Doctor | Mohammed V University of Rabat Mohammadia School of Engineering | Morocco
Short Bio 🌍
Dr. Omar Soufi is a dedicated data scientist with a profound interest in artificial intelligence (AI) and its applications in spatial remote sensing and geographic information systems (GIS). His career spans significant contributions to the field, focusing on enhancing satellite imagery through deep learning techniques. Dr. Soufi holds a Ph.D. in Computer Engineering, specializing in AI, from EMI Rabat, marking a pivotal achievement in his academic journey.
Profile
ORCID
Education 📚
Dr. Omar Soufi’s academic pursuits reflect his commitment to excellence in computer engineering and AI. He completed his doctoral studies at EMI Rabat, where his research delved into leveraging deep learning for spatial remote sensing applications. Prior to his Ph.D., Dr. Soufi earned his engineering degree from Polytechnique Grenoble in 2020. His educational background also includes a degree in Computer Engineering from EMI Rabat, setting a solid foundation for his future endeavors in data science and AI.
Experience 💼
Dr. Soufi’s professional trajectory is marked by leadership roles and impactful contributions in AI and data science. He began his career as a Project Manager at the Service Informatique, demonstrating early expertise in managing complex IT projects. His role evolved over the years, culminating in positions such as Head of Business Intelligence and Decision Support Tools at various organizations. Notably, Dr. Soufi has been instrumental in spearheading national platforms for disaster risk management and satellite imagery enhancement, underscoring his practical application of AI in solving real-world challenges.
Research Interests 🧠
At the core of Dr. Soufi’s research interests lies a passion for exploring the intersection of AI and spatial remote sensing. His doctoral research focused on advancing the quality of satellite imagery and optimizing sensor processes through deep learning methodologies. Dr. Soufi continues to explore new frontiers in AI, aiming to revolutionize how spatial data is collected, analyzed, and utilized for societal benefit.
Awards 🏆
Dr. Soufi’s contributions to AI and satellite technology have garnered prestigious recognition within the scientific community. His innovative approaches have been acknowledged with patents and numerous publications in renowned journals and conferences. These accolades underscore his impact and leadership in advancing the field of AI and spatial remote sensing.
Publications 📝
Dr. Soufi’s publications are pivotal in showcasing his expertise and contributions to the field:
- Study of deep learning-based models for single image super-resolution, published in the Revue d’Intelligence Artificielle in 2022, explores the application of deep learning models in enhancing the resolution of satellite images.
- FSRSI: New deep learning-based approach for super-resolution of multispectral satellite images, featured in Ingénierie des Systèmes d’Information in 2023, presents a novel approach to improving the quality of multispectral satellite imagery using deep learning techniques.
- Deep learning technique for image satellite processing, published in Intell Methods Eng Sci in 2023, discusses methodologies for processing satellite images using deep learning algorithms.
- Enhancing Accessibility to High-Resolution Satellite Imagery: A Novel Deep Learning-Based Super-Resolution Approach, published in the Journal of Environmental Treatment Techniques in 2023, proposes a novel approach to enhancing satellite imagery resolution through deep learning techniques.
- An intelligent deep learning approach to spacecraft attitude control: the case of satellites, currently under evaluation, explores intelligent deep learning techniques applied to spacecraft attitude control, demonstrating Dr. Soufi’s ongoing commitment to advancing AI applications in aerospace engineering.
Each publication showcases Dr. Soufi’s innovative thinking and his ability to apply cutting-edge AI technologies to solve complex challenges in spatial data analysis and remote sensing.