Biography: Suat Özdemir has been a faculty member in the Department of Computer Engineering at Hacettepe University in Ankara, Turkey since May 2020.  He is the head of Artificial Intelligence Engineering division of the department. Prior to this, he served at Gazi University’s Computer Engineering Department from 2007 to 2020. He holds an MSc in Computer Science from Syracuse University (2001) and a PhD in Computer Science from Arizona State University (2006). With a professional career that bridges academia and industry, Prof. Özdemir has maintained strong collaborations with the industrial sector for over a decade. From 2010 to 2014, he contributed as a senior researcher at TÜBİTAK, Turkey’s leading research institution, working on nationally significant projects. He is an active member of the IEEE and contributes to the global research community by serving as an editor, technical program committee member, and reviewer for numerous high-impact IEEE and ACM journals and conferences. His work spans areas of computer engineering with a particular emphasis on network security, IoT, and emerging computing paradigms. 

Title:  "Digital Twin-Driven Task Offloading for Next-Generation IoT Networks" 

Abstract:  As the Internet of Things (IoT) scales into complex, heterogeneous environments, traditional paradigms of static processing and centralized control fall short. The future of IoT demands networks that are intelligent, responsive, and capable of dynamic resource management. At the heart of this transformation lies the convergence of digital twins and task offloading, enabling a new generation of adaptive, efficient, and resilient IoT infrastructures. This keynote will present cutting-edge developments in digital twin-based modeling and its integration with task offloading strategies, particularly in fog and edge computing environments. The talk will also highlight our recent research on deep reinforcement learning frameworks for microservice offloading and digital twin-supported vehicular edge computing.

Moreover, we will explain how digital twins provide real-time, virtualized representations that enhance contextual awareness and decision-making for IoT tasks. Finally, scalable task offloading architectures and case studies demonstrating the operational impact of these technologies in smart city, transportation, and mission-critical applications will be evaluated as well.