Keynote Speaker I

Prof. Trung Q. Duong

Queen's University Belfast, UK

IEEE Fellow and AAIA Fellow

Bio.: Dr. Trung Q. Duong (IEEE Fellow and AAIA Fellow) is a Canada Excellence Research Chair and Full Professor at Memorial University of Newfoundland, Canada. He is also a Research Chair of the Royal Academy of Engineering and a Chair Professor in Telecommunications at Queen’s University Belfast, UK.  His current research interests include quantum optimisation and machine learning in wireless communications. He has published 530+ books/bookchapters/papers with 20000 citations and h-index 75. He has served as an Editor for many reputable IEEE journals (IEEE Trans on Wireless Communications, IEEE Trans on Communications, IEEE Trans on Vehicular Technology, EEE Communications Surveys & Tutorials, IEEE Communications Letters, and IEEE Wireless Communications Letters) and has been awarded best paper awards in many flagship conferences including IEEE ICC 2014, IEEE GLOBECOM 2016, 2019, and 2022. He is the recipient of the Research Fellowship (2015-2020) and Research Chair (2020-2025) of the Royal Academy of Engineering. In 2017, he was awarded the Newton Prize from the UK government. He is a Fellow of IEEE and a Fellow of Asia-Pacific Artificial Intelligence Association (AAIA).

Speech Title: Quantum Machine Learning for Revolutionizing 6G Computing-Intensive Communication and Networking: A Case Study of Channel Estimation 

Abstract: Future wireless networks beyond the fifth generation (5G) have been envisioned to increase many folds, in terms of key performance metrics such as latency, data rate, reliability, mobility, and user’s quality of experience. However, one of the biggest difference between the sixth generation (6G) and its predecessor is the ability to support high speed connectivity of mobile services with computing-intensive applications. Quantum inspired machine learning (QML) and optimization has been considered as an efficient tool to realise the potential of 6G communications. Despite this premise, the research in QML is still in its infancy with many open problems. This talk discusses an overview of quantum machine learning for optimal resource allocation for 6G ISTNs, with respect to the case study of using QML for channel estimation. The talk not only provides fundamental requirements, but also enabling technologies, visions, and future challenges of this emerging technology.

Keynote Speaker  II

Prof. Phil Mawby

University of Warwick, UK

IET Fellow

Bio.: Professor Mawby joined the University of Warwick having spent 19 years at the University of Wales, Swansea. He has built an international reputation in the area of power electronics and power device research.

His main interests are materials for new power devices, modelling of power devices and circuits, power integrated circuits. He has also worked extensively on development of device simulation algorithms, as well as optoelectronic and quantum based device structures.

Professor Mawby graduated from the University of Leeds, and obtained his PhD from the same institution in 1986, where he studied GaAs/AlGaAs Heterojunction bipolar transistors for high power radio frequency applications in conjunction co-workers at the GEC Hirst Research Centre in Wembley.

Whilst in Swansea Professor Mawby established the Power Electronics Design Centre, which carried work out in a whole range of areas relating to power electronics. The centre focussed on interaction with SME's in Wales as well as larger international companies. Whilst he was in Swansea he held the Royal Academy of Engineering Chair for Pwer Electronics.

Professor Mawby is on many international conference committees including, ISPSD, EPE, BCTM and ESSDERC. He is Chartered Engineer, a Fellow of the IET, and a Fellow of the Institute Physics as well as a Senior Member of the IEEE. He has published over 70 Journal papers and 100 conference papers, and is a distinguished lecturer for the IEEE Electron devices society.

Keynote Speaker  III

Prof. Jun Wang

Hunan University, China

Bio.: Jun Wang (Senior Member, IEEE) received the B.S. degree from the Huazhong University of Science and Technology, Wuhan, China, in 2000, the M.S. degree from the Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China, in 2003, the M.E. degree from the University of South Carolina, Columbia, SC, USA, in 2005, and the Ph.D. degree from North Carolina State University, Raleigh, NC, USA, in 2010, respectively, all in electrical engineering.,From 2010 to 2013, he was a Device Design Engineer with Texas Instruments, Inc., Bethlehem, PA, USA. In 2014, he was a Professor with the College of Electrical and Information Engineering, Hunan University, Changsha, China. His research interests include power semiconductor devices and their applications in power electronics systems.,Dr. Wang has been an Associate Editor for the IEEE Journal of Emerging and Selected Topics in Power Electronics, since 2017.

Keynote Speaker  Ⅳ

Prof. Huai Wang

Aalborg University, Denmark

Bio.: Huai Wang is currently a Professor at the Department of Energy, Aalborg University, Denmark, where he leads the Reliability of Power Electronic Converters (ReliaPEC) group. He is also the Head of Mission on Digital Transformation and AI, with 13 affiliated research groups, to enable the next leap in transforming energy systems by bridging the multi-disciplinary research and innovation in energy, digitalization, and AI. His research addresses the fundamental challenges and application issues in efficient, reliable, and cognitive power electronic converters functioning as energy processors for our electrified and digitalized world. He collaborates widely with industry companies across the value chain, from power electronic materials and components to systems. He has contributed a few original concepts and methods to power electronics reliability and passive components and received six paper awards from IEEE and IET. In addition, he has given more than 100 invited talks in universities, companies, and conferences. He is the co-founder of Nordic Passive ApS.

Dr. Wang received his Ph.D. degree from the City University of Hong Kong in 2012 and a B. E. degree from the Huazhong University of Science and Technology in 2007. He was a short-term visiting scientist with the Massachusetts Institute of Technology (MIT) in 2013 and ETH Zurich in 2014. He was with the ABB Corporate Research Center, Switzerland, in 2009. He received the Richard M. Bass Outstanding Young Power Electronics Engineer Award from the IEEE Power Electronics Society in 2016 for his contribution to the reliability of power electronic converter systems. He was as the Chair of IEEE IAS/IES/PELS Chapter in Denmark during 2018-2020 and serves the editorial board of four journals from IEEE, Springer Nature, and Elsevier.