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Distinguished Professor Jie Lu AO IEEE Fellow, IFSA Fellow, ACS Fellow, Australian Laureate Fellow Director of Australian Artificial Intelligence Institute University of Technology Sydney Australia |
Summary
The talk will present how machine learning can innovatively and effectively learn from data to support data-driven decision-making in uncertain and dynamic situations. A set of new autonomous transfer learning theories, methodologies and algorithms will be presented that can transfer knowledge learnt in more source domains to a target domain by building latent space, mapping functions and self-training to overcome tremendous uncertainties in data, learning processes and decision outputs. Another set of autonomous concept drift theories, methodologies and algorithms will be discussed about how to handle ever-changing dynamic data stream environments with unpredictable stream pattern drifts by effectively and accurately detecting concept drift in an explanatory way, indicating when, where and how concept drift occurs and reacting accordingly. These new developments enable advanced machine learning and therefore enhance data-driven prediction and decision support systems in uncertain and dynamic real-world environments.
Biography
Distinguished Professor Jie Lu is a world-renowned scientist in the field of computational intelligence, primarily known for her work in fuzzy transfer learning, concept drift, recommender systems, and decision support systems. She is an IEEE Fellow, IFSA Fellow, Australian Computer Society Fellow, and Australian Laureate Fellow. Professor Lu is the Director of the Australian Artificial Intelligence Institute (AAII) at University of Technology Sydney (UTS), Australia. She has published six research books and over 500 papers in leading journals and conferences; won 10 Australian Research Council (ARC) Discovery Projects and over 20 industry projects as leading chief investigator; and has supervised over 50 PhD students to completion. Prof Lu serves as Editor-In-Chief for Knowledge-Based Systems and International Journal of Computational Intelligence Systems. She is a recognized keynote speaker, delivering over 40 keynote speeches at international conferences. She is the recipient of two IEEE Transactions on Fuzzy Systems Outstanding Paper Awards (2019 and 2022), NeurIPS Outstanding Paper Award (2022), Australasian Artificial Intelligence Distinguished Research Contribution Award (2022), Australian NSW Premier's Prize on Excellence in Engineering or Information & Communication Technology (2023) and the Officer of the Order of Australia (AO) in the Australia Day 2023.
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Prof. Yannis Ioannidis President of the ACM University of Athens Greece |
Summary
As the modern extension of the ancient practice of storytelling, (interactive) digital storytelling relies on the use of multimedia assets to convey different types of stories. It has been used broadly as a term to cover a range of digital narratives from web-based stories to hypertexts, narrative computer games and even filmmaking. As a characteristic example, digital storytelling, or more generally story experiencing, has been established as an effective means of promoting visitor engagement in cultural sites, with significant potential to promote reflection and meaning making. This talk will present different approaches for story-centric experience design, focusing also on the role of agency, sociality, and personalization when combined with an interactive digital narrative. The application of interactive digital storytelling in different contexts and with a variety of technological solutions, from mobile-based or web technologies to desktop or immersive VR, has produced valuable insight as to the most effective use of novel technologies to engineer engagement.
Biography
Yannis Ioannidis is the President of the Association of Computing Machinery (ACM). He is a Professor at the Department of Informatics and Telecom of the University of Athens as well as an Associated Faculty at the “Athena” Research and Innovation Center, where he also served as the President and General Director for 10 years. His research interests include Database and Information Systems, Data Science, Data Infrastructures and Digital Repositories, Recommender Systems and Personalization, and Interactive Digital Storytelling, topics on which he has published over 180 articles in leading journals and conferences and holds four patents. His work is often inspired by and applied to data management and analysis problems that arise in industrial environments or in the context of other scientific fields (Social Sciences and Humanities, Life Sciences, Physical Sciences) and the Arts. He is an ACM and IEEE Fellow, a member of Academia Europaea, and a recipient of several research, teaching, and service awards. He is a co-founder of OpenAIRE, the international data infrastructure for Open Science in Europe, the technology director of the EBRAINS European Research Infrastructure on neuroscience, the coordinator of the implementation of the EU Node of the European Open Science Cloud, as well as of several AI/data-driven spin-offs. He is also a co-chair of the Global Climate Hub of the UN Sustainable Development Solutions Network.
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Prof. Mounir GHOGHO UM6P: University Mohammed VI Polytechnic Morocco |
Summary
As artificial intelligence systems increasingly migrate from centralized cloud infrastructures to distributed edge devices, the question of efficiency in computation, communication, and energy has become central to their viability. This talk examines recent advances in efficient and adaptive inference, the art and science of enabling complex models to operate under real-world constraints. I will discuss how techniques in model compression, adaptive computation, and hardware–algorithm co-design are reshaping the boundaries between intelligence and computation. Beyond specific methods, adaptive inference invites a broader reflection on how we balance accuracy, complexity, and autonomy when intelligence must coexist with scarcity. The edge, I will argue, is not merely a deployment context but a place where AI must learn to adapt and think frugally.
Biography
Mounir Ghogho received his PhD in Signal Processing from the National Polytechnic Institute of Toulouse, France, in 1997. He was a Research Fellow at the University of Strathclyde (Scotland) before joining the University of Leeds (England), where he became Full Professor and Head of the Signal Processing and Communications Group in 2008. In 2010, he joined the International University of Rabat as Founding Director of TICLab and Dean of the College of Doctoral Studies, while maintaining an affiliation with the university of Leeds. In March 2025, he joined the College of Computing at UM6P. His research focuses on machine learning and statistical signal processing, with applications in wireless communications, robotics, cybersecurity, and healthcare. He has published over 400 papers, supervised more than 50 PhD students, and led over 20 research projects funded by institutions such as the US Army Research Lab, EU Commission, NATO, USAID, IBM, Google, and The Academy Hassan II for Sciences and Techniques. He received the Royal Academy of Engineering Research Fellowship in 2000 and the IBM Faculty Award in 2013. He was elevated to IEEE Fellow in 2018, AAIA Fellow in 2021, and The World Academy of Sciences (TWAS) Fellow in 2024. He has served on editorial boards of leading journals, including IEEE Transactions on Signal Processing and IEEE Signal Processing Magazine, and is currently Subject Editor for Elsevier’s Signal Processing. He served as General Chair of several conference including IEEE SPAWC 2010 and EUSIPCO 2013.