معرفی
Khaoula Raboudi serves as an Associate Professor at the Higher Institute of Multimedia Arts of Manouba in Tunisia and is an active member of the CRESSON research team within the AAU Laboratory. Her institutional affiliations extend to the Joint Research Unit (UMR) comprising CNRS, UGA, ENSA Nantes, ENSA Grenoble, and Centrale Nantes, with significant collaborative work at the Grenoble Computer Science Laboratory.
Professor Raboudi's research centers on the innovative application of generative techniques and machine learning in architectural and urban design. She specializes in developing solar control systems and sustainable building morphologies that address contemporary challenges in energy efficiency and user well-being. Her work uniquely integrates technical computational approaches with qualitative design dimensions, creating solutions that are both scientifically rigorous and human-centered.
Analysis of her publication record from 2011-2024 reveals consistent research evolution from foundational solar envelope modeling to advanced machine learning applications. Her recent work shows increasing focus on artificial intelligence in environmental design, with notable contributions to both physical architecture and digital environments like animated films. The research demonstrates strong interdisciplinary connections between architecture, computer science, and environmental engineering.
Professor Raboudi maintains an active research profile with regular conference presentations and journal publications. Her work with the CRESSON team demonstrates commitment to advancing architectural knowledge through computational methods, particularly in sustainability-focused design applications. Current projects like AMaL (Ambiances Machine Learning) indicate continued innovation at the intersection of architecture and artificial intelligence.
Her research methodology combines theoretical architectural knowledge with practical computational techniques, often collaborating with computer scientists to develop specialized tools for solar analysis and morphological generation. This approach has produced valuable contributions to sustainable design practices and educational resources for architectural computation.
