Prof. Fakhri Karray (IEEE Fellow)
University of Waterloo, Canada & The Mohamed ben Zayed University of AI, UAE

Fakhri Karray is the inaugural co-director of the University of Waterloo Artificial Intelligence Institute and served as the Loblaws Research Chair in Artificial Intelligence in the department of electrical and computer engineering at the University of Waterloo, Canada. He is also Professor of Machine Learning and held the position of Provost at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), in the UAE. Fakhri's research focuses on operational and generative AI, cognitive machines, natural human-machine interaction, and autonomous and intelligent systems, with applications to virtual care systems, cognitive and self-aware devices, and predictive analytics in supply chain management and intelligent transportation systems. He aholds editorial roles in major publications related to intelligent systems and information fusion. Fakhri's latest textbook, "Elements of Dimensionality Reduction and Manifold Learning," was published by Springer Nature in early 2023. In 2021, he was honored by the IEEE Vehicular Technology Society (VTS) with the IEEE VTS Best Land Transportation Paper Award for his pioneering research on enhancing traffic flow prediction using deep learning and AI. Furthermore, his research on federated learning in communication systems earned him and his co-authors the 2022 IEEE Communication Society's MeditCom Conference Best Paper Award. Fakhri is also the co-founder and Chief Scientist of Yourika.ai, a provider of AI-based online learning systems. He holds fellowship status in the IEEE, the Canadian Academy of Engineering, and the Engineering Institute of Canada. Additionally, he has served as a Distinguished Lecturer for the IEEE and is a Fellow of the Kavli Frontiers of Science. Fakhri earned his Ph.D. from the University of Illinois Urbana-Champaign, USA.

Prof. Hong Zhu (IEEE Senior Member)
Oxford Brookes University, UK

Dr. Hong Zhu is a professor of computer science at the Oxford Brookes University, Oxford, UK, where he chairs the Cloud Computing and Cybersecurity Research Group. He obtained his BSc, MSc and PhD degrees in Computer Science from Nanjing University, China, in 1982, 1984 and 1987, respectively. He was a faculty member of Nanjing University from 1987 to 1998. He joined Oxford Brookes University in November 1998 as a senior lecturer in computing and became a professor in Oct. 2004. His research interests are in the area of software development methodologies, including software engineering of cloud-native applications, software engineering of AI and machine learning applications, formal methods, software design, software testing, programming languages, software modelling, and automated software engineering tools and environments, etc. He has published 2 books and more than 200 research papers in journals and international conferences. He is a senior member of IEEE, a member of British Computer Society and ACM.

Speech Title: Scenario-based Testing and Evaluation of LLMs Capability of Code Generation

Abstract: One of the most valuable capabilities of large language models (LLM) like GPT, Gemini, Codex and Falcon, etc. is to generate program code from natural language input. They have been widely employed in the IT industry. However, it is also widely reported that software developers are concerned with the quality of LLM generated code. It remains an open question that how to evaluate LLMs capability of code generation. Existing work on this subject has been focused on the functional correctness, yet the results reported in the literature are controversial. In this talk, we address the problem through a scenario-based approach to build a wide spectrum of the quality profile for each LLM on its capability of generating program code in Java. This quality profile of a LLM does not only cover the functional correctness but also its robustness, and usability on various quality attributes. We will share our novel technology that enables effective and efficient testing and evaluation via a benchmark marked-up by metadata and a multi-agent datamorphic test system to achieve test automation. We will also report our discoveries found in our experiments and discuss the directions for future research.

Prof. Fabrizio Lamberti (IEEE Senior Member)
Politecnico di Torino, Italy

Prof. Fabrizio Lamberti received the M.Sc. and the Ph.D. degrees in computer engineering from Politecnico di Torino, Italy, in 2000 and 2005, respectively. Currently, he is a Full Professor at the Department of Control and Computer Engineering, where he serves as Chair of the PhD Program in Computer and Control Engineering, is responsible for the “Graphics and Intelligent Systems” research laboratory and of the VR@POLITO hub. He co-authored more than 300 technical papers in the areas of computer graphics, computer vision, human-machine interaction, and intelligent systems, and has been the principal investigator for 40 research projects and grants funded by public bodies and private companies. He is a senior member of the IEEE and is currently serving as Chair for the IEEE Computer Society, Italy Chapter. In 2020 he was elected as BoG Member-at-Large (2021-2023 term) of IEEE Consumer Technology (CTSoc), for which he is now serving as VP Technical Activities and Chair of the TC Board. He is a Life Member of the Mu Nu Chapter of IEEE-EKN Honor Society. Since 2005 he has been involved in the Organizing and Technical Program Committees of more than 50 conferences. He has served as Associate Editor for IEEE Transactions on Computers, IEEE Transactions on Emerging Topics in Computing, and IEEE Transactions on Learning Technologies. He is currently serving as Associate Editor of IEEE Transactions on Visualization and Computer Graphics, IEEE Consumer Electronics Magazine, and the International Journal of Human-Computer Studies. He is a Senior Associate Editor of IEEE Transactions on Consumer Electronics. He has been appointed Editor in Chief of IEEE Consumer Electronics Magazine for 2025-2026 term.

Speech Title: Rethinking How We Teach and Train with eXtended Reality

Abstract: This keynote will explore the opportunities offered by eXtended Reality (XR) in education and professional training. XR technologies have demonstrated significant impact in these domains, enabling the recreation of complex scenarios in a consistent and controlled manner — even when such situations would be dangerous, impractical, or prohibitively expensive to reproduce in the real world. However, the benefits in terms of learning outcomes and user experience quality when using XR-based educational tools are often closely tied to the specific instructional objectives. The same applies when determining the most effective strategies for integrating XR into an educational programme. Moreover, technological choices can play a decisive role in the success of a given solution. Building on these considerations, the presentation will showcase a series of initiatives undertaken in recent years by the VR@POLITO laboratory (https://vr.polito.it/) at the Department of Control and Computer Engineering, Politecnico di Torino, Italy. These case studies aim to offer valuable insights for future research and to support the ongoing evolution of XR technologies in learning contexts.