Prof. Feifei Gao, IEEE Fellow

Department of Automation, Tsinghua University, China

Speech Title: Key Technologies and Prototype Design for Integrated Sensing and Communications System

Abstract: In the future, millions of base stations (BSs) and billions of users (UEs) will natively build an integrated sensing and communications (ISAC) system, which can utilize intelligent ubiquitous methods to realize the ultimate goal of sensing, i.e., constructing the global mapping from real physical world to digital twin world, while providing communications services at the same time. For this purpose, we conduct a series of theoretical and technical researches on ISAC, in which we decompose the real physical world into static environment, dynamic targets, and various object materials. The ubiquitous static environment occupies the vast majority of the physical world, for which we design static environment reconstruction (SER) scheme to obtain the layout and point cloud information of static buildings. The dynamic targets floating in static environments create the spatiotemporal transition of the physical world, for which we design comprehensive dynamic target sensing (DTS) scheme to detect, estimate, track, image and recognize the dynamic targets in real-time. The object materials enrich the electromagnetic laws of the physical world, for which we develop object material recognition (OMR) scheme to estimate the electromagnetic coefficient of the objects. Finally, based on these theoretical researches, we build an ISAC hardware prototype platform working in millimeter wave frequency band, realizing high-precision SER, DTS, and basic OMR, which provides preliminary verification for building the digital twin for communications networks.

Prof Kang Li, Chair of Smart Energy Systems

School of Electronic and Electrical Engineering, University of Leeds, UK

Speech Title: Machine Learning in Green Transportation – Some Case Studeis

Abstract: The transport decarbonization increases the coupling of the transport and energy systems, demanding a holistic and data-driven approach to manage and operate the systems with increased complexity. This talk presents several case studies in the application of machine learning techniques in holograph based state estimation and management of electric vehicles in complex urban traffic environments.

Prof. Chentao Wu, recipient of the National Youth Talent Program,

Shanghai Jiao Tong University, China

Speech Title: Failure Prediction for Large Scale Storage Devices

Abstract: Failure prediction for large-scale storage systems is a critical technology aimed at enhancing reliability and minimizing downtime in data-intensive environments. By leveraging advanced machine learning algorithms, statistical models, and real-time monitoring of hardware health indicators (e.g., SMART attributes), this approach identifies early warning signs of potential storage device failures. Proactive prediction enables timely maintenance, data migration, or component replacement, thereby preventing catastrophic data loss and service interruptions. Such systems are particularly vital for cloud platforms, data centers, and AI-driven infrastructures where petabytes of data are processed daily. Current research focuses on improving prediction accuracy through multimodal data fusion, adaptive thresholding, and anomaly detection. In this report, effective failure prediction methods will be introduced, which are not only optimize operational costs but also ensures seamless continuity in mission-critical applications, making it a cornerstone of modern storage architecture.

Prof. Yonghui Li, ARC Future Fellow, IEEE Fellow

School of Electrical and Information Engineering, University of Sydney, Sydney

Speech Title: Beyond 5G towards a Super-connected World

Abstract: Connected smart objects, platforms and environments have been identified as the next big technology development, enabling significant society changes and economic growth. The entire physical world will be connected to the Internet, referred to as Internet of Things (IoT). The intelligent IoT network for automatic interaction and processing between objects and environments will become an inherent part of areas such as electricity, transportation, industrial control, utilities management, healthcare, water resources management and mining. Wireless networks are one of the key enabling technologies of the IoT. They are likely to be universally used for last mile connectivity due to their flexibility, scalability and cost effectiveness. The attributes and traffic models of IoT networks are essentially different from those of conventional communication systems, which are designed to transmit voice, data and multimedia. IoT access networks face many unique challenges that cannot be addressed by existing network protocols; these include support for a truly massive number of devices, the transmission of huge volumes of data burst in large-scale networks over limited bandwidth, and the ability to accommodate diverse traffic patterns and quality of service (QoS) requirements. Some IoT applications have much stringent latency and reliability requirements which cannot be accommodated by existing wireless networks. Addressing these challenges requires the development of new wireless access technologies, underlying network protocols, signal processing techniques and security protocols. In this talk, I will present the IoT network development, architecture, key challenges, requirements, potential solutions and recent research progress in this area, particularly in 5G and beyond 5G.

Professor Pingyi Fan, IET Fellow

Academician of the United States National Academy of Artificial Intelligence

Tsinghua University, China

Speech Title: How information theory shapes the theory and practice of artificial intelligence

Abstract: The report mainly discusses the applications and challenges of information theory in the developments of Artificial Intelligence and Machine Learning, covering its application in generative models, reinforcement learning, distributed  federated learning etc. and the AI implementations in autonomous driving, semantic communication and other scenarios. At the same time, the challenges of AI interpretability, model design, and coordination with humans are considered, and the future research directions of the combination of information theory and AI is prospected.