CCCI 2026 Keynote Speakers

Bio

Dr. Kuljeet Kaur works as an Associate Professor at École de technologie supérieure (ÉTS), Montreal since 2020. Her research interests are cybersecurity, Cloud/Edge Computing, the Internet of Things (IoT), applied machine learning and artificial intelligence, Communications, and Smart Grids. She published over 75 scientific/technical articles and 3 books. She has secured research funding from various sources such as the Natural Sciences and Engineering Research Council of Canada (NSERC), Fonds de Recherche du Québec Nature et technologies (FRQNT), Department of Science and Technology (DST), and TCS Innovations Labs. Dr. Kaur is the recipient of the 2023 N2Women Rising Stars in Networking and Communications and the 2021 IEEE Technical Committee on Scalable Computing (TCSC) Award for Excellence in Scalable Computing for Early Career Researchers. She was also awarded the 2021 IEEE System Journal and 2018 IEEE ICC best paper awards. She also received the Best Research Paper Awards from the Thapar Institute of Engineering & Technology in 2022 and 2019.

She has been serving as an Editor/Guest Editor for various international journals of repute, such as Computer Communications (Elsevier), Ad hoc Networks (Elsevier), Wiley Security and Privacy Journal, Journal of Information Processing Systems, Human‑centric Computing and Information Sciences (Springer), Frontiers in Communications and Networking: Board of Smart Grid Communications, International Journal of Applied Engineering Research (IJAER), and Human‑centric Computing and Information Sciences (Springer). She has organized different special issues at different venues such as IEEE Internet of Things Journal, IEEE Transactions of Industrial Informatics, IEEE Transactions on Consumer Electronics, and IEEE Open Journal of the Computer Society. She has been organizing international symposiums and workshops for several flagship conferences such as IEEE GLOBECOM, IEEE INFOCOM, IEEE ICC, ACM MOBICOM and others. She served as a technical program committee (TPC) member for several international conferences, including IEEE GLOBECOM and IEEE ICC.

Abstract

Machine learning systems are increasingly deployed in distributed and privacy-sensitive environments, where data cannot be centralized due to regulatory, security, and confidentiality constraints. Federated Learning offers a compelling solution by enabling collaborative model training without directly accessing raw data. However, this decentralization introduces a critical vulnerability: the learning process itself becomes a new and powerful attack surface.

In practice, federated systems are exposed to poisoned model updates, malicious or unreliable clients, and inference attacks that can extract sensitive information from shared gradients. Although existing defense strategies leverage techniques such as robust aggregation, secure computation, and differential privacy, they remain fundamentally constrained by a persistent trade-off: it is difficult to simultaneously achieve strong privacy guarantees, high robustness against adversaries, and efficient scalability in real-world deployments.

This talk presents a set of novel defence strategies for building secure, reliable, and resilient federated learning systems that address these challenges in a unified manner.

Bio

Dr. Bo Zhang is currently an Associate Professor, and Vice Dean of the College of Electronic and Communication Engineering and the College of Artificial Intelligence at Tianjin Normal University. He received his Ph.D. degree from the University of Sheffield, UK, in 2018.

His research interests lie at the forefront of wireless communications and signal processing, with a particular focus on Integrated Sensing and Communications (ISAC), Simultaneous Wireless Information and Power Transfer (SWIPT), physical layer secure communications via directional modulation, and array signal processing.

Dr. Zhang has authored more than 50 peer-reviewed scientific papers in journals and conferences and holds 5 authorized national invention patents.

Abstract

As wireless networks rapidly evolve toward the 6G era, securing data transmission in highly open and dynamic radio environments has become a critical challenge. While traditional cryptographic methods face growing computational vulnerabilities, Physical Layer Security (PLS) has emerged as a robust, hardware-level alternative. Among PLS techniques, Directional Modulation (DM) stands out as a cutting-edge paradigm. By exploiting array signal processing and advanced beamforming, DM synthesizes secure signals exclusively along pre-specified spatial directions, deliberately distorting the signal constellation for unauthorized eavesdroppers in all other directions.

