Keynote Speakers

Prof. Zhu Han, University of Houston, USA
(AAAS Fellow, IEEE fellow, John and Rebecca Moores Professor)

Short bio: Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. From 2000 to 2002, he was an R&D Engineer of JDSU, Germantown, Maryland. From 2003 to 2006, he was a Research Associate at the University of Maryland. From 2006 to 2008, he was an assistant professor at Boise State University, Idaho. Currently, he is a John and Rebecca Moores Professor in the Electrical and Computer Engineering Department as well as the Computer Science Department at the University of Houston, Texas. Dr. Han is an NSF CAREER award recipient of 2010, and the winner of the 2021 IEEE Kiyo Tomiyasu Award. He has been an IEEE fellow since 2014, an AAAS fellow since 2020, an IEEE Distinguished Lecturer from 2015 to 2018, and an ACM Distinguished Speaker from 2022-2025. Dr. Han is also a 1% highly cited researcher since 2017.

Speech Title: Federated Learning and Analysis with Multi-access Edge Computing
Abstract: With the maturity of edge computing and the large amount of data generated by IoT devices, we have witnessed an increasing number of intelligent applications in wireless networks. The growing awareness of privacy further motivates the wide study and deployment of federated learning, a collaborative distributed model training framework for predictive tasks. However, a wide range of applications, more broadly relevant to data analytics and query in wireless networks, cannot be well supported by this framework. These applications usually require more complex and diverse aggregation methods, instead of the simple weight aggregations, and are broadly nourished by statistics, information theory, and signal processing, besides machine learning. This talk aims to present the recent advances in federated analytics at the intersection of data science, wireless communication, and security and privacy. We will present the definition, taxonomy, and architecture of the federated analytics techniques. It will also cover several practical and important data analytics tasks in wireless networks, including federated anomaly detection, federated frequent pattern analysis, federated distribution estimation and skewness analytics. Finally, we will present important challenges, open problems, and future directions at the intersection of federated learning/analysis and wireless networks.

Prof. Robert Minasian, The University of Sydney, Australia 

(IEEE Life Fellow, OSA Fellow, Fellow of The Royal Society of NSW)

Short bio: Robert A. Minasian received the B.E. degree from the University of Melbourne, Melbourne, Australia, the M.Sc. degree from University College London, U.K., and the Ph.D. degree from the University of Melbourne. He is a Chair Professor with the School of Electrical and Information Engineering at the University of Sydney, Australia. He is also the Founding Director of the Fibre-optics and Photonics Laboratory. His research has made key contributions to microwave photonics and photonic signal processing. He is recognized as an author of one of the top 1% most highly cited papers in his field worldwide. Professor Minasian has contributed over 400 research publications, including Invited Papers in the IEEE Transactions and OSA (now Optica) Journals. He has 84 Plenary, Keynote and Invited Talks at international conferences. He is an Advisory Editor of Optical Fiber Technology. He has served on numerous program, technical and steering committees of international conferences. He has also served on the Australian Research Council and on the Research Evaluation Committee for the Excellence in Research for Australia initiative. Professor Minasian was the recipient of the ATERB Medal for Outstanding Investigator in Telecommunications awarded by the Australian Telecommunications and Electronics Research Board. He is a Life Fellow of the IEEE, a Fellow of the Optical Society of America (now Optica), and a Fellow of The Royal Society of NSW.

Speech Title: Advances in Integrated Photonic Signal Processing and Sensing
Abstract: Integrated photonic signal processing offers new powerful paradigms for signal processing and sensing systems. This stems from its inherent advantages of wide bandwidth and immunity to electromagnetic interference. Current trends are focused on integrating photonics onto silicon platforms to leverage the highly developed CMOS fabrication technologies and to enable boosting the performance of future systems performing signal processing and deep learning, with the potential for implementing high bandwidth, fast and complex functionalities. Recent advances in silicon photonics integrated signal processing and sensing are presented. These include techniques for LIDAR on-a-chip systems and neural network assisted control for beamsteering, photonic approaches to artificial neural networks for deep learning, programmable integrated photonic processors, and high-resolution integrated sensors using optical microresonators that strongly enhance the light-matter interaction to attain high sensitivity and which utilize deep learning techniques to enhance the photonic sensor interference resilience performance. These photonic processors open new capabilities for the realisation of high-performance signal processing and sensing.

Prof. Aaron H.P. Ho, The Chinese University of Hong Kong

(SPIE Fellow, HKIE Fellow, Professor, Biomedical Engineering)

Short bio: Professor Ho received his BEng and PhD in Electrical and Electronic Engineering from the University of Nottingham. Currently serving the Department of Biomedical Engineering, The Chinese University of Hong Kong (CUHK), as the department chairman and a professor, he has been with the Department of Electronic Engineering and held positions as Associate Dean of Engineering, CUHK; Assistant Professor in Department of Physics and Materials Science, City University of Hong Kong; Senior Process Engineer for semiconductor laser fabrication in Hewlett-Packard. His service in the professional and academic community includes Chairman of Hong Kong Optical Engineering Society; Chairman of IEEE Electron Device/Solid-State Circuits (ED/SSC) Hong Kong Chapter, Admission Panel member of Technology Business Incubation Programme (IncuTech) operated by Hong Kong Science and Technology Parks Corporation (HKSTP); Council Member of The Technological and Higher Education Institute of Hong Kong (THEi). His current academic interests focus on nano-sized semiconductor materials for photonic and sensor applications, optical instrumentation, surface plasmon resonance biosensors, lab-on-a-chip and biophotonics. He has published over 400 peer-reviewed articles, 33 Chinese and 6 US patents. He is a Fellow of SPIE and HKIE.

Speech Title: Bio-detection based on Centrifugal Force-Actuated Microfluidics
Abstract: Typical microfluidic devices are based on the linear-flow strategy. Actuation of fluidic flow relies entirely on the use of pumps. We have explored the possibility of using inertial forces by spinning the fluidic device platform. This so-called centrifugal microfluidics approach has a unique advantage of offering sample actuation force anywhere within the rotating device platform, hence making it particularly suitable for conducting multiple bio-detection events simultaneously. Our effort focuses on the development of lab-on-a-disc devices for detecting target biomolecules. Sample-to-answer detection of several clinically relevant bacteria and pathogens, thus making the technique highly suited for point-of-care applications, have been demonstrated. We also report our recent effort in the development of a lab-in-a-centrifuge platform for conducting real-time monitoring of centrifugation and inertial force-actuated analysis of single molecules using a inverted pyramidal silicon nanopore.