Distinguished Seminar Series in Computer Networks and Communications

• 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.