Mohand Said Allili is a full professor in the Department of Computer Science and Engineering at the University of Quebec in Outaouais (UQO). He directs the Imaging, Vision, and Artificial Intelligence Research Laboratory (LARIVIA), focusing on machine vision, machine learning, multimedia data processing, and cybersecurity applications in medical and aerial imagery analysis. His research spans over three decades with notable contributions in image segmentation, texture analysis, deep learning models for medical diagnostics, and AI-driven infrastructure inspection systems. Key projects include drone-based bridge defect detection systems and dermatological imaging networks. His work integrates probabilistic modeling (e.g., Gaussian mixtures) with modern neural networks for robust computer vision solutions. Publications highlight trends in: Deep learning applications for infrastructure monitoring (bridge defect detection) Medical imaging advancements (skin lesion segmentation) Augmented reality systems for remote inspection Innovative texture modeling techniques Allili's lab develops cutting-edge tools like MEDiXNet for dermatological analysis and SES-ReNet for human detection in adverse conditions. His work bridges theoretical computer science with practical engineering solutions. Research focuses on: Semantic image/video annotation Multimodal data fusion Lightweight neural networks for edge computing Context-aware feature extraction
