Mohamed is a senior-level computer vision researcher with industrial and academic experience in image processing and robotics field over 10 years. He has managed technical projects with worldwide clients and European partners. His research expertise involves embedded vision, information retrieval and medical imaging. He has published highly cited papers in top computer vision conferences (i.e., ICCV, ACVIS, CAIP, VCIP). He serves as a reviewer for a number of journals and conferences in computer vision field.

• Teaching Fellow at University of Strathclyde, UK (2022 - Present)
• PhD in Computer Vision, France (2014 - 2019)
• Joint MSc in Computer Vision and Robotics, Europe (2012 - 2014)
• Winner of ICCV 2017 2D reflection symmetry detection competitions (among participants)

• awesome-computer-vision-resources, A structured learning reference for computer vision: from image fundamentals to research frontiers.
• awesome-computer-vision-tools, About Curated tools for the full CV pipeline: training, data augmentation, experiment tracking, inference, edge deployment, and MLOps.
• RoadToFAANG_CV, Hitchhiker's guide in getting a FAANG job (Computer Vision).
• awesome-academic, curated list of academic tools for teaching and research.
• [WIP] awesome-cs, curated educational list for computer science.
• [WIP] cv-recipes, Recipes for computer vision for educational and research use.

• VCIP'21, Security and Forensics Exploration of Learning-based Image Coding: Watermarking and Source Identification.
• [Available Soon] ICISP'18, Editorial Image Retrieval Using Handcrafted and CNN Features.
• [Available Soon] ICIAR'18, Mobile-Based Painting Photo Retrieval Using Combined Features.
• ICCVW'17, Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms.
• [Available Soon] CAIP'17, Multiple reflection symmetry detection via linear-directional kernel density estimation.
• arXiv'17, Automatic Classification of Bright Retinal Lesions via Deep Network Features.
• ICIAR'16, Automatic nonlinear filtering and segmentation for breast ultrasound images.
• [Available Soon] arXiv'16, Detecting and avoiding frontal obstacles from monocular camera for micro unmanned aerial vehicles.