Assoc. Prof. Dr. Fadi Sibai | Network | Best Researcher Award
Associate Professor at Gulf University for Science and Technology, Kuwait
Dr. Fadi Sibai is an accomplished Associate Professor at Gulf University for Science and Technology (GUST) in Kuwait, with over 35 years of experience in electrical and computer engineering, AI, cybersecurity, and academic leadership. He holds a Ph.D. in Electrical Engineering from Texas A&M University and has held senior roles in prestigious institutions like Intel, Saudi Aramco, and UAE University. Dr. Sibai’s interdisciplinary research focuses on AI, machine learning, cybersecurity, and digital design, with numerous publications, including over 78 journal articles and 10 books. He has received multiple prestigious awards, such as the IBM Faculty Award and nVIDIA Research Grant, and is recognized as a leading innovator in smart cities, healthcare, and AI ethics. His work has significantly impacted education, industry, and society, making him an ideal candidate for the Best Researcher Award.
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Educational Details
Dr. Sibai earned his Ph.D. and M.S. degrees in Electrical Engineering from Texas A&M University, specializing in computer engineering with minors in computer science and communications. His doctoral research focused on AI-based coprocessor architectures. He also received his B.S. in Electrical Engineering from the University of Texas at Austin, concentrating on computer science and engineering. His academic qualifications are bolstered by certifications such as PMP, CISSP, CQRM, and advanced studies at Stanford University.
Professional Experience
Dr. Sibai’s career spans academia, industry, and research leadership. He served as Dean of Engineering at Prince Mohammad Bin Fahd University, Dean at American International University Kuwait, and Associate Dean at GUST, where he played a key role in curriculum development, program accreditation (ABET, NCAAA), and launching AI and cybersecurity programs. In the industry, he held senior positions at Intel Corporation and Saudi Aramco, overseeing impactful projects in HPC, network security, robotics, and IT/OT integration. He has also been a consultant, educator, and researcher at various global institutions, including Walden University, KFUPM, and UAE University.
Research Interests
Dr. Sibai's research is interdisciplinary, focusing on AI, machine learning, computer and embedded systems, high-performance computing, digital design, data science, robotics, and cybersecurity. His recent work includes AI for smart cities , AI ethics and regulation , ensemble learning models in healthcare , and network-on-chip architectures. He is also an expert in neural networks , fault-tolerant computing, and energy-aware systems, blending theoretical modeling with real-world applications.
Author Metrics
Dr. Sibai has authored or co-authored over 250 scholarly publications, including 78+ journal articles, 10 books/book chapters, and numerous conference papers . Many of these are indexed in Scopus and IEEE Xplore, with his work being cited nearly 1,000 times. He is a long-time member of editorial boards and technical committees for top journals and conferences.
Awards and Honors
Dr. Sibai’s career has been recognized with numerous prestigious awards, including the IBM Shared University Research Award , IBM Faculty Award, nVIDIA Research Grant, and multiple Intel awards for innovation and performance excellence. He was named among IBC’s Top 100 Engineers and Educators and featured in Who’s Who in the World. He is also a distinguished speaker at international conferences and won the Best Research Project Award at UAE University. As a Senior IEEE Member and a member of organizations like PMI and ISC2, he is widely respected in both academia and industry.
1. ParaEyes: The Smart Eye Bot System for Paralyzed Patients
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Authors: M. Al Jaziri, G. Al Ali, H. Al Swailmeen, A.A. El-Moursy, F.N. Sibai
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Journal: Journal of Circuits, Systems and Computers, 2025
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Summary:
This paper presents ParaEyes, a smart assistive bot system that enables paralyzed patients to interact with their environment using eye movements and blinking patterns. Leveraging computer vision, machine learning, and sensor fusion, the system facilitates tasks such as communication and device control. The research highlights its real-world applicability in neurorehabilitation and patient autonomy, especially for individuals with ALS or spinal cord injuries.
2. Using The Cancer Genome Atlas from cBioPortal to Develop Genomic Datasets for Machine Learning Assisted Cancer Treatment
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Authors: A. Asaduzzaman, C.C. Thompson, F.N. Sibai, M.J. Uddin
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Platform: bioRxiv preprint, 2025.02.17.638660
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Summary:
This study extracts and preprocesses genomic data from The Cancer Genome Atlas (TCGA) using cBioPortal, creating machine-learning-ready datasets for the development of predictive models in personalized cancer treatment. It lays the foundation for using ML classifiers to assist oncologists in tailoring treatment plans based on genomic signatures, supporting precision oncology research.
3. The Robustness of AI-Classifiers in the Face of AI-Assisted Plagiarism: The Case of Turnitin AI Content Detector
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Authors: K. Ibrahim, D. Otaibi, F.N. Sibai
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Journal: International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT), 2025
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Summary:
This paper examines the reliability and vulnerability of AI-based plagiarism detection tools, particularly Turnitin’s AI Content Detector, in detecting content generated or modified by large language models (LLMs). The findings expose potential weaknesses in current detection algorithms and propose methods for enhancing AI classifier robustness in academic integrity systems.
4. Application of Ensemble Learning Models in Computer-Aided Diagnosis of Skin Diseases
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Authors: A. Asaduzzaman, C.C. Thompson, F.N. Sibai, M.J. Uddin
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Journal: Neural Computing and Applications, 2025
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Summary:
This research evaluates the performance of ensemble learning models (such as Random Forest, XGBoost, and AdaBoost) for diagnosing skin diseases using clinical and dermoscopic image data. The models showed high accuracy, sensitivity, and specificity, demonstrating that ensemble-based approaches can significantly enhance AI-aided dermatological diagnostics.
5. A Collaborative Adaptive Cybersecurity Algorithm for Cognitive Cities
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Authors: A. Abonamah, F.N. Sibai
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Journal: Journal of Computer Information Systems, 2025, pp. 1–16
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Summary:
This paper proposes a collaborative and adaptive cybersecurity framework designed for cognitive cities. It integrates machine learning algorithms and shared threat intelligence to enable smart urban infrastructure to defend against evolving cyber threats. The model improves resilience, detection, and adaptability in real-time, supporting safe digital environments in smart city ecosystems.
Conclusion
Assoc. Prof. Dr. Fadi Sibai is eminently qualified for the Best Researcher Award, standing out for his long-term research excellence, technological innovation, and strategic leadership in academic and applied AI domains. His work addresses societal challenges through AI, pushes forward smart infrastructure and cybersecurity, and contributes meaningfully to medical, ethical, and educational AI ecosystems.
His record of scholarship, innovation, mentorship, and global engagement makes him a deserving and ideal candidate for this prestigious honor. The award would not only recognize his decades of impact but also support his ongoing contributions to shaping the future of AI, education, and engineering.