AI/ML group

AI/ML group

The AI/ML Research Group is committed to advancing the field of artificial intelligence and machine learning through groundbreaking research and practical applications. Our experts specialize in deep learning, innovating new architectures and techniques that drive state-of-the-art performance across diverse domains. We are also pioneers in areas such as computer vision, natural language processing, and reinforcement learning, crafting solutions that integrate seamlessly with modern industry needs and help shape the future of intelligent systems.

Students Benefits

Goals:

  • High-Impact Publications: We strive to publish our cutting-edge discoveries in leading journals, contributing to the scientific community’s knowledge base on AI/ML methodologies, tools, and best practices.
  • Global Conference Engagement: We actively present our findings at top-tier conferences, fostering collaboration with international experts and pushing the boundaries of what artificial intelligence and machine learning can achieve.

Publications:

  • Kamalov, F., Calonge, D. S., Hultberg, P. T., Smail, L., & Jamali, D. (2025). Comparative analysis of leading artificial intelligence chatbots in the context of entrepreneurship. Journal of Innovation and Entrepreneurship, 14(1), 58.
  • Elgazwy, A., Elgazzar, K., & Khamis, A. (2025). Predicting Pedestrian Crossing Intentions in Adverse Weather With Self-Attention Models. IEEE Transactions on Intelligent Transportation Systems.
  • Zhang, C., He, Q., Li, F., Wang, X., Garg, S., Han, M. S. H. Z., & Yuan, W. (2025). GAI based Resource and QoE Aware Service Placement in Next-Generation Multi-domain IoT Networks. IEEE Transactions on Cognitive Communications and Networking.
  • Zaki, A. M., Elsayed, S. A., Elgazzar, K., & Hassanein, H. S. (2025). Quality and Budget-Oriented Task Offloading for Vehicular Cooperative Perception Using Reinforcement Learning. IEEE Internet of Things Journal.
  • Elreedy, D., Atiya, A. F., & Kamalov, F. (2024). A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning. Machine Learning, 113(7), 4903-4923.
  • Dai, C., He, G., Guo, B., Garg, S., Kaddoum, G., & Hassan, M. M. (2025). Position-aware structural knowledge sharing-based federated graph learning for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems.

Stanford Top 2% (2024)

  • Firuz Kamalov (rank 19,620)
  • Sahil Garg (rank 28,135)

 

Group members:

Dr. Sahil Garg

Chair

Mr. Abdulhamid M.A. Alsbakhi

Member

Dr. Khalid Elgazzar

Member

Dr. Haytham El-Messiry

Member

Dr. Rita Zgheib

Head of Research Group

Dr. MohammadNoor Injadat

Member

Dr. Kuljeet Kaur

Member

Dr. Firuz Kamalov

Member