Distinguished Seminar Series in Computer Networks and Communications

• Host: Prof. Hongbo Jiang. Co-Host: Prof. Geyong Min (University of Exeter, UK). Time: November 25, 2024. 17:00PM. Venue (Tencent Meeting): 773-852-223
  Speaker: Professor Schahram Dustdar,  Technischen Universität Wien (TU Wien, Austria).
  Title: On Active Inference for Distributed Intelligence in the Computing Continuum
  Abstract: Modern distributed systems also deal with uncertain scenarios, where environments, infrastructures, and applications are widely diverse. In the scope of IoT-Edge-Fog-Cloud computing, leveraging these neuroscience-inspired principles and mechanisms could aid in building more flexible solutions able to generalize over different environments. A captivating set of hypotheses from the field of neuroscience suggests that human and animal brain mechanisms result from a few powerful principles. If proved to be accurate, these assumptions could open a deep understanding of the way humans and animals manage to cope with the unpredictability of events and imagination.
  Bio: Schahram Dustdar is a Full Professor of Computer Science at TU Wien, where he leads the Research Division of Distributed Systems in Austria. He also holds a part-time position as an ICREA Professor at UPF, Barcelona. Additionally, Professor Dustdar maintains several honorary appointments, including at the University of California, Los Angeles (USC), Monash University in Melbourne, Shanghai University, Macquarie University in Sydney, and Pompeu Fabra University in Barcelona, Spain. Between December 2016 and January 2017, he served as a Visiting Professor at the University of Sevilla, Spain, and from January to June 2017, he was a Visiting Professor at UC Berkeley, USA. From 1999 to 2007, he co-founded and served as Chief Scientist of Caramba Labs Software AG, a venture-capital-backed software company in Vienna specializing in team collaboration software (acquired by ProjectNetWorld AG). He is also the co-founder of CooVally.com in Barcelona and the co-founder and Chief Scientist of Sinoaus.net, an R&D organization based in Nanjing, China, focused on IoT and Edge Intelligence. Professor Dustdar serves as Editor-in-Chief of *Computing* (Springer). His numerous accolades include the IEEE TCSVC Outstanding Leadership Award (2018), the IEEE TCSC Award for Excellence in Scalable Computing (2019), ACM Distinguished Scientist (2009), ACM Distinguished Speaker (2021), and the IBM Faculty Award (2012). He is an elected member of the Academia Europaea, an IEEE Fellow (2016), and a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) (2021), where he also served as President from 2020 to 2021. He is the president of the International AI Industry Alliance (AIIA) since 2023.

• Host: Prof. Hongbo Jiang. Time: October 17, 2024. 15:00PM. Venue (Tencent Meeting): 485-284-620
  Speaker: Professor Yan Zhang,  University of Oslo.
  Title: Ubiquitous Computing Power Networks
  Abstract: Firstly, we introduce the concept and model of ubiquitous computing power network. Then, new and unique scientific research problems in ubiquitous computing power networks are defined and solved, including the optimal allocation of computing resources, computing power collaboration and clustering mechanism, and distributed computing power sharing. Finally, we point out the future scenarios and open questions of ubiquitous computing power networks.
  Bio: Yan Zhang is currently a Full Professor with the Department of Informatics, University of Oslo, Norway. His research interests include next-generation wireless networks leading to 6G, green and secure cyber-physical systems. Dr. Zhang is an Editor for several IEEE transactions/magazine. Since 2018, Prof. Zhang has been listed as a Highly Cited Researcher by Clarivate Analytics (i.e., Web of Science). He is Fellow of IEEE, Fellow of IET, elected member of Academia Europaea (MAE), elected member of the Royal Norwegian Society of Sciences and Letters (DKNVS), and elected member of Norwegian Academy of Technological Sciences (NTVA).

