His current interest is on building fundamental understanding of and developing enabling technologies for the upcoming Fifth Generation (5G) wireless networks. To this end, he is currently working on research topics such cache-aided wireless networks, energy efficient communications, energy harvesting systems, and information theoretical secrecy. His general area of interest is on communications networking and stochastic optimization.

Dr. Gürbüz’s research is on designing network algorithms and protocols for bandwidth and/or energy efficient wireless networking towards 5G systems. In this context, her research interests include, but not limited to:

• Medium Access Control (MAC), resource allocation and routing
• Cross layer design
• Video delivery and Quality of Service
• Cooperative communications and networking
• Full duplex wireless communications and networking
• Full duplex radio implementation

This area focuses on using AI and digital twin technology to optimize and secure next-generation wireless networks. AI-driven techniques improve network management, resource allocation, and adaptive control, while network digital twins enable real-time simulation, optimization, and resilience testing.

His research focuses on AI-driven optimization and digital twin modeling for wireless networks. He is working on developing learning-based methods for dynamic resource allocation, multi-connectivity strategies (e.g., MR-DC), and interference management in 5G/6G. He also builds digital twin frameworks to simulate network behavior, integrating real-time data for predictive analytics and optimization. His methods are applied in areas such as energy-efficient network operation, ultra-reliable low-latency communication (URLLC), and disaster recovery using digital twin-based simulations. These solutions help improve the efficiency, adaptability, and security of future wireless networks.