Keynote Speakers
Qingqing Wu
Associate Professor
Shanghai Jiao Tong University
Speech Title:Intelligent Reflecting Surface Empowered 6G Wireless Networks
Abstract: Deepfakes are artificially created media posing as actual video recordings and are a potential source of fake news or disinformation. Although research has been done in developing algorithms for the automatic detection of deepfakes, there needs to be more work conducted on how users identify deep fakes. This is a critical missing link because algorithms are currently not performing at a level where human judgement is unneeded. This presentation will discuss the verification strategies users adopt when engaging with content from deepfakes and the implications on businesses and societies.In this talk, we introduce a new wireless research paradigm by employing a massive number of low-cost passive reflecting elements with controllable phase, named intelligent reflecting surface (IRS), which is able to smartly change the wireless signal propagation to enable various functions such as beamforming and interference nulling/cancelation. We illustrate the main applications of IRS in achieving spectrum and energy efficient as well as secure and sustainable wireless networks in the future, and its advantages as compared to existing technologies such as massive MIMO and active relaying. We then present the signal and channel model of IRS by taking into account its hardware limitation in practice. Next, we focus on discussing the main design challenges in IRS-aided wireless networks, including joint active and passive beamforming optimization, channel estimation, etc., and highlight important directions for sensing, powering and computing.
Biography: Qingqing Wu is an Associate Professor with Shanghai Jiao Tong University. His current research interest includes intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV) communications, and MIMO transceiver design. He has coauthored more than 100 IEEE journal papers with 30+ ESI highly cited papers and 9 ESI hot papers, which have received more than 26,000 Google citations. He was listed as the Clarivate ESI Highly Cited Researcher in 2022 and 2021, the Most Influential Scholar Award in AI-2000 by Aminer in 2021 and World’s Top 2% Scientist by Stanford University in 2020 and 2021.
He was the recipient of the IEEE Communications Society Fred Ellersick Prize, IEEE Best Tutorial Paper Award in 2023, Asia-Pacific Best Young Researcher Award and Outstanding Paper Award in 2022, Young Author Best Paper Award in 2021, the Outstanding Ph.D. Thesis Award of China Institute of Communications in 2017, the IEEE ICCC Best Paper Award in 2021, and IEEE WCSP Best Paper Award in 2015. He was the Exemplary Editor of IEEE Communications Letters in 2019 and the Exemplary Reviewer of several IEEE journals. He serves as an Associate Editor for IEEE Transactions on Communications, IEEE Communications Letters, IEEE Wireless Communications Letters. He is the Lead Guest Editor for IEEE Journal on Selected Areas in Communications. He is the workshop co-chair for IEEE ICC 2019-2023 and IEEE GLOBECOM 2020. He serves as the Workshops and Symposia Officer of Reconfigurable Intelligent Surfaces Emerging Technology Initiative and Research Blog Officer of Aerial Communications Emerging Technology Initiative. He is the IEEE Communications Society Young Professional Chair in Asia Pacific Region.
Mohammad Reza Ghavidel Aghdam
Dr
Özyeğin University
Speech Title:Toward Enhanced Wireless Communications: Leveraging Reconfigurable Intelligent Surfaces (RIS) and End-to-End Machine Learning
Abstract: In the rapidly evolving landscape of wireless communication networks, the emergence of Reconfigurable Intelligent Surfaces (RIS) stands as a game-changer. These surfaces, empowered by intelligent algorithms, offer unprecedented control over electromagnetic wave propagation, promising to revolutionize communication efficiency and coverage. RIS introduces a paradigm shift in the way we conceive and implement wireless communication systems. By strategically deploying passive reflecting elements, RIS optimizes signal transmission, mitigates interference, and enhances overall network performance. However, to fully unlock the capabilities of RIS, seamless integration with machine learning is imperative.
End-to-end (E2E) machine learning algorithms provide a holistic approach to optimizing the performance of RIS-enabled communication networks. These algorithms leverage vast amounts of data to autonomously adapt RIS configurations, channel allocation, and beamforming strategies, thereby achieving optimal performance under dynamic and complex operating environments. Through E2E machine learning, RIS networks can continuously learn from real-world interactions, evolving to meet evolving communication demands.
