Keynote Speakers

Kin K. Leung

Tanaka Chair Professor, Imperial College, London

Kin K. Leung received his M.S. and Ph.D. degrees from University of California, Los Angeles. He worked at AT&T Bell Labs and its successor companies in New Jersey from 1986 to 2004. Since then, he has been the Tanaka Chair Professor in the Electrical and Electronic Engineering and Computing Departments at Imperial College in London. He was the Head of Communications and Signal Processing Group from 2019 to 2024 and now serves as Co-Director of the School of Convergence Science in Space, Security and Telecommunications at Imperial. His current research focuses on optimization and machine learning for design and control of large-scale communications, computer and quantum networks. He also works on multi-antenna and cross-layer designs for wireless networks. He is a Fellow of the Royal Academy of Engineering, IEEE Fellow, IET Fellow, and member of Academia Europaea. He received the Distinguished Member of Technical Staff Award from AT&T Bell Labs (1994) and the Royal Society Wolfson Research Merits Award (2004-09). Jointly with his collaborators, he received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize (2021), the IEEE ComSoc Best Survey Paper Award (2022), the U.S.–UK Science and Technology Stocktake Award (2021), the Lanchester Prize Honorable Mention Award (1997), and several best conference paper awards. He chaired the IEEE Fellow Evaluation Committee for ComSoc (2012-15) and serves as the General Chair of the IEEE INFOCOM 2025. He has served as an editor for 10 IEEE and ACM journals and chaired the Steering Committee for the IEEE Transactions on Mobile Computing. Currently, he is an editor for the ACM Computing Survey and International Journal of Sensor Networks.

Keynote Title: Optimization and Federated Learning for Edge Computing with Resource Constraints

Abstract: Allocation of limited resources to competing demands is an important problem for efficient design and management of computing services at network edge. The speaker will first present a machine-learning method by using two Coupled Long Short-Term Memory (CLSTM) networks to quickly and robustly produce the optimal or near-optimal resource allocation. Numerical examples will be presented to show the effectiveness of the proposed method. The speaker will then give an overview of new approaches to supporting federated learning (FL) and improving the learning process by model pruning and split learning. The FL learns the model parameters from distributed data and adapts according to the limited availability of resources. The key idea of model pruning is to remove unimportant model parameters to reduce computation and communication burden, while split learning divides the model and learning between the server and user sides. Experimentation results show that the proposed approaches perform near to the optimum or offer significant performance improvement over other methods.

Alessandro Di Nuovo

Professor of Machine Intelligence, Sheffield Hallam University

Professor Alessandro Di Nuovo is Professor of Machine Intelligence at the Department of Computing, Sheffield Hallam University (SHU), and leads the AI, Robotics and Digital research theme at the Advanced Wellbeing Research Centre. He is internationally recognized for his contributions to human-robot interaction and AI applications in wellbeing and assistive technologies. Professor Di Nuovo is the founder and leader of the Smart Interactive Technologies (SIT) Research Laboratory—an internationally renowned group equipped with cutting-edge facilities for research in machine intelligence, neuro-developmental robotics, and cognitive mechatronics. The SIT Lab collaborates with a global network of academic and industrial partners and leads a £10 million portfolio of interdisciplinary projects funded by the European Union, UKRI, US ASFOR, and charitable organizations.

Keynote Title: How Bio-Inspired Robotic Platforms Can Foster the Development of AI Services to Benefit Society

Abstract: Recent technological advancements have fuelled the growth of artificial intelligence (AI), making it increasingly accessible for real-world applications. Bio-inspired robotic platforms offer innovative ways to develop AI services that benefit society. These robots provide a physical embodiment that plays a critical role in cognitive processes, enabling the creation of intelligent, autonomous agents with enhanced social capabilities. An emerging field in this domain is Neuro-Robotics, which integrates computational intelligence, neuroscience, and robotics to develop artificial models of minds with human-like learning abilities rooted in biological cognition. Multidisciplinary research in health and well-being has demonstrated that advanced robots can deliver personalized assistance to individuals, such as children with autism or older adults experiencing cognitive decline, while simultaneously supporting caregivers. This talk will present the latest research findings and offer an overview of the current state-of-the-art in social robotics. It will explore the benefits, limitations, and potential breakthroughs of this technology, with a focus on conducting responsible research that empowers people rather than replacing them.

Thomas Nowotny

Head of AI Research Group, Sussex AI, University of Sussex

Thomas Nowotny has a background in theoretical and mathematical physics, with a Diplom (MSc) in theoretical Physics at Georg-August Universität Göttingen and a PhD in theoretical Physics at Universität Leipzig. After his PhD, he worked at the Institute for Nonlinear Science at the University of California, San Diego where he conducted research in Computational Neuroscience and bio-inspired AI. In 2007, he moved to the University of Sussex as an RCUK Academic Fellow and rapidly climbed the ranks to Professor of Informatics in the School of Engineering and Informatics. He is the head of the AI research group and one of two directors of the “Sussex AI” Centre of Excellence. His research spans insect olfaction, artificial olfaction, insect navigation, insect-inspired machine learning models, hybrid brain-computer systems and neuromorphic computing. He is the creator of the research software STDPC for hybrid brain-computer experimentation and GeNN/mlGeNN for simulating spiking neural networks and performing event-based machine learning. His recent work is focused on decarbonising AI by developing event-based neural networks that run on neuromorphic accelerators to save orders of magnitude of energy in machine learning.

Keynote Title: Decarbonising AI with event-based neural networks

Abstract: This talk is about how to train event-based neural networks for more energy-efficient AI. I will briefly discuss the adjoint method for calculating exact gradients in event-based neural networks and demonstrate how it scales beneficially with the length of the input sequence. I will also discuss the deployment of trained networks on the Intel Loihi 2 neuromorphic system and the achievable energy savings. I will conclude with a discussion of the potential of event-based neural networks for AI and neuromorphic computing.