• Host: Prof. Hongbo Jiang. Time: July 8, 2024. 10:30AM. Venue: College of CSEE Building, Room 624
  Speaker: Professor Qian Zhang,  Hong Kong University of Science and Technology.
  Title: Pushing the Limit of Mobile Sensing: Smart Healthcare in the Age of AloT
  Abstract: Sensing is an effective means to connect the physical world and digital space. Exploring the intelligent sensing capability has drawn researcher's great attention. lt is quite excited to see in recent years, besides wearable sensing, people have also begun to explore the acoustic, wireless signal, light, and other ambient communication medium's capability for sensing purpose. In this talk, l will introduce some of our work related to how to leverage the wearable and the communication medium's sensing capability to enable smart healthcare applications, especially focus on home care scenario.
  Bio: Dr. Qian Zhang joined the Hong Kong University of Science and Technology (HKUST) in September 2005. She is currently the Acting Head of the Division of lntegrative Systems and Design, Tencent Professor of Engineering and Chair Professor in the Department of Computer Science and Engineering. She also serves as the Director othe Microsoft Research Asia-HKUST Joint Lab. Co-Director of the Huawei-HKUST innovation Lab. and Director of the HKUST Digital Life Research Center. Prior to this, she worked at Microsoft Research Asia starting in July 1999where she served as the Research Manager of the Wireless and Networking Group. Dr. Zhang has published over 400 referenced papers in leading international journals and major conferences, She is the inventor of more than 5(international patents. Her current research interests include the lnternet of Things (loT), smart healthcare, mobile computing and sensing, wireless networks, and cyber security. Dr, Zhang is a Fellow of lEEE and a Fellow of the Hong Kong Academy of Engineering Sciences (HKAES). She has received the TR100 (MlT Technology Review) World's Top Young innovator Award, the HLHL innovation Award, the China Young Scientist Award, and the Second Class National Natural Science Award (as the third contributor). She has also received best paper awards at multiple international conferences, Dr. Zhang served as the Editor-in-Chief of lEEE Transactions on Mobile Computing from 2020 to 2022, She is currently a member of the lEEE Infocom Steering Committee.

• Host: Prof. Hongbo Jiang. Time: July 5, 2024. 16:30. Venue: College of CSEE Building, Room 624
  Speaker: Professor Jun Luo,  Nanyang Technological University.
  Title: Towards Secured lSAC upon Commodity Wi-Fi
  Abstract: With the widespread deployment of Wi-Fi and the development of Wi-Fi sensing technology, enabling commercial Wi-Fi devices to possess both sensing and communication capabilities, i.e., Wi-Fi integrated Sensing and Communication (lSAC), has become an urgent priority. However, the integration of sensing and communication based on commercial Wi-Fi faces two major challenges, distinct from the sensing-communication integration framework advocated in communication engineering. On the one hand, the inherent nature of Wi-Fi communication leads to a multi-static system deployment (i.e., separate transmitters and receivers), which is incompatible with the monostatic deployment (i.e., co-located transmitters and receivers) commonly used in Rfsensing (radar), thereby introducing numerous error factors that affect sensing performance. On the other hand ISAC must adhere to commercial Wi-Fi standards to maintain good communication quality, which means it cannot use specifically designed (modulated) waveforms and must instead maximize sensing utility using existing Wi-Fi waveforms. In this report, we propose for the first time a revolutionary development path for fundamentally improving Wi-Fi hardware to achieve integrated sensing and communication, We will present the "two and a half steps" we have taken under this framework: 1) the hardware innovation of monostatic Wi-Fi sensing, 1.5) the evolutionary plan for commercial Wi-Fi multi-person sensing, and 2) the revolutionary solution for commercial Wi.Fi single-link multi-person sensing.
  Bio: Dr. Jun Luo, Professor at Nanyang Technological University in Singapore, is an lEEE Fellow. For over twenty years, he has been engaged in research on wireless sensing, deep learning, and computational system integration. He has led numerous national research projects and corporate joint projects, including Singapore Ministry of Education Tier 2 projects, and collaborations with BMW, SAP, and CSIJRl, contributing significantly to the development and practical application of mobile computing, pervasive computing, and smart sensing technologies. Based on his research achievements, Dr. Luo has published over 160 papers in leading international conferences and journals. such as MobiCom, CCS, CVPR/lCCV, SenSys, S&P, INFOCOM, UbiComp, ToN, and TMC. Among these, two papers have been cited over 1000 times each, and his total citations on Google Scholar exceed 10,000. For more information, please visit Dr. Luo's homepage: https://personal.ntu.edu.sg/junluo/.