This keynote will provide a comprehensive overview of the principles and recent breakthroughs in DM technologies. We will explore innovative array architectures and beamforming designs that maximize secrecy capacity while enhancing energy efficiency. Furthermore, the talk will delve into the transformative applications of DM in next-generation wireless systems. A special focus will be placed on its integration into Integrated Sensing and Communications (ISAC) paradigms. We will discuss how DM can be leveraged to not only guarantee secure data delivery to intended users but also facilitate simultaneous, high-precision target sensing and tracking using shared spectrum and hardware resources.

Finally, the presentation will highlight open challenges, practical implementation considerations, and promising future research directions for DM-empowered secure networks, offering valuable insights for both researchers and industry practitioners.

Bio

Dr. Yijie (Lina) Mao is an Assistant Professor at the School of Information Science and Technology, ShanghaiTech University, Shanghai, China. She received the B.Eng. degree from the Beijing University of Posts and Telecommunications, and the B.Eng. (Hons.) degree from the Queen Mary University of London (London, United Kingdom) in 2014. She received the Ph.D. degree in the Electrical and Electronic Engineering Department from the University of Hong Kong (Hong Kong, China) in 2018. She was a Postdoctoral Research Fellow at the University of Hong Kong (Hong Kong, China) from Oct. 2018 to Jul. 2019 and a postdoctoral research associate with the Communications and Signal Processing Group (CSP), Department of the Electrical and Electronic Engineering at the Imperial College London (London, United Kingdom) from Aug. 2019 to Jul. 2021. Her research interests include the design of future wireless communications and artificial intelligence-empowered wireless networks. She is a senior member of both IEEE and the China Institute of Communications.

Dr. Mao receives the Best Paper Awards of EURASIP Journal on Wireless Communications and Networking (JWCN) 2022 and IEEE International Mediterranean Conference on Communications and Networking (MeditCom) 2023, the Exemplary Editor for IEEE Communications Surveys & Tutorials 2025, as well as the Exemplary Reviewer for IEEE Transactions on Communications 2021 and IEEE Communications Letters 2022, 2023. She is currently serving as an associate editor for IEEE Communications Surveys & Tutorials, IEEE Transactions on Mobile Computing, and IEEE Communications Letters. She was a guest editor for four special issues of IEEE Journal on Selected Areas in Communications, IEEE Transactions on Green Communications and Networking, and IEEE Open Journal of the Communications Society. She has been a Workshop Chair, a Track Chair, and Technical Program Committee (TPC) member for several leading international IEEE conferences. She has been recognized as the World's Top 2% Scientists by Stanford University in 2023-2025.

Abstract

Multiple access (MA) is essential in wireless systems, using dimensions like time, frequency, power, antennas, and code to serve multiple users efficiently. As wireless networks evolve to support communication, sensing, localization, and computing, along with the rise of machine learning and artificial intelligence (AI), MA techniques are expected to undergo major changes in 6G and future networks. This talk provides an overview of the past, present, and future of MA techniques, with a specific focus on their convergence with AI and their critical role in enabling multi-functional 6G systems. Concluding with a forward-looking roadmap for 6G standardization, this tutorial aims to consolidate the expansive landscape of MA into a coherent framework, offering a holistic perspective for researchers and engineers.

Bio

Chi Lin received B.E. and Ph.D. degrees from Dalian University of Technology, Dalian, China, in 2008 and 2013, respectively. He has been an Assistant Professor with the School of Software, Dalian University of Technology, since 2014. He is now a full Professor with the School of Software, Dalian University of Technology. He has authored more than 50 scientific papers in several journals and conferences, including MobiCom, INFOCOM, SECON, ICDCS, ICNP, ICPP, IEEE TON, IEEE TMC, ACM TECS, IEEE TVT, and the special issue of \emph{SCIENCE}. His research interests include pervasive computing and wireless sensor networks. In 2015, he was the recipient of the ACM Academic Rising Star.

