Sophie Fortz

Sophie Fortz is a researcher at the Faculty of Computer Science, University of Namur, affiliated with the Research Center on Information Systems Engineering and the Namur Digital Institute. Her work bridges machine learning and software engineering, focusing on variability-intensive systems and self-adaptive architectures.

• PhD in Computer Science (2023), University of Namur
• Master in Computer Science (2019), University of Namur

Research Interests: Fortz specializes in applying machine learning techniques like recurrent neural networks (RNNs) and feature-based learning to analyze and optimize software systems. Her projects include variability-aware behavioral learning, architectural smell detection, and quantum computing algorithm acceleration.

Recent Publications: Her work on VaryMinions (2024) leverages RNNs for variant identification in system logs, while 2025 papers explore quantum eigensolvers accelerated by machine learning. Earlier contributions include concolic testing tools and behavioral mapping approaches for self-adaptive systems.

Activities: Fortz participates in major conferences like SPLC and VaMoS, serves as a keynote speaker, and organizes cybersecurity symposiums. She collaborates with researchers like Gilles Perrouin and Pascal van Hooft.