• Host: Prof. Hongbo Jiang. Time: July 5, 2024. 10:30AM. Venue: College of CSEE Building, Room 624
  Speaker: Professor Dapeng Oliver Wu,  City University of Hong Kong.
  Title: Federated Continual Learning
  Abstract: The goal of federated learning is to preserve data privacy when training Artificial Intelligence (AI) systems, while continual learning is to enable an AI system to acquire new skills without forgetting old skills.  To combine the capabilities of federated learning and continual learning, federated continual learning (FCL) arises.  But before FCL can enjoy the benefits of both federated learning and continual learning, FCL needs to be able to effectively transfer knowledge across different clients and across various tasks. Current FCL methods mainly focus on avoiding interference between tasks, thereby overlooking the potential of knowledge transfer across tasks learned by different clients in separated time intervals. To address this issue, in this talk, I will present a Prompt-based Knowledge Transfer FCL algorithm, to effectively foster the transfer of knowledge encapsulated in prompts between various sequentially learned tasks and clients.
  Bio: Dapeng Oliver Wu received Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, PA, in 2003. Currently, he is Yeung Kin Man Chair Professor of Network Science, at the Department of Computer Science, City University of Hong Kong. His research interests are in the areas of artificial intelligence, FinTech, communications, image processing, computer vision, signal processing, and biomedical engineering. He received University of Florida Term Professorship Award in 2017, University of Florida Research Foundation Professorship Award in 2009, AFOSR Young Investigator Program (YIP) Award in 2009, ONR Young Investigator Program (YIP) Award in 2008, NSF CAREER award in 2007, the IEEE Circuits and Systems for Video Technology (CSVT) Transactions Best Paper Award for Year 2001, the Best Paper Award in GLOBECOM 2011, and the Best Paper Award in QShine 2006. He has served as Editor-in-Chief of IEEE Transactions on Network Science and Engineering, and Associate Editor of IEEE Transactions on Cloud Computing, IEEE Transactions on Communications, IEEE Transactions on Signal and Information Processing over Networks, IEEE Signal Processing Magazine, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Wireless Communications and IEEE Transactions on Vehicular Technology. He was the founding Editor-in-Chief of Journal of Advances in Multimedia between 2006 and 2008. He has served as Technical Program Committee (TPC) Chair for IEEE INFOCOM 2012. He was elected as a Distinguished Lecturer by IEEE Vehicular Technology Society in 2016. He is an IEEE Fellow.

• Host: Prof. Hongbo Jiang. Time: June 6, 2024. 10:00AM. Venue (Tencent Meeting): 977-524-608
  Speaker: Professor Song Guo,  The Hong Kong University of Science and Technology.
  Title: Towards Edge-Native Foundation Models
  Abstract: Foundation Models like GPT, LLaMA, and DALL-E have been transformative in AI, demonstrating remarkable versatility across tasks. Yet, the full potential of edge computing—with its inherent benefits in cost, latency, and privacy—remains untapped for deploying these models. In this talk, we unveil the concept of Edge-native Foundation Models, an innovative approach that harnesses the power of distributed, diverse, and collaborative edge computing resources. We introduce a user-friendly Edge FM-as-a-service system, allowing seamless access to Foundation Model services without the burdens of expensive deployment or intricate management. Furthermore, we present a novel, environment-responsive adaptation strategy for Edge-native FMs, enabling rapid tuning to meet the dynamic demands of edge environments. Crucially, our methodology emphasizes a commitment to ethical standards and regulatory compliance for AI governance at the edge. We envisage a future where Foundation Models are conceived, nurtured, and utilized within the edge ecosystem. Our empirical findings suggest that Edge-native Foundation Models could level the AI playing field, disrupt the centralization of data processing, and offer a viable, scalable architecture for AI's evolution.
  Bio: Song Guo is a full professor in the Department of Computer Science and Engineering at Hong Kong University of Science and Technology. Prof. Guo made fundamental and pioneering contributions to the development of edge AI and cloud-edge computing which has created significant impact from generation of new scientific knowledge to creation of innovative technologies. He published many papers in top venues and received over a dozen Best Paper Awards from IEEE/ACM conferences, journals and technical committees. He is the recipient of 2024 Edward J. McCluskey Technical Achievement Award, Gold Medal in 2023 Geneva Inventions Expo, Gold Award in 2023 AsiaWorld-Expo, and Intellectual Property Ambassador Award in 2020 Hong Kong Social Enterprise Competition. Prof. Guo is a Fellow of the Canadian Academy of Engineering, Member of Academia Europaea, and Fellow of the IEEE. Prof. Guo has served on IEEE Fellow Evaluation Committee for both ComSoc and Computer Society. He is the founding and current Editor-in-Chief of IEEE Open Journal of the Computer Society and a member of Steering Committee of IEEE TCC. Prof. Guo has been named on editorial board of a number of prestigious international journals like IEEE TC, IEEE TPDS, IEEE TCC, etc. He has also served as chair of organizing and technical committees of numerous IEEE/ACM conferences and workshops. He has served on RGC engineering panel and been frequently invited for various national and international grant/award reviews.

