Mr. Mohamed Hamroun | Healthcare | Breakthrough Research Award
Mr. Mohamed Hamroun | Healthcare | XLIM/ University of Limoges | France
Dr. Mohamed Hamroun is an accomplished computer scientist and engineer specializing in artificial intelligence, image processing, and multimodal information retrieval. Currently serving as a researcher and lecturer at the 3iL School and the XLIM Laboratory at the University of Limoges, France, he has made significant contributions to the fields of deep learning, computer vision, and semantic data indexing. His multidisciplinary expertise spans across AI, VR/AR systems, big data analytics, and intelligent information retrieval systems, positioning him as a leading researcher in computational intelligence and multimedia data analysis. Through his work, Dr. Hamroun has advanced both theoretical understanding and practical applications of machine learning and artificial intelligence for complex visual and semantic data challenges.
Professional Profile
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Summary of Suitability for the “Breakthrough Research Award”
Dr. Mohamed Hamroun is an exceptionally qualified candidate for the Research for Breakthrough Research Award, demonstrating a strong academic foundation, extensive research experience, and impactful scientific contributions in the fields of artificial intelligence (AI), image processing, deep learning, and multimodal information retrieval.
Education
Dr. Hamroun’s academic journey reflects a deep commitment to advancing computer science and AI-driven data analysis. He earned his Ph.D. in Computer Science from the University of Bordeaux, where his doctoral research focused on “Indexing and retrieval by visual, semantic, and multi-level content of multimedia documents,” under the supervision of Professors Henri Nicolas and Ikram Amous. His doctoral work bridged the gap between computational semantics and large-scale multimedia information retrieval. He later completed his Habilitation to supervise research at the University of Limoges, where his postdoctoral contributions were consolidated into a major research theme titled “Contributions to indexing and information retrieval: application to generalist and medical multimodal data,” under the guidance of Professor Damien Sauveron. Before his doctoral studies, he obtained a Computer Engineering degree from the University of Sfax, Tunisia, and a Bachelor’s degree in Computer Science from the same institution. His undergraduate and graduate projects revolved around multilingual search engine development and database management systems, establishing his foundation in applied informatics and intelligent systems.
Professional Experience
Dr. Hamroun’s professional experience demonstrates a steady trajectory of academic excellence and applied innovation. He began his career as an R&D Engineer at SIM-SOFT in Tunisia, where he was involved in software development and data-driven industrial applications. Following this, he pursued his Ph.D. research jointly between the University of Bordeaux and the University of Sfax, working on hybrid semantic and visual content retrieval models. After completing his Ph.D., he joined the XLIM Laboratory at the University of Limoges as a Postdoctoral Researcher, where he focused on the integration of deep learning and ontology-based frameworks for medical and multimedia data analysis. Later, he was appointed as a Lecturer at EILCO Engineering School in France, contributing to both teaching and research in computer science and artificial intelligence. He now holds the position of Associate Professor at 3iL Engineering School, affiliated with the XLIM Laboratory, where he supervises research projects and mentors graduate students in AI, machine learning, and multimedia information systems.
Research Interests
Dr. Hamroun’s research interests cover a wide spectrum of computational and artificial intelligence domains. His core expertise includes image and signal processing, deep learning architectures for data classification and clustering, virtual and augmented reality applications, and semantic data mining. His studies often combine statistical learning, ontology modeling, and multimodal data fusion to enhance human-computer interaction and knowledge extraction. A significant part of his current research focuses on developing intelligent systems for multimodal medical data retrieval and applying AI to improve healthcare diagnostics and decision support. His recent work also extends to federated learning frameworks and semantic interpretation in multimedia environments, bridging applied computer science with real-world AI applications.
Awards
Dr. Hamroun has been recognized for his innovative research in artificial intelligence and multimedia information systems through various academic honors and nominations. His outstanding work in deep learning-based image analysis and computational semantics has earned him recognition among peers in the international AI research community. He has contributed as a co-author to several highly cited papers and participated in collaborative European research projects aimed at integrating AI into real-world industrial and medical systems. His nomination for the award highlights his leadership in combining artificial intelligence with practical problem-solving across domains such as emotion recognition, diabetic foot ulcer diagnosis, and semantic retrieval.
Publication Top Notes
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Title: Emotion recognition from speech using spectrograms and shallow neural networks
Authors: A. Slimi, M. Hamroun, et al.
Year: 2020
Citations: 47
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Title: DFU-Siam: A novel diabetic foot ulcer classification with deep learning
Authors: M. S. A. Toofanee, M. Hamroun, et al.
Year: 2023
Citations: 43
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Title: A survey on intention analysis: successful approaches and open challenges
Authors: M. Hamroun
Year: 2020
Citations: 21
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Title: An interactive engine for multilingual video browsing using semantic content
Authors: M. B. Halima, M. Hamroun, et al.
Year: (arXiv preprint, circa 2013)
Citations: 16
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Title: DFU-Helper: Innovative framework for longitudinal diabetic foot ulcer evaluation using deep learning
Authors: M. S. A. Toofanee, M. Hamroun, et al.
Year: 2023
Citations: 11