Mohammed Oualid ATTAOUI

Dr. Mohammed Oualid ATTAOUI is a Postdoctoral Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, affiliated with the SVV department. His work focuses on advancing artificial intelligence and machine learning methodologies, particularly in the context of deep neural network (DNN) safety analysis, clustering algorithms for data streams, and medical image processing applications. He holds a strong research interest in generative adversarial networks (GANs), black-box model analysis, and transfer learning techniques. His current position involves developing robust frameworks to ensure safety and reliability in AI systems, with notable contributions to the SAFE methodology for DNN retraining and validation.

Research interests span across multiple domains including:

• Deep Learning and Safety-Critical AI Systems
• Data Stream Mining and Real-Time Clustering
• Medical Imaging Analysis (Histopathology, Computer Vision)
• Feature Selection and Optimization in Bioinformatics
• Subspace and Topological Clustering Approaches

His recent research trends emphasize the intersection of adversarial machine learning and safety engineering, with publications demonstrating advancements in GAN-enhanced simulation-driven testing and clustering-based safety analysis for DNNs. His work bridges theoretical algorithm development with practical applications in healthcare imaging and autonomous systems.

Dr. ATTAOUI's research outputs reflect a strong focus on methodological innovation with over 15 peer-reviewed articles since 2018. He actively contributes to open-source tools for data stream clustering and maintains an active presence in AI safety communities. His current projects explore adaptive clustering techniques for evolving data streams and robustness certification of medical image processing systems.