• Host: Prof. Hongbo Jiang. Time: May 16, 2024. 21:00PM. Venue (Tencent Meeting): 520-342-555
  Speaker: Professor Yingying (Jennifer) Chen,  Rutgers University.
  Title: Is AI Good or Bad for Edge Sensing and Computing? Advancements, Vulnerabilities, and Opportunities
  Abstract: The pervasive usage of edge devices such as IoT devices, smartphones, AR/VR headsets, delivery drones, and autonomous vehicles, has experienced a notable upward trend. This trend offers unprecedented opportunities for on-device intelligence and a wide range of edge sensing and computing applications. Artificial Intelligence (AI) has emerged as a key enabler enhancing the efficiency of these emerging applications, including AR/VR applications, intelligent audio assistant systems, and behavior-based user authentication. However, the increasing reliance on AI also introduces inherent security vulnerabilities, which pose significant threats when deploying these applications in real-world environments. This talk aims to discuss recent advancements in AI-enabled edge sensing and computing achieved through hardware-software co-design and on-device AI, highlighting their significant contributions to achieving efficient inference. Additionally, it will delve into the exploration of new attack surfaces, such as adversarial attacks and backdoor attacks, that arise with the integration of AI. Case studies of AR/VR applications and intelligent edge-based audio systems will be presented to illustrate these concepts. Furthermore, the talk will explore novel opportunities, such as domain invariant modeling, which leverage AI to enhance efficiency and bolster security defense in edge devices, thereby advancing next-generation edge sensing and computing capabilities.
  Bio: Yingying (Jennifer) Chen is a Professor and Department Chair of Electrical and Computer Engineering (ECE) and Peter Cherasia Endowed Faculty Scholar at Rutgers University. She is the Associate Director of Wireless Information Network Laboratory (WINLAB). She also leads the Data Analysis and Information Security (DAISY) Lab. She is a Fellow of ACM, a Fellow of IEEE and a Fellow of National Academy of Inventors (NAI). She is also an ACM Distinguished Scientist. Her research interests include Applied Machine Learning in Mobile Computing and Sensing, Internet of Things (IoT), Security in AI/ML Systems, Smart Healthcare, and Deep Learning on Mobile Systems. She is a pioneer in RF/WiFi sensing, location systems, and mobile security. Before joining Rutgers, she was a tenured professor at Stevens Institute of Technology and had extensive industry experiences at Nokia (previously Lucent Technologies). She has published 3 books, 4 book chapters and 300+ journal articles and refereed conference papers. She is the recipient of seven Best Paper Awards in top ACM and IEEE conferences. She is the recipient of NSF CAREER Award and Google Faculty Research Award. She received New Jersey Inventors Hall of Fame Innovator Award and is also the recipient of IEEE Region 1 Technological Innovation in Academic Award. Her research has been supported by many funding agencies including NSF, NIH, ARO, DoD and AFRL and reported in numerous media outlets including MIT Technology Review, CNN, Fox News Channel, Wall Street Journal, National Public Radio and IEEE Spectrum. She has been serving/served on the editorial boards of IEEE Transactions on Mobile Computing (TMC), IEEE Transactions on Wireless Communications (TWireless), IEEE/ACM Transactions on Networking (ToN) and ACM Transactions on Privacy and Security (TOPS). For more information, please refer to her homepage at: http://www.winlab.rutgers.edu/~yychen/.

