Keynote Speaker I
Prof. Fei Liu
La Trobe University
Biography:
Dr. Fei Liu received her PhD degree from the Department of Computer Science & Computer Engineering, La Trobe University in 1998. Before joining the department as a senior lecturer in 2002, she was a lecturer in the School of Computer Science & Information Technology, RMIT University (1999 - 2002) and the School of Computer & Information Science, University of South Australia (1996 – 1997). She also worked as a software engineer in Ericsson Australia. During which she participated the design and development of the earliest Short Messaging System (SMS) for Telstra Australia.
Fei’s research interests are in Artificial Intelligence including Sentiment Analysis, Text Summarization, Recommendation Systems, Semantic Web and Logic Programming. She has supervised and co-supervised 11 PhD students to successful completion. She was the supervisor of over 40 Honours and Master by Course Work students to successfully complete their minor thesis. Fei has been active in research service on various roles including member of editorial advisory board, journal guest editors, member of conference advisory committees and program committees. She has authored and co-authored over 80 conference and journal papers.
Speech Title: The Knowledge Graph that Enables Human and Machine Interactions as a Translation Dictionary
Abstract: In this talk, we present a framework for constructing a knowledge graph which can act as a human_x0002_machine translation dictionary to enable human-machine interactions. Given a concept/word, a computer can extract the definition of the concept from the knowledge graph, and consequently “understand” its meaning. In the knowledge graph, a node represents a concept. The concept is defined through its relations with other concepts. Relations are specified in the machine language. To a certain degree, the proposed knowledge graph has a resemblance to WordNet, which consists of a set of concepts/words with synonyms being linked to form the net. WordNet plays an important role in text mining, such as sentiment analysis, document classification, text summarization and question answering systems etc. However, merely providing synonyms is not sufficient. The proposed dictionary provides a definition for each concept. Based on the definition, a computer can accurately estimate the distance and similarity between two concepts. As a monotonic mapping, the algorithm for estimating distances and similarities isproved to be always convergent. We envisage that the dictionary will become an important tool in all Text Mining disciplines.
Keynote Speaker II
Prof. Yuanwei Liu
Queen Mary University of London
Biography:
Yuanwei Liu is an Associate Professor with the School of Electronic Engineering and Computer Science, Queen Mary University of London. His research interests are NOMA, RIS/STAR, Integrated sensing and communications, and machine learning. Yuanwei Liu is an IEEE Communication Society Distinguished Lecturer and IEEE Vehicular Technology Society Distinguished Lecturer. He received several career awards, including Web of Science Highly Cited Researcher 2021 and 2022, the 2020 IEEE ComSoc Outstanding Young Researcher Award for EMEA, the 2020 Early Achievement Award of the IEEE ComSoc Signal Processing and Computing for Communications (SPCC) Technical Committee, the 2020 Early Achievement Award of IEEE Communication Theory Technical Committee, the 2021 IEEE ComSoc Best Young Professional Outstanding Nominee, 2022 International Union of Radio Science Young Scientist Award. He received several best paper awards, he is the co-recipient of IEEE SPCC Best Paper Award, the Best Paper Award in ISWCS 2022 and the Best Student Paper Award in IEEE VTC2022-Fall. Yuanwei Liu received several research recognization, including listing 35 Innovators Under 35 China by MIT Technology Review in 2022, listing among the World's Top 2% Scientists by Stanford University from 2020 to 2022, the 2023 - Research.com Electronics and Electrical Engineering in United Kingdom Leader Award, AI 2000 Most Influential Scholar Honorable Mention in Internet of Things in both 2022 and 2023.
Yuanwei Liu is the academic Chair for the Next Generation Multiple Access Emerging Technology Initiative, the vice chair of the IEEE Technical Committee on Cognitive Networks (TCCN). He serves as a Senior Editor of IEEE Communications Letters, an Editor of the IEEE Transactions on Wireless Communications, the IEEE Transactions on Communications, and the IEEE Transactions on Network Science and Engineering. He serves as the Guest Editor for Proceeding of the IEEE, IEEE JSAC, IEEE JSTSP and IEEE Network. He has served as the Publicity Co-Chair for VTC 2019-Fall, Symposium Co-Chair for Cognitive Radio & AI-Enabled Networks for IEEE GLOBECOM 2022 and Communication Theory for IEEE GLOBECOM 2023. He serves as the chair of Special Interest Group (SIG) in SPCC Technical Committee on signal processing Techniques for next generation multiple access, the vice-chair of SIG WTC on Reconfigurable Intelligent Surfaces for Smart Radio Environments.
Speech Title: Simultaneously Transmitting And Reflecting Surface (STARS) for 360° Coverage
Abstract: In this talk, the novel concept of simultaneously transmitting and reflecting surfaces (STARS) will be introduced. First, the STAR basics will be introduced. In particular, the fundamental signal modelling, performance limit characterization, practical operating protocols, and joint beamforming design will be discussed. Then, the coupled phase-shift STAR model, AI enabled STARS, and channel estimation methods will be focused. Furthermore, several promising case studies of employing STARS will be put forward, including STAR-NOMA, spatial analysis for STARS, federated learning with STARS, and STARS enabled integrated sensing and communications (ISAC). Finally, research opportunities and problem as well as commercial progress of STARS.