Keynote Speaker I
Prof. Zuqing Zhu
IEEE Fellow
The University of Science and Technology of China (USTC), China
Keynote Lecture: Accelerating Collective Communications with Mutual Benefits of Optical Rackless DC and In-Network Computing
Abstract: We propose a novel data center (DC) architecture to explore the mutual benefits of optical rackless data center (ORDC) and in-network computing for accelerating collective communications for emerging applications such as MapReduce clasters and Large Language Model training. Our experimental results indicate that the proposal reduces job completion time of collective communications by 27:4% to 43.3% over traditional benchmarks.
Biography: Zuqing Zhu received his Ph.D. degree from the Department of Electrical and Computer Engineering, University of California, Davis, in 2007. From 2007 to 2011, he worked in the Service Provider Technology Group of Cisco Systems, San Jose, California, as a Senior Engineer. In January 2011, he joined the University of Science and Technology of China, where he currently is a Full Professor in the School of Information Science and Technology. He has published 360+ papers in peer-reviewed journals and conferences. He is the Steering Committee Chair of the IEEE International Conference on High Performance Switching and Routing (HPSR), and was the Chair of the Technical Committee on Optical Networking (ONTC) in IEEE Communications Society. He has received the Best Paper Awards from ICC 2013, GLOBECOM 2013, ICNC 2014, ICC 2015, and ONDM 2018. He is a Fellow of IEEE. <Learn More>
Keynote Speaker II
Prof. Yifan Chen
University of Electronic Science and Technology of China, China
Keynote Lecture: Microwave Medical Imaging and Sensing for Disease Dagnosis and Health Monitoring
Abstract: Microwave medical imaging and sensing (MMIS) operating over the frequency range covering hundreds of megahertz to tens of gigahertz has the potential to provide proactive healthcare solutions to patients with acute (for early diagnosis) or chronic (for daily monitoring) medical conditions. This technology exploits the tissue dielectric properties for disease diagnosis by using quantitative or qualitative algorithms. The conventional medical imaging systems such as computerized tomography (CT), magnetic resonance imaging (MRI), magnetic resonance electrical properties tomography (MR-EPT), and ultrasound face different disadvantages including ionized radiation, difficulty in interpretation, high cost, and poor spatial resolution. MMIS offers an alternative solution to monitor in vivo abnormalities in a nonionizing, affordable, and lightweight manner, which facilitates the diagnosis of patients in homes, nursing homes, and ambulances. The advantages of MMIS include low health risk, low operational cost, lightweight implementation, and ease of use, given its perspective of miniaturization and integration into portable and handheld devices with networking capability. In this talk, we will discuss our works on MMIS for cancer detection, stroke detection, heart imaging, bone imaging, tracking of in-body drug-loaded nanorobots, etc.
Biography: Dr Yifan Chen is a Distinguished Professor in the School of Life Science and Technology, University of Electronic Science and Technology of China. He received the B.Eng. (Hons. I) and Ph.D. degrees in electrical and electronic engineering from Nanyang Technological University, Singapore. He has held various academic and leadership positions in top-tier universities in China, New Zealand, UK, and Singapore across multiple disciplines such as electrical and electronic engineering, biomedical engineering, and computer science and engineering. He is a Fellow of Engineering New Zealand, a Fellow of The Institution of Engineering and Technology, UK, and a Fellow of European Alliance for Innovation. …… <personal webpage>
Keynote Speaker III
Prof. Liang Zhou
IEEE Fellow
Nanjing University of Posts and Telecommunications, China
Keynote Lecture: Cross-Modal Communications: Theories, Technologies, and Applications
Abstract: Driven by the rapid evolution of multimedia and wireless technologies—social media, ultra-high-definition mobile video, 5G, and beyond—users’ audiovisual appetites are increasingly satisfied, shifting attention toward richer, higher-order sensory engagement. Research shows that integrating additional senses, such as haptics, into traditional audiovisual services can deepen immersion. This demand has catalyzed cross-modal communication as the indispensable enabler of truly multimodal experiences. Yet the path to high-quality cross-modal delivery is steep, because audio, video, and haptic signals differ drastically in transmission requirements and processing needs. Three key challenges stand out: (1) how to characterize signals from multiple sensory channels quickly and accurately; (2) how to reconcile the conflicting, modality-specific demands within a single bitstream; and (3) how to reconstruct cross-modal information so that the immersive experience remains coherent. This talk will present our team’s latest solutions to these problems and outline our roadmap for future work.
