ABSTRACT: The convergence of advanced AI models and next-generation connectivity is creating a new computing paradigm:hybrid AI across the edge-cloud continuum, set to transform sectors such as telecom networks, connected vehicles,industrial IoT, and multimedia analytics. Meanwhile, multi-access edge computing (MEC) and 5G/6G radio accessenable real-time insights by processing data near devices. Hybrid AI is envisaged to push the boundaries of dataprocessing and machine learning through dynamic model partitioning and offloading, cloud-assisted training with on-device inference, and privacy-preserving federated learning at scale. Hence, how to design, deploy, and govern thesesystems - balancing latency, energy, cost, and trust - has become a major objective for researchers and practitioners.

The panelists, with diverse backgrounds and outstanding accomplishments, will present their views on the challengesand strategies to realize hybrid AI across the edge–cloud continuum. The session will then be open for discussionsfrom the floor.

PANELISTS:

• Prof. Paul Patras, University of Edinburgh
• Dr. Nektarios Georgalas, British Telecom (BT)
• Prof. Liangxiu Han, Manchester Metropolitan University
• Mr. Stefanos Laskaridis, Amazon Science
• Dr. Xinyi Lin, Toshiba Bristol Research and Innovation Laboratory (BRIL)
• Dr. Simone Mangiante, Vodafone

BIO: Paul Patras is a Professor of Mobile Intelligence in the School of Informatics at the University of Edinburgh and co-founder/CEO of Net AI. Dr Patras has over 10 years of experience of leading research at the intersection of mobile networking, artificial intelligence and security, some of which has been transferred from the lab into commercial products. He has spearheaded the use of deep learning to solve several problems in the mobile networking domain, which were previously considered intractable.

BIO: Nektarios Georgalas is an Innovation Principal (Data & AI) at BT and Industrial Principal Investigator across three pillars at the government-funded BT Ireland Innovation Centre (BTIIC) in Northern Ireland: Autonomous IoT, Process Mining & Analytics, and Trustworthy and Explainable AI. At BTIIC, Nektarios leads multi-disciplinary teams with university partners, BT Research and BT Engineering, suppliers and customers to realise the Autonomous IoT vision—self-serviced and self-managed IoT Ecosystems by means of AI, machine learning, advanced analytics, Edge/Fog/Cloud Computing, IoT SLA management and optimisations. Nektarios’ contributions have been recognised with 22 International and BT honours as well as IEEE service: two IEEE Outstanding Awards and two IEEE Outstanding Leadership Awards. Nektarios is inventor/co-inventor of 20 patents and author of 100+ peer-reviewed papers in high impact factor IEEE Journals and Conferences.

BIO: Prof. Han is currently a full Professor of Computer Science at the Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University. Prof. Han’s research areas mainly lie in the development of novel big data analytics/Machine Learning/AI, and development of novel intelligent architectures that facilitates big data analytics (e.g., parallel and distributed computing, Cloud/Service-oriented computing/data intensive computing) as well as applications in different domains (e.g. Precision Agriculture, Health, Smart Cities, Cyber Security, Energy, etc.) As a Principal Investigator (PI) or Co-PI, Prof. Han has a proven track record of successfully leading multi-million-pound projects on both national and international scales (supported by diverse funding sources: UKRI, GCRF/Newton, EU, Industry, and Charity) and has extensive research and practical experiences in developing intelligent data driven AI solutions for various application domains (e.g. Health, Food, Smart Cities, Energy, Cyber Security) using various large datasets (e.g. images, numerical values, sensors, geo-spatial data, web pages/texts).

BIO: Stefanos is a Research Scientist specialising in Machine Learning (ML), Distributed & Mobile Systems, and Efficient ML algorithms. His research interest revolves around the areas of dynamic network architectures, federated/collaborative learning, on-device AI as well resource and energy-efficient deep learning. Currently, he is an Applied Scientist at Amazon Science, where he focuses on Large Language Models (LLMs) for Alexa+, particularly in the areas of self-learning and speculative decoding for efficient inference. He is a Cambridge graduate and has held previous research positions at Brave, Samsung AI, the University of Cambridge and CERN. His work has been featured in various top-tier venues, including NeurIPS, ICML, MobiCom and ECCV, among others, and has led the organisation of numerous workshops and tutorials on On-Device Computing, Distributed ML and Federated Learning.

BIO: Xinyi Lin received the B.Eng. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2020, and the Ph.D. degree from the University of Glasgow, Glasgow, U.K., in 2024, supported by the EPSRC iCASE Studentship with British Telecom. She is currently a Research Engineer at Toshiba Bristol Research and Innovation Laboratory (BRIL), U.K., where she contributes to the EU 6G SNS 6G-Goals project. Her research interests include reconfigurable intelligent surfaces (RIS), semantic communication, and network optimization. Dr. Lin was recognized as a 2023 Wireless Communication Letters Exemplary Reviewer and serves on the Technical Program Committees for ICC 2025 and MECOM 2024 & 2025.

BIO: Dr. Simone Mangiante received his PhD in Computer Networks in 2013 from the University of Genoa in Italy, working on Carrier Ethernet and SDN. He then spent three years with Dell EMC in Ireland as a senior research scientist, where he managed European projects on SDN and network transport. He led the design and deployment of an industrial IoT testbed and contributed to several H2020 EU proposals. He is currently a senior researcher in Vodafone Group R&D in the UK where he focuses on machine learning algorithms for future networks and services, and synthetic data generation to make data available to innovation projects. His main research interests are edge computing, distributed cloud architecture and machine learning for computer networks.