In this presentation, we will explore the key challenges and opportunities in deploying RIS with E2E machine learning. We will discuss the intricacies of training machine learning models to effectively harness the capabilities of RIS, ensuring seamless integration with existing network infrastructures. Moreover, we will highlight promising applications of RIS-E2E machine learning in various domains, including 5G/6G networks, IoT connectivity, and smart city initiatives. Join us as we embark on a journey to unlock the full potential of RIS through E2E machine learning. Discover how this convergence of technologies is poised to redefine the future of wireless communication networks, ushering in an era of unprecedented connectivity, efficiency, and innovation.
Biography: Dr. Mohammad Reza Ghavidel Aghdam is a distinguished scholar and researcher specializing in Telecommunication Engineering and Wireless Communications. He earned his Bachelor of Science degree in Electrical Engineering from Urmia University in 2014, followed by a Master of Science in Communication Engineering and a Doctorate in Telecommunication Engineering from the University of Tabriz, Iran, in 2016 and 2020, respectively.
Beginning his academic journey as an Adjunct Assistant Professor at the Department of Electrical Engineering, Faculty of Engineering, Ilkhichi Branch, Islamic Azad University, Iran, in 2017, Dr. Ghavidel Aghdam swiftly established himself as a dedicated educator and researcher. His commitment to excellence and passion for advancing the field led him to Özyeğin University in Istanbul, Turkey, where he joined as a postdoctoral research fellow at the esteemed Communication Theory and Technologies (CT&T) Research Group in October 2022.
Dr. Ghavidel Aghdam's research interests span a wide array of cutting-edge topics in wireless communications, including but not limited to signal processing techniques, intelligent reflecting surfaces (IRSs) or reconfigurable intelligent surfaces (RISs), machine learning applications, massive multiple-input and multiple-output (mMIMO), millimeter wave (mmWave) communications, and non-orthogonal multiple access (NOMA). His contributions in these areas have significantly advanced the understanding and implementation of wireless communication systems, paving the way for enhanced performance, efficiency, and scalability in modern telecommunications networks.
Yipeng Zhou
Dr
Macquarie University
Speech Title:Enhancing Federated Learning by Sparsifying Transmitted Model Updates
Abstract: Federated learning facilitates the collaborative training of a machine learning model among geographically dispersed clients by exchanging model updates with a central server via Internet communication. However, transmitting these updates between the server and numerous decentralized clients over the Internet consumes considerable bandwidth and is susceptible to malicious attacks. This presentation showcases our various contributions aimed at improving communication efficiency and preserving privacy in federated learning. Our focus lies in sparsifying the transmission of model updates between the server and clients. By meticulously evaluating both the learning value, communication cost and privacy cost of transmitting each individual model update, we effectively mitigate the exposure of low-value updates to minimize communication and privacy costs. Extensive experiments conducted on real datasets demonstrate that our algorithms can significantly reduce communication costs and bolster privacy protection compared to the state-of-the-art federated learning baselines.
Biography: Dr Yipeng Zhou is a senior lecturer with the School of Computing, Faculty of Science and Engineering, Macquarie University. Before joining Macquarie University, he was a research fellow with the University of South Australia, and a lecturer with Shenzhen University, respectively. He got his Ph.D. and M.Phil degrees from The Chinese University of Hong Kong, and B.S. degree from University of Science and Technology of China, respectively. He received 2023 Macquarie University Vice-Chancellor's Research Excellence Award Highly Commended for Innovative Technology, and 2023 IEEE Open Journal of the Communications Society Best Editor Award. He was the recipient of 2018 Australia Research Council Discover Early Career Researcher Award (DECRA). His research interests lie in federated learning, data privacy-preservation, networking, etc. He has published 120+ papers in top venues, including IEEE INFOCOM, IJCAI, ICNP, IWQoS, IEEE ToN, JSAC, TPDS, TMC, TMM, etc.
Dmitry A. Zaitsev
Professor
The University of Derby
Speech Title:Clans for HPC: Composition of clans to speed-up solving sparse linear systems on parallel and distributed architecture
Abstract: Solving linear Diophantine systems of equations is applied in discrete-event systems, model checking, formal languages and automata, logic programming, cryptography, networking, signal processing, and chemistry. For modeling discrete systems with Petri nets, a solution in non-negative integer numbers is required, which represents an intractable problem. For this reason, solving such kinds of tasks with significant speedup is highly appreciated.
