Keynotes

Speaker: Prof. Mohamed Younis, 

Department of Computer Science and Electrical Engineering, 

University of Maryland Baltimore County

younis@umbc.edu

Keynote: Hardware-assisted IoT Security 

Abstract: The notion of the Internet of Things (IoT) has emerged to characterize the internetworking of numerous and diverse devices to form ubiquitous computing systems that enable probing the environment, sharing data, and controlling physical processes. IoT applications can be found in many sectors such as utility infrastructure, healthcare and transportation. The societal impact and role of IoT elevate the importance of guarding it against security threats. However, countering cyberattacks in IoT is more challenging than in traditional networks due to the wide range of communication protocols and limited capabilities of the involved devices. Security threats for IoT devices range from malicious impersonation and denial of service to leaking sensitive information. The resource limitation restrains the applicability of elaborate security solutions, and mandates the use of lightweight primitives. This talk will highlight how to achieve the security goals through the incorporation of simple tamper-proof hardware circuits that act as a device fingerprint. Specifically, the advantages and challenges of the use of physical unclonable functions (PUFs) will be highlighted. A number of protocols and application scenarios will be discussed where PUFs are employed to safeguard IoT devices against cyberattacks. The talk also will highlight the vulnerabilities of PUFs and present sample protection mechanisms.

Bio: Mohamed F. Younis is currently a professor and chair of the department of computer science and electrical engineering at the University of Maryland Baltimore County (UMBC). Before joining UMBC, he was with Honeywell International Inc., where he led multiple projects for building dependable computing infrastructure. He also participated in the development of the Redundancy Management System, which is a key component of the Vehicle and Mission Computer for NASA’s X-33 space launch vehicle. Dr. Younis’ technical interest includes network architectures and protocols, applications of artificial intelligence, cyber-physical systems, intelligent transportation systems, secure communication and IoT networks. He has published over 350 technical papers in refereed conferences and journals. His contribution to science and engineering is acknowledged by the large number of (over 26 thousand) citations of his work and his high h-index (62). He is also deemed among the top 2% of world scientists in the 2024 rankings by Stanford University. Dr. Younis has nine granted and three pending patents. In addition, he serves/served on the editorial board of multiple journals and the organizing and technical program committees of numerous conferences.  Dr. Younis is a Fellow of the IEEE and the IEEE communications society. 

Speaker: Prof. Merouane Debbah, 

Khalifa University, Abu Dhabi, UAE

Keynote: TelecomGPT: Next Generation AI Telecom Network 

Abstract: The evolution of generative artificial intelligence (GenAI) constitutes a turning point in reshaping the future of technology in different aspects. Wireless networks in particular, with the blooming of self-evolving networks, represent a rich field for exploiting GenAI and reaping several benefits that can fundamentally change the way how wireless networks are designed and operated nowadays. To be specific, large language models (LLMs), a subfield of GenAI, are envisioned to open up a new era of autonomous wireless networks, in which a multimodal large model trained over various Telecom data, can be fine-tuned to perform several downstream tasks, eliminating the need for dedicated AI models for each task and paving the way for the realization of artificial general intelligence (AGI)-empowered wireless networks. In this talk, we aim to unfold the opportunities that can be reaped from integrating LLMs into the Telecom domain. In particular, we aim to put a forward-looking vision on a new realm of possibilities and applications of LLMs in future wireless networks, defining directions for designing, training, testing, and deploying Telecom LLMs, and reveal insights on the associated theoretical and practical challenges.

Bio:  Mérouane Debbah is Professor at  Khalifa University of Science and Technology in Abu Dhabi and founding Director of the KU 6G Research Center. He is a frequent keynote speaker at international events in the field of telecommunication and AI. His research has been lying at the interface of fundamental mathematics, algorithms, statistics, information and communication sciences with a special focus on random matrix theory and learning algorithms. In the Communication field, he has been at the heart of the development of small cells (4G), Massive MIMO (5G) and Large Intelligent Surfaces (6G) technologies. In the AI field, he is known for his work on Large Language Models, distributed AI systems for networks and semantic communications. He received multiple prestigious distinctions, prizes and best paper awards (more than 40 IEEE best paper awards) for his contributions to both fields and according to research.com is ranked as the best scientist in France in the field of Electronics and Electrical Engineering. He is an IEEE Fellow, a WWRF Fellow, a Eurasip Fellow, an AAIA Fellow, an Institut Louis Bachelier Fellow, an AIIA Fellow and a Membre émérite SEE. His recent work led to the development of NOOR (upon it release, largest language model in Arabic) released in 2022, Falcon LLM (upon its release, top ranked open source large language model) released in 2023 and the Falcon Foundation in 2024. The Falcon Model Series and The Falcon Foundation have positioned the UAE as a global leader in the generative AI field. He is actually chair of  the IEEE Large Generative AI Models in Telecom (GenAINet) Emerging Technology Initiative and  a member of the Marconi Prize Selection Advisory Committee.