Prof. Mang Ye

Wuhan University, China

Prof. Mang Ye is a Professor at the School of Computer Science and the Head of the Department of Intelligent Science at Wuhan University, China. In 2021, he was selected for the National Overseas High-level Young Talents Program and is also a Clarivate Analytics Highly Cited Researcher. His long-term research focuses on areas such as multimodal computing and medical artificial intelligence. He has published over 100 CCF-A category papers as the first/corresponding author, accumulating more than 15,000 citations on Google Scholar, with a single paper receiving over 2,500 citations. He serves as an Editorial Board member for prestigious CCF-A journals including IEEE TIP and IEEE TIFS, and holds roles as a Senior Area Chair or Area Chair for conferences such as CVPR, ICLR, NeurIPS, ICML, and AAAI. Professor Ye has led over 10 scientific research projects, including the NSFC-RGC Joint Research Scheme and projects under the National Key R&D Program of China. He has been consecutively named to the Stanford University list of the "World's Top 2% Most-Cited Scientists" and has received honors such as the Baidu AI Young Scholar.

Title: Multimodal Large Language Models: Continual Learning and Safe Tuning 

Abstract: As Multimodal Large Language Models (MLLMs) demonstrate exceptional capabilities in understanding content across various modalities such as text and images, they have become a focal point of cutting-edge research in artificial intelligence. However, two primary challenges constrain their deployment and expansion in the dynamic real world. On one hand, models often forget previously learned knowledge when acquiring new information, which necessitates the ability for continual learning, much like humans. On the other hand, the vast number of parameters and substantial computational resource requirements present significant obstacles for adapting these models to different application scenarios, highlighting the critical importance of efficient tuning. This report will introduce our latest advancements in addressing these two challenges and provide an outlook on future research directions, aiming to offer insights for building the next generation of more flexible, scalable, and cost-effective artificial intelligence systems.

Prof. Thippa Reddy Gadekallu

Zhejiang A&F University, China

(IEEE Senior Member)

Prof. Thippa Reddy Gadekallu is currently working as a professor at The College of Mathematics and Computer Science, Zhejiang A&F University, China, as well as Chief Engineer at Zhongda Group. He received his Ph.D. from Vellore Institute of Technology, India, in 2017. With over 15 years of teaching experience, he has published more than 300 papers in reputed journals and conferences. His research areas include Machine Learning, Internet of Things (IoT), Deep Neural Networks, Blockchain, and Computer Vision. He serves as an editor for several publishers including Springer, Hindawi, PLOS ONE, Scientific Reports (Nature), and Wiley, and has acted as a guest editor for IEEE, Elsevier, Springer, Hindawi, and MDPI. He has been consecutively recognized among the world's top 2% scientists by Elsevier for the years 2021, 2022, 2023, and 2024. In 2023, he was recognized as a Highly Cited Researcher (Young Scientist Category) by Clarivate (Web of Science) for the period 2017-2022.

Title: Agentic AI in Healthcare 5.0: From Autonomous Systems to Symbiotic Care Ecosystems

Abstract: Healthcare 5.0 represents a major shift toward human-centered, resilient, and cooperative human-AI collaboration. This keynote introduces agentic AI, which are goal-directed systems with long-term memory, multi-step planning, and the ability to use tools. These systems serve as the main organizing layer for the future of healthcare. I will present a reference architecture, a systematic classification of applications, real-world case studies from industry leaders, and an important analysis of safety, security, and ethical issues. The talk will conclude with a step-by-step roadmap for responsible deployment, highlighting that the most effective systems are those with carefully balanced autonomy and strong human oversight, rather than those that rely solely on automation.

Prof. Hongbing Cheng

Zhejiang University of Technology, China

(IEEE Member)

Prof. Hongbing Cheng (Member IEEE) is a Professor at the School of Computer Science, Zhejiang University of Technology. He received his Ph.D. degree from the Nanjing University of Posts and Telecommunications and completed post-doctoral research at the State Key Laboratory for Novel Software Technology, Nanjing University. He has published over 100 technical papers in prestigious venues such as IEEE ToN, TFS, TSC, TIFS, TNSM, as well as ICDCS and ICC. Prof. Cheng has served as an invited editor for several international journals and as a session chair/committee member for various international conferences. His research interests encompass blockchain, cryptography, privacy preserving and information security, computer communications, and cloud computing security.

Tile: Scalable edge server deployment stratege based on spectral clustering and deep reinforcement learning

Abstract: With the rapid development of 5G/6G networks, the Internet of Things (IoT), and artificial intelligence, emerging applications such as intelligent transportation, video surveillance, and industrial internet are imposing increasingly stringent requirements on low latency, high reliability, and computational capability. Traditional cloud computing, which relies on centralized data centers, suffers from significant transmission delays and bandwidth constraints, making it inadequate for latency-sensitive and large-scale applications. To address these limitations, edge computing has emerged as a promising paradigm by bringing computation and storage resources closer to end users. This paradigm effectively alleviates network congestion, reduces transmission latency, and improves system responsiveness and service quality. As a result, edge computing is becoming a fundamental infrastructure for supporting smart cities, intelligent manufacturing, and ubiquitous connectivity in next-generation information systems.

Prof. Dr. Angela Lee Siew Hoong

Sunway University, Malaysia

(FHEA, IEEE, MBOT, AIS, IFIP, MYAIS)

Prof. Dr Angela Lee Siew Hoong is the Deputy Dean (Alumni & Industry Engagement) at the School of Engineering and Technology, Sunway University, Malaysia. As a pioneer in data science education in Malaysia, she spearheaded the introduction of Sunway University's Bachelor of Data Science programme in 2009—one of the earliest in Southeast Asia—and has continuously led curriculum innovation for over a decade. She is a two-time recipient of the SAS Global Forum Outstanding Educator Award (Asia Pacific, 2021 & 2024) and received the National Outstanding Educator Award in 2023. With extensive publications in healthcare analytics, technology adoption, predictive modeling, and AI education, she serves on the editorial boards of the Data Science Journal, Journal of Business Administration Research, Journal of Industrial Applications of Information Technology, and Journal of Computational Algorithms. An active reviewer for international journals and conferences, she founded the SAS User Group Malaysia and the Sunway Analytics Society. A frequent keynote speaker, she promotes AI, data analytics, and industry collaboration to drive innovation, healthcare transformation, and graduate employability.

Title: AI for Longevity: Redefining Health and Aging in a Connected World

Abstract: The rise of ageing populations presents one of the most significant healthcare challenges of our time. This keynote explores how Artificial Intelligence (AI), combined with Internet of Things (IoT) technologies and predictive analytics, is redefining health and longevity in a connected world. By harnessing continuous data from smart devices, AI can anticipate health risks, detect subtle behavioural changes, and support proactive interventions that enhance quality of life and extend healthy ageing. Lessons from pandemic data underscore the urgency of scalable, AI-driven health frameworks, while blockchain offers pathways to ensure trust and transparency in the use of data. Drawing on research in predictive modelling, consumer analytics, and adoption frameworks, this keynote envisions a future where AI-enabled ecosystems empower individuals, caregivers, and policymakers to build healthier and more resilient societies.