Researcher and AI scientist from Khalifa University, China
Dr. Said Boumaraf is a distinguished researcher specializing in artificial intelligence (AI), computer vision, and medical imaging. Currently serving as a Postdoctoral Fellow at Khalifa University, his work primarily focuses on developing advanced AI methodologies to address complex challenges in visual recognition and healthcare diagnostics. Dr. Boumaraf has contributed significantly to the field through his involvement in projects that enhance remote sensing of gas flares and improve face parsing techniques under occlusion conditions. His research has been published in reputable journals and conferences, reflecting his commitment to advancing technological solutions for real-world problems. Collaborating with international teams, he continues to push the boundaries of AI applications, particularly in areas that intersect with environmental monitoring and medical diagnostics. Dr. Boumaraf’s dedication to research excellence positions him as a leading figure in the integration of AI technologies into practical applications.
Professional Profile
Education
Dr. Boumaraf’s academic journey is marked by a strong foundation in computer science and engineering. He earned his Ph.D. in Computer Science, where his research focused on the development of AI algorithms for medical image analysis. His doctoral studies provided him with in-depth knowledge of machine learning, deep learning, and their applications in healthcare. Prior to his Ph.D., Dr. Boumaraf completed his Master’s degree in Computer Engineering, during which he explored various aspects of computer vision and pattern recognition. His academic pursuits have equipped him with a robust skill set that bridges theoretical understanding and practical implementation of AI technologies. Throughout his education, Dr. Boumaraf has demonstrated a commitment to interdisciplinary research, integrating principles from computer science, engineering, and healthcare to develop innovative solutions. His educational background lays the groundwork for his ongoing contributions to the field of AI and its applications in critical domains.
Professional Experience
Dr. Boumaraf’s professional experience encompasses a range of roles that highlight his expertise in AI and its applications. As a Postdoctoral Fellow at Khalifa University, he has been instrumental in leading research projects that apply deep learning techniques to environmental and medical challenges. His work includes developing AI-enhanced methods for remote sensing of gas flares and creating robust face parsing algorithms capable of handling occlusions. Prior to his current role, Dr. Boumaraf collaborated with various research institutions and industry partners, contributing to projects that required the integration of AI into practical solutions. His experience extends to developing computer-aided diagnosis systems for breast cancer detection, showcasing his ability to apply AI in critical healthcare settings. Dr. Boumaraf’s professional journey reflects a consistent focus on leveraging AI to address real-world problems, underscoring his role as a key contributor to the advancement of intelligent systems in diverse applications.
Research Interests
Dr. Boumaraf’s research interests lie at the intersection of artificial intelligence, computer vision, and medical imaging. He is particularly focused on developing deep learning models that enhance the accuracy and efficiency of image analysis in complex scenarios. His work on occlusion-aware face parsing addresses challenges in visual recognition where parts of the face are obscured, improving the reliability of facial analysis systems. In the medical domain, Dr. Boumaraf has contributed to creating AI-driven diagnostic tools that assist in the early detection of diseases such as breast cancer. His research also explores the application of AI in environmental monitoring, specifically in the remote sensing of gas flares, which has implications for energy management and environmental protection. Dr. Boumaraf’s interdisciplinary approach combines theoretical research with practical applications, aiming to develop AI solutions that can be effectively integrated into various sectors.
Research Skills
Dr. Boumaraf possesses a comprehensive set of research skills that enable him to tackle complex problems in AI and its applications. His proficiency in deep learning frameworks such as TensorFlow and PyTorch allows him to design and implement sophisticated neural network architectures. He is skilled in image processing techniques, including segmentation, feature extraction, and classification, which are essential for medical image analysis and computer vision tasks. Dr. Boumaraf is adept at handling large datasets, employing data augmentation and preprocessing methods to enhance model performance. His experience with algorithm optimization and model evaluation ensures the development of efficient and accurate AI systems. Additionally, his collaborative work with multidisciplinary teams demonstrates his ability to integrate AI solutions into broader technological and scientific contexts. Dr. Boumaraf’s research skills are instrumental in advancing AI applications across various domains.
Awards and Honors
Throughout his career, Dr. Boumaraf has received recognition for his contributions to the field of artificial intelligence. His research publications in esteemed journals and conferences have garnered attention from the academic community, reflecting the impact of his work. While specific awards and honors are not detailed in the available information, his role as a Postdoctoral Fellow at a leading institution like Khalifa University signifies a level of esteem and acknowledgment of his expertise. Dr. Boumaraf’s ongoing collaborations and research endeavors continue to position him as a respected figure in the AI research community.
Conclusion
Dr. Said Boumaraf stands out as a dedicated researcher whose work bridges the gap between artificial intelligence theory and practical application. His contributions to computer vision and medical imaging demonstrate a commitment to developing AI solutions that address real-world challenges. Through his role at Khalifa University, Dr. Boumaraf continues to engage in cutting-edge research, collaborating with international teams to push the boundaries of what AI can achieve. His interdisciplinary approach and robust research skills make him a valuable asset to the scientific community, and his work holds promise for significant advancements in both environmental monitoring and healthcare diagnostics. As AI continues to evolve, researchers like Dr. Boumaraf play a crucial role in ensuring that these technologies are harnessed effectively for the betterment of society.
Publications Top Notes
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Title: A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images
Authors: S. Boumaraf, X. Liu, Z. Zheng, X. Ma, C. Ferkous
Year: 2021
Citations: 169
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Title: Conventional machine learning versus deep learning for magnification dependent histopathological breast cancer image classification: A comparative study with visual explanation
Authors: S. Boumaraf, X. Liu, Y. Wan, Z. Zheng, C. Ferkous, X. Ma, Z. Li, D. Bardou
Year: 2021
Citations: 83
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Title: A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
Authors: S. Boumaraf, X. Liu, C. Ferkous, X. Ma
Year: 2020
Citations: 80
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Title: A new three-stage curriculum learning approach for deep network based liver tumor segmentation
Authors: H. Li, X. Liu, S. Boumaraf, W. Liu, X. Gong, X. Ma
Year: 2020
Citations: 12
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Title: Deep distance map regression network with shape-aware loss for imbalanced medical image segmentation
Authors: H. Li, X. Liu, S. Boumaraf, X. Gong, D. Liao, X. Ma
Year: 2020
Citations: 11
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Title: A multi-scale and multi-level fusion approach for deep learning-based liver lesion diagnosis in magnetic resonance images with visual explanation
Authors: Y. Wan, Z. Zheng, R. Liu, Z. Zhu, H. Zhou, X. Zhang, S. Boumaraf
Year: 2021
Citations: 10
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Title: AI-enhanced gas flares remote sensing and visual inspection: Trends and challenges
Authors: M. Al Radi, P. Li, S. Boumaraf, J. Dias, N. Werghi, H. Karki, S. Javed
Year: 2024
Citations: 6
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Title: Web3-enabled metaverse: the internet of digital twins in a decentralised metaverse
Authors: N. Aung, S. Dhelim, H. Ning, A. Kerrache, S. Boumaraf, L. Chen, M.T. Kechadi
Year: 2024
Citations: 6
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Title: U-SDRC: a novel deep learning-based method for lesion enhancement in liver CT images
Authors: Z. Zheng, L. Ma, S. Yang, S. Boumaraf, X. Liu, X. Ma
Year: 2021
Citations: 5
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Title: Bi-Directional LSTM Model For Classification Of Vegetation From Satellite Time Series
Authors: K. Bakhti, M.E.A. Arabi, S. Chaib, K. Djerriri, M.S. Karoui, S. Boumaraf
Year: 2020
Citations: 5