Biography: ZHOU Liang is a professor at Nanjing University of Posts and Telecommunications. His research primarily focuses on multimedia communications and intelligent communications. In recent years, he has published numerous academic papers in journals like IEEE JSAC. He has led key projects funded by the National Natural Science Foundation of China, as well as industry-academia collaboration projects with companies like Huawei and ZTE. Currently, he serves as the Chair of the Multimedia Communications Technical Committee of the IEEE Communications Society, Director of the Professional Committee of the China Institute of Communications, and is an editorial board member for academic journals such as IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, IEEE Internet of Things Journal, IEEE Wireless Communications, and IEEE Network. He is an IEEE Fellow/IET Fellow/BCS Fellow. <personal webpage>
Keynote Speaker IV
Prof. Peter Chong
Auckland University of Technology, New Zealand
Keynote Lecture: Fall Prediction in Elderly through Vital Signs Monitoring – A Fuzzy-Based Approach
Abstract: Senior individuals are among the most frequent users of healthcare services, accounting for approximately 12% of the public sector, primary, and hospital care. Most elderly people who live alone and are not monitored are at risk of losing their lives because of a sudden fall caused by a slip, trip, or health issue and are not reported to an emergency department in time to receive immediate treatment. Thus, reliable, and cost-effective e-health technologies are essential for solitary older adults.
In this talk, our research aims to predict potential falls in elderly individuals by detecting anomalies through continuous monitoring. The proposed prediction technique with the Fall Prediction Algorithm learns and performs tasks using Fuzzy rules. The acquired results are categorized based on the prediction risk levels. The proposed fall risk prediction model is evaluated using data collected from three different sources and the findings are compared to the Morse Falls Scale. According to the results obtained, the suggested prediction model has a total accuracy of 95.24%, sensitivity of 93.75%, and specificity of 100%. With these advancements in the proposed heterogeneous technology, elderly falls can be predicted earlier to save their lives.
Biography: Professor Peter Han Joo Chong is the Associate Head of School (Research) at the School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, New Zealand. Between 2016 and 2021, he was the Head of Department of Electrical and Electronic Engineering at AUT. He received the Ph.D. degree in Electrical and Computer Engineering from the University of British Columbia, Canada in 2000. He is currently an Adjunct Professor at the Department of Information Engineering, CUHK. He is an Honorary Professor at Amity University, India. He is a Fellow of the Institution of Engineering and Technology (FIET), UK. Prof. Chong is listed in the World's Top 2% Scientists published by Stanford University in 2022. Prof. Chong received the Research Excellence Award from the Faculty of Design and Creative Technologies in 2023. Before joining AUT in 2016, Professor Chong was an Associate Professor (tenured) at the School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore. Between 2013 and 2016, he was the Director of Infinitus, Centre for Infocomm Technology. From 2001 to 2002, he was with the Radio Communications Laboratory at Nokia Research Center, Finland. Between 2000 and 2001, he worked in the Advanced Networks Division at Agilent Technologies Canada Inc., Canada. He co-founded P2 Wireless Technology in Hong Kong in 2009 and Zyetric Technologies in Hong Kong, New Zealand and US in 2017.
His current research projects focus on machine learning techniques applied to vehicular networks. He has been developing techniques of deep reinforcement learning (DRL)-based resource management for 5G-V2X networks. His research interests are in the areas of wireless/mobile communications systems including radio resource management, multiple access, MANETs/VANETs, green radio networks and 5G-V2X networks. He has published over 300 journal and conference papers, 3 edited books and 13 book chapters in the relevant areas.