• Host: Prof. Hongbo Jiang. Time: April 25, 2024. 10:30AM. Venue (Tencent Meeting): 638-174-728
  Speaker: Professor Lin Cai,  University of Victoria.
  Title: Intelligent Protocol Architecture for 6G
  Abstract: The fusion of digital and real worlds in all dimensions will be the driving force for future sixth-generation (6G) wireless systems. Ubiquitous in-time and on-time communication services between humans, machines, robots, and their virtual counterparts are essential, and they expand from the ground to air, space, underground, and deep sea. 6G systems are not only data pipelines but also large-scale distributed computing systems with integrated sensing, processing, storage, communication and computing capabilities. It is challenging to build ubiquitous and intelligent 6G systems, handling stringent quality-of-service (QoS) requirements, providing a rich set of communication modes, including unicast, multicast, broadcast, in-cast, and group-cast, and supporting user-centric mobile applications. In this talk, we introduce a new protocol architecture that can provide a wide range of control functions, and be intelligently configured for different types of 6G applications and networking environments. Its design principles, self-evolving and transformative potentials, and open issues are discussed. We also introduce two use cases applying the architecture to develop a delay-guaranteed routing protocol and a mobility-aware multi-path QUIC protocol for seamless handover in satellite networks.
  Bio: Lin Cai is a Professor with the Department of Electrical & Computer Engineering at the University of Victoria. She is an NSERC E.W.R. Steacie Memorial Fellow, an Engineering Institute of Canada (EIC) Fellow, a Canadian Academy of Engineering (CAE) Fellow, and an IEEE Fellow. In 2020, she was elected as a Member of the Royal Society of Canada's College of New Scholars, Artists and Scientists, and a 2020 "Star in Computer Networking and Communications" by N2Women. Her research interests span several areas in communications and networking, focusing on network protocol and architecture design supporting ubiquitous intelligence. She was a recipient of the NSERC Discovery Accelerator Supplement (DAS) Grants in 2010 and 2015, respectively. She has co-founded and chaired the IEEE Victoria Section Vehicular Technology and Communications Joint Societies Chapter. She is an elected member of the IEEE Vehicular Technology Society (VTS) Board of Governors (BoG), 2019 – 2024 and serves its VP Mobile Radio since 2023. She is a BoG member of IEEE Communications Society (2024-2026) and IEEE Women-in-Engineering (2022-2024). She is the Associate Editor-in-Chief for IEEE Transactions on Vehicular Technology and has served as a Distinguished Lecturer of both the IEEE VTS Society and the IEEE ComSoc Society.

• Host: Prof. Hongbo Jiang. Time: April 18, 2024. 10:00AM. Venue (Tencent Meeting): 805-608-478
  Speaker: Professor Shiwei Mao,  Auburn University.
  Title: How to reduce the reliance on data in deep learning-based wireless research
  Abstract: Deep learning has shown great promise in solving many open challenges in wireless networking research and applications and intelligence has been recognized as a defining feature of the next generation wireless networks. However, deep learning is data hungry, and one of the critical obstacles towards fulfilling its promise is facilitating the acquisition of sufficient amounts of data to train and validate deep learning models. In this talk, we examine various approaches that enable wireless researchers and practitioners to acquire data more efficiently at reduced cost and to utilize existing data more effectively. In particular, we will review several effective approaches to reduce the reliance on data in deep learning-based wireless research, such as data imputation and augmentation methods with case studies. These approaches are quite effective and general, and should be helpful to researchers in this exciting field to tackle other data-drive wireless problems.
  Bio: Shiwen Mao (S'99-M'04-SM'09-F'19) is a Professor and Earle C. Williams Eminent Scholar, and Director of the Wireless Engineering Research and Education Center at Auburn University. Dr. Mao's research interest includes wireless networks, multimedia communications, and smart grid. He is the editor-in-chief of IEEE Transactions on Cognitive Communications and Networking. He received the IEEE ComSoc MMTC Outstanding Researcher Award in 2023, the 2023 SEC Faculty Achievement Award for Auburn, the IEEE ComSoc TC-CSR Distinguished Technical Achievement Award in 2019, the Auburn University Creative Research & Scholarship Award in 2018, the NSF CAREER Award in 2010, and several service awards from IEEE ComSoc. He is a co-recipient of the 2022 Best Journal Paper Award of IEEE ComSoc eHealth Technical Committee, the 2021 Best Paper Award of Elsevier/KeAi Digital Communications and Networks Journal, the 2021 IEEE Internet of Things Journal Best Paper Award, the 2021 IEEE Communications Society Outstanding Paper Award, the IEEE Vehicular Technology Society 2020 Jack Neubauer Memorial Award, the 2018 Best Journal Paper Award and the 2017 Best Conference Paper Award from IEEE ComSoc MMTC, the 2004 IEEE Communications Society Leonard G. Abraham Prize in the Field of Communications Systems, and 10 IEEE best conference paper/demo awards.