, he shares his analysis of the current situation and offers his outlook for the future. 

As part of the Inforums and CyberExcellence seminars, we are pleased to welcome Dr. Ryan Wails (Georgetown University), who will speak to us about cybersecurity. You will find the abstract of his presentation and his biography below.No registration required.We hope to see many of you at this event!On the Interplay of Modern Traffic Analysis and Internet Censorship & Circumvention TechniquesIn this talk, I will review the current state of real-world Internet censorship and some tools that network users employ to circumvent censorship. Then, I’ll take a forward-looking view on how censors might incorporate modern ML-based traffic analysis techniques to block users, highlighting the need for stronger circumvention tools. Finally, I’ll discuss our new internet censorship evasion technique called Unidentified Protocol Generation (published at USENIX Security 2025), which is capable of evading detection by state-of-the-art traffic analysis.Author bio: Ryan Wails is a postdoctoral researcher at Georgetown University studying network privacy and security. He completed his PhD while working at the U.S. Naval Research Laboratory. This lab is known for originating the Tor network and establishing the PETs research community, which is now among the top international research communities in computer security. Ryan is a core contributor to the Tor project and has published in leading conferences on network simulators, privacy attacks, privacy-preserving measurements, path selection algorithms, censorship circumvention, and website fingerprinting. He has received several international distinctions, including Best Paper awards at top conferences and recognition at community events for his contributions.

On May 13, the software engineering project presentations (known as the MDL project) were held before a panel consisting of coaches and external clients who had come for the occasion. Through the seven projects presented this year, the students were able to showcase their work and explain how they collaborated with their clients to successfully complete their development projects.

An innovative educational initiative was carried out at the University of Namur during the 2024–2025 academic year as part of the “Software Testing and Quality” course in the Master’s program in Computer Science, with a focus on software engineering. This initiative led to the publication of the SNAIL Report 2025, a comprehensive barometer of software development practices in the Wallonia-Brussels Federation.

Lately, and thanks to LUMI, one of Europe's largest supercomputers, the limits of our knowledge of cell lipid membranes have been pushed back. A team of Namur researchers, including Professor Benoît Champagne and Drs. Pierre Beaujean and Charlotte Bouquiaux, has just published in the Journal of Chemical Information and Modeling. The results of this study pave the way for new approaches in the field of membrane lipid therapy.

One year after receiving funding from the European Research Council (ERC) for his B-Yond project, Prof Guillaume Berionni has just been named a Fellow of the prestigious European chemistry society Chemistry Europe. This distinction makes him the new representative for Belgium for a period of 2 years.

(Update 04/04: This article is "an April Fool") A remarkable event took place during Printemps des sciences, which ran from March 18 to 24, 2024. While a visit to the UNamur Astronomical Observatory was offered to the general public on the evening of March 23, a unique phenomenon occurred.

How does chemistry play a part in developing innovative, sustainable solutions for society's current and future challenges? What unique teaching methods does UNamur use to train enlightened leaders in the field of chemistry? On the occasion of its 50th anniversary, the Department of Chemistry lifts the veil on some of its major research projects and innovative teaching practices.