Keynote Speaker V
Prof. Xiao Han
Sichuan University, China
Keynote Lecture: AI in Pathology: From Self-Supervised Learning to Foundation Models
Abstract: “Artificial intelligence in pathology is a cornerstone for achieving precision medicine, holding significant promise for improving the efficiency and accuracy of pathological diagnosis, predicting disease prognosis, and developing personalized treatment plans. This presentation will systematically review our team's recent progress and achievements in the field of AI in pathology, covering a spectrum of research from self-supervised and weakly-supervised learning methods to the development and clinical application of pathology foundation models. The talk will begin by introducing our explorations in self-supervised learning, including Transformer-based feature extraction and clustering-guided contrastive learning. It will then elaborate on the application of weakly-supervised, multi-instance learning techniques for automated cancer diagnosis and grading. Finally, the presentation will focus on our recent breakthroughs in pathology foundation models and explore their potential for generalized cancer diagnosis, prognostic prediction, and clinical decision support.”
Biography: Professor Xiao Han has long been devoted to advancements in machine learning technology within the medical field, including neuroscience, radiation therapy and computational pathology. Prior to joining Sichuan University, he served as a principal scientist at Tencent AI Lab and deputy general manager of Tencent AI Medical Center, where he led a team of over 30 researchers in medical AI research and product development. Among his key achievements, his team developed the Tencent AIMIS Digital Pathology Platform, which obtained EU CE certification in 2022 and won the Advanced Technology Achievement Award at the 2022 China International Big Data Industry Expo. In collaboration with Mindray Medical, his team developed China’s first AI-assisted peripheral blood cell analysis product – the first AI product in the in vitro diagnostic industry that was granted the “Green Pathway” for innovative medical devices by China NMPA. Professor Han has published over 100 papers in top international journals and leading conferences, with a total of over 12,000 citations (per Google Scholar). For the past five years, he has been consistently included in Stanford University’s list of the world’s top 2% scientists in both the "Career Scientific Impact" and "Annual Scientific Impact" categories. Professor Han holds over 40 granted patents in China and over 30 in the USA. He was awarded the China National Excellence Patent Award in 2022, and two Gold Prizes at Tencent’s Annual Patent Awards in 2022 and 2023 respectively.
Keynote Speaker VI
Prof. Lunchakorn Wuttisittikulkij
Chulalongkorn University, Thailand
Keynote Lecture: The Metaverse Revolution: Transforming Interaction in the Digital Age
Abstract: The evolution of advanced digital technologies is fundamentally reshaping the way we interact, learn, and collaborate. As immersive technologies progress, the metaverse has transitioned from a futuristic vision to a dynamic digital ecosystem with far-reaching implications across academia, industry, and society. By integrating extended reality (XR), artificial intelligence (AI), and advanced signal processing, it creates seamless and intuitive digital interactions. Moving beyond traditional video conferencing and social media, the metaverse enables immersive and interactive communication through avatars, spatial computing, and real-time data integration. The metaverse enhances accessibility, engagement, and innovation in the digital era by incorporating digital twins, AI-powered simulations, and virtual collaborative spaces. This keynote will explore the latest advancements in metaverse technologies, emphasizing their transformative applications in education, research, and cross-border collaboration. A key focus will be MANGOs: Metaverse for Academic Nexus Global Opportunities, a pioneering initiative designed to revolutionize academic collaboration by creating immersive, interactive, and globally connected virtual environments. MANGOs enhance experiential learning, remote teamwork, and knowledge dissemination, paving the way for a more inclusive and dynamic digital education ecosystem in the metaverse. As the metaverse continues to evolve, its role in shaping the future of communication, education, and global collaboration will be pivotal, driving new possibilities for immersive and interconnected digital experiences.
