Biography

Dr. Sahil Garg is an Associate Professor at the School of Engineering, Applied Science, and Technology, Canadian University, Dubai, where he also leads the Master of Science in Artificial Intelligence (MScAI) program as the Program Coordinator. He chairs the AI/ML Research Group at CUD, which focuses on advancing innovative research and applications in the field. 

With more than seven years of professional experience bridging academia and industry, he has developed deep expertise in AI, machine learning, and advanced computing. He has authored over 200 high-impact publications in leading journals and conferences, which have collectively received over 10,000 citations according to Google Scholar, reflecting his substantial influence on the field. 

He has been actively involved in editorial leadership for several top-tier journals, shaping research directions in his fields of expertise, and has participated in numerous international conferences in diverse roles, supporting the advancement of AI, machine learning, and related technologies. His contributions have been recognized with multiple Early Career Research Awards and Best Paper Awards, reflecting his significant impact and leadership in the field.

Dr. Garg is listed among the top 2% of researchers worldwide by Stanford University and is one of the Highly Ranked Scholars™ in ScholarGPS, placing him in the top 0.05% of scholars globally.

Selected Publications

• V. Kumar, I. Budhiraja, A. Singh, S. Garg, G. Kaddoum, and M. M. Hassan, “Energy efficient resource allocation and trajectory optimization method for secure digital twin-enabled UAV-assisted MEC in 6G networks,” Computer Networks, p. 111679, 2025.
• M. Wazid, J. Singh, A. K. Das, S. Garg, W. Susilo, and M. M. Hassan, “Cloud storage-based secure big data analytics mechanism for drone-assisted healthcare 5.0 data fusion system,” Information Fusion, vol. 121, p. 103085, 2025.
• J. Liu, H. Lin, X. Wang, L. Wu, S. Garg, and M. M. Hassan, “Reliable trajectory prediction in scene fusion based on spatio-temporal Structure Causal Model,” Information Fusion, vol. 107, p. 102309, 2024.
• J. Miao, Z. Wang, M. Wang, S. Garg, M. S. Hossain, and J. J. P. C. Rodrigues, “Secure and efficient communication approaches for Industry 5.0 in edge computing,” Computer Networks, vol. 242, p. 110244, 2024.
• T. Fu, L. Wang, S. Garg, M. S. Hossain, Q. Yu, and H. Hu, “Adaptive signal light timing for regional traffic optimization based on graph convolutional network empowered traffic forecasting,” Information Fusion, vol. 103, p. 102072, 2024.
• X. Wang, J. Liu, H. Lin, S. Garg, and M. Alrashoud, “A multi-modal spatial–temporal model for accurate motion forecasting with visual fusion,” Information Fusion, vol. 102, p. 102046, 2024.
• Z. Zhen, X. Wang, H. Lin, S. Garg, P. Kumar, and M. S. Hossain, “A dynamic state sharding blockchain architecture for scalable and secure crowdsourcing systems,” Journal of Network and Computer Applications, vol. 222, p. 103785, 2024.
• B. Xu, H. Zhao, H. Cao, S. Garg, G. Kaddoum, and M. M. Hassan, “Edge aggregation placement for semi-decentralized federated learning in Industrial Internet of Things,” Future Generation Computer Systems, vol. 150, pp. 160–170, 2024.
• C. Tian, H. Cao, J. Xie, S. Garg, M. Alrashoud, and P. Tiwari, “Community Detection-Empowered Self-Adaptive Network Slicing in Multi-Tier Edge-Cloud System,” IEEE Transactions on Network and Service Management, vol. 21, no. 3, pp. 2624–2636, 2023.
• S. Liang, H. Wu, L. Zhen, Q. Hua, S. Garg, G. Kaddoum, M. M. Hassan, and K. Yu, “Edge YOLO: Real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 25345–25360, 2022.
• X. Wang, S. Garg, H. Lin, J. Hu, G. Kaddoum, M. J. Piran, and M. S. Hossain, “Toward accurate anomaly detection in industrial internet of things using hierarchical federated learning,” IEEE Internet of Things Journal, vol. 9, no. 10, pp. 7110–7119, 2021.
• Y. Liu, S. Garg, J. Nie, Y. Zhang, Z. Xiong, J. Kang, and M. S. Hossain, “Deep anomaly detection for time-series data in industrial IoT: A communication-efficient on-device federated learning approach,” IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6348–6358, 2020.
• Y. Qu, S. R. Pokhrel, S. Garg, L. Gao, and Y. Xiang, “A blockchained federated learning framework for cognitive computing in industry 4.0 networks,” IEEE Transactions on Industrial Informatics, vol. 17, no. 4, pp. 2964–2973, 2020.
• V. Hassija, V. Chamola, S. Garg, D. N. G. Krishna, G. Kaddoum, and D. N. K. Jayakody, “A blockchain-based framework for lightweight data sharing and energy trading in V2G network,” IEEE Transactions on Vehicular Technology, vol. 69, no. 6, pp. 5799–5812, 2020.
• X. Lin, J. Wu, S. Mumtaz, S. Garg, J. Li, and M. Guizani, “Blockchain-based on-demand computing resource trading in IoV-assisted smart city,” IEEE Transactions on Emerging Topics in Computing, vol. 9, no. 3, pp. 1373–1385, 2020.
• S. Garg, K. Kaur, S. Batra, G. Kaddoum, N. Kumar, and A. Boukerche, “A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications,” Future Generation Computer Systems, vol. 104, pp. 105–118, 2020.
• S. Garg, K. Kaur, N. Kumar, G. Kaddoum, A. Y. Zomaya, and R. Ranjan, “A hybrid deep learning-based model for anomaly detection in cloud datacenter networks,” IEEE Transactions on Network and Service Management, vol. 16, no. 3, pp. 924–935, 2019.
• S. Garg, K. Kaur, N. Kumar, and J. J. P. C. Rodrigues, “Hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in SDN: A social multimedia perspective,” IEEE Transactions on Multimedia, vol. 21, no. 3, pp. 566–578, 2019.
• S. Garg, K. Kaur, G. Kaddoum, and K. K. R. Choo, “Toward secure and provable authentication for Internet of Things: Realizing industry 4.0,” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4598–4606, 2019.