Biography

Google Scholar

Abdulhamid Alsbakhi is a Lecturer in the School of Engineering, Applied Science and Technology at Canadian University Dubai, with over 13 years of experience in the higher education sector, including 5 years in academic teaching roles. He specializes in teaching Artificial Intelligence, Computer Networking, Computer Fundamentals, and Management Information Systems across both Engineering and Business schools. He is currently in the third year of his PhD in Computer Science at the University of Huddersfield, UK focusing on AI and Machine Learning, where his work bridges technical innovation with practical applications in education and industry.

Beyond teaching, Abdulhamid has contributed to student success through the supervision of projects, the organization of coding competitions, and the mentoring of teams that have achieved top rankings in regional IEEE competitions. He has also played active roles in conferences and outreach activities, including SMART24 and mobile development workshops, reinforcing his commitment to experiential learning and industry engagement.

His research focuses on interpretable AI for healthcare, particularly autism diagnosis, reflecting a strong interest in socially impactful, data-driven solutions. His published work includes:

Academic Publications

  • Alsbakhi, A., Lu, J., Thabtah, F., & Dyer, J. (2023). Interpretable Data Driven Classifiers: A proposal for Autism Diagnosis of Children Using Ensemble Learning. Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023, 1424–1431. https://doi.org/10.1109/CSCI62032.2023.00233
    DOI | Repository record

Academic Contributions

Selected Publications:

  • Alsbakhi, A., Thabtah, F., & Lu, J. (2025). Autism Data Classification Using AI Algorithms with Rules: Focused Review. Bioengineering, 12(2), 160. https://doi.org/10.3390/bioengineering12020160
  • Alsbakhi, A., Lu, J., Thabtah, F., & Dyer, J. (2023, December). Interpretable Data Driven Classifiers: A proposal for Autism Diagnosis of Children Using Ensemble Learning. In 2023 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1424-1431). IEEE.