Biography: Lunchakorn Wuttisittikulkij is a Professor in the Department of Electrical Engineering at Chulalongkorn University, Bangkok, Thailand. He received his B.Eng. degree in electrical engineering from Chulalongkorn University in 1990. Pursuing advanced studies abroad, he obtained his M.Sc. and Ph.D. degrees in telecommunications from the University of Essex, U.K., in 1992 and 1997, respectively. During his doctoral studies, he participated in a European Commission-funded project on multiwavelength transport networks (MWTN), where he played a key role in network design and dimensioning. Upon completing his Ph.D., Prof. Lunchakorn joined the Faculty of Engineering at Chulalongkorn University in 1997. Over the years, he has made substantial contributions to both academia and industry, authoring or co-authoring 15 books and publishing over 150 research articles in esteemed journals and conferences worldwide. He has served as General Chair of the 33rd International Technical Conference on Circuits/Systems, Computers and Communications. His research interests span several domains, including wireless communication and networks, the metaverse, and immersive technologies such as extended reality (XR). <personal webpage>
Invites Speaker I
Asst. Prof. Junhan Zhao
University of Chicago, USA
Invited Lecture: AI-driven Phenomics Analysis for Cancer Diagnostics, Prognostics and Therapeutics
Abstract: AI-driven phenomics analysis has revolutionized the field of cancer diagnostics, prognostics, and therapeutics by leveraging advanced algorithms to analyze cellular images such as H&E histology images. In this talk, I will discuss my work to incorperate medical domain knowledge for developing clinical AI, including weakly-supervised, self-supervised learning and foundation model. We will conclude with a discussion on enhancing cancer care through more efficient and effective AI.
Biography: Junhan Zhao is an Assistant Professor in the Department of Pediatrics at the University of Chicago and a visiting scientist at Harvard Medical School and Massachusetts General Hospital. He completed his postdoctoral fellowship at Harvard Medical School, where he specialized in AI-driven digital health. Zhao earned his Ph.D. in computer graphics as a Bilsland Fellowship awardee and served as a lecturer at Purdue University. He completed his engineering training and graduated with distinction from Shanghai Jiao Tong University and Cornell University. He also holds a M.Sc in Biostatistics from Harvard University. Zhao has served as first or senior author on high-impact publications featured in Nature, Nature Comm, Light Science & Applications, Med, and IEEE portfolio (TVCG, TIP, TMI, TIM, TCSVT, JBHI). He led AI development and deployment in early-stage ventures related to AI, wearable devices, stem cell therapy and rejuvenation.
Invites Speaker II
Dr. Jin Y. Du
Guangdong CAS Angels Biotechnology Co., Ltd.
Invited Lecture: TBD
Abstract: TBD
Biography: Dr. Du received his Ph.D. from the University of Illinois at Chicago in 2018, under the supervision of Professor Dima Sinapova. His doctoral research was on utilizing forcing techniques to prove the consistency of infinitary combinatorial principles assuming large cardinals. He then pursued postdoctoral research at The Chinese University of Hong Kong under the mentorship of Professor Xiaodan Fan to develop a Hidden Markov Model for which genes may influence a phenotype. Since 2022, he has collaborated with Guangdong Provincial Hospital of Traditional Chinese Medicine on a project to develop an artificial intelligence-based medicine recommendation system for nasopharyngeal cancer patients.
Dr. Du’s core research involves developing AI-powered dental lesion detection systems based on large-scale language models and reconstructing precise 3D dental implant models from CBCT scans using advanced segmentation algorithms. He has published papers in various fields. His work has been recognized with funding, awards or recognition from the Association for Symbolic Logic, the UCLA Department of Mathematics and Guangdong CAS Angels Biotechnology Co., Ltd., earning him high acclaim.
Since 2019, Dr. Du has focused on applying artificial intelligence technology to medical and health monitoring and treatment. His research has been published in top-tier journals such as Molecular Therapy - Nucleic Acids and conferences such as IMIP2022.
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