We introduce a nearness relation on a set of system’s equations, which transitive closure gives a clan relation. A sparse system is decomposed into a set of its clans. Solving a subsystem for each clan and then the clan composition system gives a speed-up of computations. We design a new solver of linear Diophantine systems, based on the simultaneous and parallel-sequential composition of the system clans, that runs on parallel architectures using a two level parallelization concept based on MPI and OpenMP. A decomposable system is usually represented by a sparse matrix; a minimal clan size of the decomposition restricts the granulation of the technique. A dynamic task-dispatching subsystem is developed for distributing systems on nodes in the process of compositional solution. Computational speedups are obtained on a series of test examples, e.g., illustrating that the best value constitutes up to 45 times speedup obtained on 5 nodes with 20 cores each. For load balancing, aggregation of the minimal clans has been implemented, that yields an additional speed-up. Solving sparse systems over fields of real numbers, using SVD decomposition to obtain basis solutions for clans, also reveals considerable speed-up on real-life tasks from the MatrixMarket repository.
Basic references:
Dmitry A. Zaitsev, Tatiana R. Shmeleva, Piotr Luszczek. Aggregation of clans to speed-up solving linear systems on parallel architectures, International Journal of Parallel, Emergent and Distributed Systems, 37(2), 2022, 198-219.http://dx.doi.org/10.1080/17445760.2021.2004412
Dmitry Zaitsev, Stanimire Tomov, Jack Dongarra. Solving Linear Diophantine Systems on Parallel Architectures, IEEE Transactions on Parallel and Distributed Systems, 30(5), 2019, 1158-1169. http://dx.doi.org/10.1109/TPDS.2018.2873354
Zaitsev D.A. Sequential composition of linear systems' clans, Information Sciences, 363, 2016, 292-307. http://dx.doi.org/10.1016/j.ins.2016.02.016
Biography: Dmitry A. Zaitsev (University of Derby, UK) developed the analysis of infinite Petri nets with regular structures, the decomposition of Petri nets into clans, the generalised neighbourhood for cellular automata, and the method of synthesizing fuzzy logic functions given by tables. He designed the Opera-Topaz software for manufacturing operative planning and control; a new stack of networking protocols, E6, and its implementation within the Linux kernel; and Petri net analysis software Deborah, Adriana, and ParAd. He developed models for TCP, BGP, IOTP protocols, Ethernet, IP, MPLS, PBB, and Bluetooth networks. His current research interests include Petri net theory and its applications in networking, computing, and automated manufacturing. Recently, he started working in the area of exascale computing, applying his theory of clans to speed up solving sparse linear systems on parallel and distributed architectures, as well as developing a novel paradigm of Sleptsov net computing. He has been a co-director of joint projects with China and Austria. Recently, he has been a visiting professor at the Technical University of Dortmund, Germany on a DAAD scholarship; the University of Tennessee, Knoxville, USA on a Fulbright scholarship; Eindhoven University of Technology, Netherlands; Johannes Kepler University, Linz, Austria; Cote D’Azur University, Nice, France; and the Technical University of Darmstadt, Germany. He has published a monograph, four book chapters, and more than a hundred papers, including those listed in JCR and CORE A. He is a senior member of ACM and IEEE.
Jiashen Teh
Associate Professor
Universiti Sains Malaysia (USM)
Speech Title:Dynamic Line Rating (DLR) for Enhanced Grid Reliability
Abstract: In this keynote speech, dynamic thermal line rating (DTLR) emerges as a transformative solution in modernizing power grid management. Highlighting its significance in enhancing grid resilience and efficiency, the speaker delves into the principles and applications of DTLR technology. Through real-world case studies and innovative approaches, attendees gain insights into how DTLR dynamically optimizes transmission line capacities based on environmental conditions, mitigating congestion risks and enabling higher utilization rates. Furthermore, the speech explores the integration of DTLR into smart grid frameworks, paving the way for a more adaptive and sustainable energy infrastructure.
Biography: Dr. Teh graduated with a PhD in Electrical Engineering from the University of Manchester, UK in 2016, and is currently an Associate Professor at Universiti Sains Malaysia (USM). He is also a Technical Director at UPE-Power in Taiwan. He mainly explores the benefits of using flexible transmission power line ratings to enhance grid reliability. To date, he has published more than 60 journal articles published in the SCIE database, with most of them ranked in the top two quarters of the ranking, garnering more than 2900 citations and 31 h-index on Google Scholar. He was the top 2% of the world's most-cited researchers by field in 2019, 2020, and 2021, according to Stanford University. In 2021 and 2022, he received the Outstanding Engineer Award by the IEEE Power & Energy Malaysia and, the IET Malaysia Outstanding Young Professional Award, respectively.