Postdoctoral Fellow at Khalifa University, Algeria
Dr. Said Boumaraf is a dedicated researcher and academic in the field of computer science, specializing in artificial intelligence, machine learning, and computer vision. With a strong background in biomedical imaging, industrial applications, and networking, his work focuses on developing innovative AI-driven solutions for real-world challenges. He has contributed significantly to both academia and industry, holding various research positions and publishing extensively in high-impact journals. His expertise spans deep learning, feature selection, transfer learning, and anomaly detection, with applications in healthcare, oil and gas industries, and satellite communication systems.
Profile
Orcid
Education
Dr. Boumaraf earned his Ph.D. in Computer Science and Technology from the Beijing Institute of Technology, China, where he worked under the guidance of Prof. Xiabi Liu. His doctoral thesis, titled “Research on Machine Learning Methods for Breast Cancer Classification,” contributed significantly to AI applications in medical diagnosis. Prior to this, he completed his M.Sc. and B.Sc. degrees in Computer Science at Abbes Laghrour University of Khenchela, Algeria. His master’s research focused on wireless sensor network localization, while his bachelor’s thesis explored ontology-based contextual information search. These foundational studies provided him with extensive knowledge in data-driven decision-making and intelligent systems.
Professional Experience
Dr. Boumaraf has accumulated extensive research and professional experience across multiple roles. Currently, he is a postdoctoral fellow at Khalifa University of Science and Technology, UAE, where he is engaged in advanced AI projects such as “Vision-based Flare Analytics” for the oil and gas industry and “AI for Digital Pathology” for healthcare applications. Previously, he was a postdoctoral researcher at the University of Malta, working on AI-driven document analysis and classification. His industrial experience includes serving as a Chief Engineer and Researcher at the Algerian Space Agency, where he contributed to satellite control operations and AI-based anomaly detection in satellite telemetry data. Additionally, he has experience in IT management and government administration, further broadening his expertise in system optimization and software development.
Research Interests
Dr. Boumaraf’s research interests encompass artificial intelligence, deep learning, and computer vision, with applications in biomedical imaging, industrial analytics, and network security. He has focused extensively on machine learning-based medical image analysis, including thyroid nodule detection, histopathology classification, and dermoscopy. His industrial research includes AI-based combustion efficiency monitoring in oil and gas flares and satellite-based remote sensing. Additionally, he is interested in optimization techniques, dynamic knowledge networks, and cross-domain methodologies for enhancing model generalization. His work integrates AI-driven solutions into critical sectors, improving both operational efficiency and scientific innovation.
Awards and Recognitions
Dr. Boumaraf has been recognized for his contributions to AI and computer vision research through various academic and professional honors. He has received multiple nominations and accolades for his work in biomedical imaging and industrial AI applications. His research has been featured in prominent conferences and journals, and he has been actively involved in interdisciplinary collaborations that have garnered recognition from scientific and industrial communities.
Publications
Said Boumaraf, Xiabi Liu, Chokri Ferkous, Xiaohong Ma (2020) – “A New Computer-aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms,” Biomedical Research International (DOI: 10.1155/2020/7695207). Cited by 50+ articles.
Said Boumaraf, Xiabi Liu, Zhongshu Zheng, Xiaohong Ma, Chokri Ferkous (2020) – “A New Transfer Learning Based Approach to Magnification Dependent and Independent Classification of Breast Cancers in Histopathological Images,” Biomedical Signal Processing and Control (DOI: 10.1016/j.bspc.2020.102192). Cited by 60+ articles.
Said Boumaraf, Xiabi Liu, Yuchai Wan, Zhongshu Zheng, Chokri Ferkous, Xiaohong Ma (2021) – “Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation,” Diagnostics (DOI: 10.3390/diagnostics11030528). Cited by 40+ articles.
Yuchai Wan, Zhongshu Zheng, Ran Liu, Zheng Zhu, Hongen Zhou, Xun Zhang, Said Boumaraf (2021) – “A Multi-Scale and Multi-Level Fusion Approach for Deep Learning-Based Liver Lesion Diagnosis in Magnetic Resonance Images with Visual Explanation,” Life (DOI: 10.3390/life11060582). Cited by 30+ articles.
Al Radi, Muaz, Pengfei Li, Said Boumaraf, Jorge Dias, Naoufel Werghi (2024) – “AI-Enhanced Gas Flares Remote Sensing and Visual Inspection: Trends and Challenges,” IEEE Access. Cited by 20+ articles.
Xiaodong Qin, Xiabi Liu, Said Boumaraf (2019) – “A New Feature Selection Method based on Monarch Butterfly Optimization and Fisher Criterion,” International Joint Conference on Neural Networks (IJCNN). Cited by 25+ articles.
Huiyu Li, Xiabi Liu, Said Boumaraf, Weihua Liu, Xiaopeng Gong, Xiaohong Ma (2020) – “A New Three-stage Curriculum Learning Approach for Deep Network Based Liver Tumor Segmentation,” International Joint Conference on Neural Networks (IJCNN). Cited by 35+ articles.
Conclusion
Dr. Said Boumaraf is a distinguished researcher whose work bridges the gap between artificial intelligence and real-world applications. His contributions to biomedical imaging, industrial AI, and satellite communication have significantly advanced the fields of machine learning and deep learning. With an extensive background in academia and industry, he continues to push the boundaries of AI-driven innovation. Through his research, publications, and professional engagements, Dr. Boumaraf remains at the forefront of cutting-edge AI applications, making meaningful contributions to scientific and technological advancements.