Abstract

Contactless wireless sensing is rapidly transforming the way intelligent systems perceive human activities and surrounding environments. By exploiting ubiquitous radio frequency (RF) signals, it enables continuous, privacy-preserving, and low-cost sensing without requiring wearable devices or cameras, making it a promising foundation for next-generation smart healthcare, intelligent environments, and pervasive computing.

This keynote presents our recent research advances in RF-based contactless sensing, covering the complete pipeline from fundamental sensing principles and signal processing techniques to practical system design and real-world deployments. Specifically, I will introduce three representative research directions developed in our laboratory: WiFi-based human fall detection for elderly healthcare, millimeter-wave radar sensing for high-precision contactless respiratory monitoring, and ultra-wideband (UWB)-based ambient humidity sensing for fine-grained environmental perception. These systems demonstrate how different wireless technologies can be leveraged to extract diverse physiological and environmental information while addressing common challenges such as weak signal recovery, environmental dynamics, multipath interference, and sensing robustness.

Beyond individual applications, the keynote will highlight the common theoretical foundations and algorithmic insights underlying heterogeneous wireless sensing systems, discuss practical deployment experiences and remaining technical challenges, and outline future opportunities toward ubiquitous, intelligent, and trustworthy contactless sensing. The talk aims to provide a comprehensive overview of recent advances and inspire future research and industrial innovation in RF-based sensing.

Bio

Zhengyu Wan received the B.Eng. degree in Electronic Information Engineering from Shenzhen University, China, in 2015, and the M.Sc. and Ph.D. degrees from the University of Sheffield, UK, in 2017 and 2023, respectively. He is currently a Lecturer with the Faculty of Engineering at Shenzhen MSU-BIT University. His research interests include array signal processing, direction-of-arrival (DOA) estimation and localization, sparse signal processing, phase retrieval, and non-coherent array processing. He has published more than 10 peer-reviewed papers in leading international journals and conferences.

Dr. Wan has served as the principal investigator of an Innovative Talent Project funded by the Department of Education of Guangdong Province. And has also been actively involved in academic service and recently co-organized a special session on Spectrum and Direction Estimation with Low-Resolution Data at the 2025 IEEE Workshop on Signal Processing Systems (SiPS).

Abstract

Wideband direction-of-arrival (DOA) estimation is important in radar and wireless communication systems. Conventional methods typically operate in the frequency domain by decomposing wideband signals into multiple narrowband frequency bins, but their performance may degrade when only limited snapshots are available. This talk presents a time-domain wideband DOA estimation framework based on convolutional sparse coding. Propagation delays across array sensors are directly modeled as convolutional filters, leading to a structured representation of the received signals. By exploiting the spatial sparsity of incident sources, the estimation problem is formulated as a group-sparse recovery problem. We further extend the framework to magnitude-only measurements, which are insensitive to frequency-independent sensor phase errors. The resulting problem is formulated as a group-sparse phase retrieval problem. Simulation results demonstrate effective performance with limited snapshots and improved robustness to sensor phase errors.

Bio

Abderrahim Benslimane is Full Professor of Computer-Science at the Avignon University/France since 2001. He is Former Vice Dean of the Faculty of Sciences and Technology and Former head of the master Degree SICOM. He is IEEE VTS Distinguished Lecturer/Speaker. He is IEEE ComSoc representative of the IEEE Blockchain Technical Community (BCTC). Currently, he is serving as Vice-Chair of the IEEE VTS Committee on Mission Critical Communications and Vice-Chair of the Steering Committee of the IEEE ComSoc Social Network Technical Committee. He served as IEEE ComSoc Steering Chair of Multimedia Communications Technical Committee 2022–2024 and previously served as Vice Chair 2020–2022. He is past Chair of the ComSoc Technical Committee of Communication and Information Security 2017–2019. He is Advisory board member of IEEE IoT journal, Associate Editor of IEEE Transactions on Information Forensics and Security, IEEE Wireless Communication Magazine, IEEE Transactions on Vehicular Technology, IEEE Transactions on Mobile Computing and Elsevier Ad Hoc Networks. He is co-founder and serves as General-Chair of the IEEE WiMob since 2005; the 2026 edition will be held in Avignon, France during 14-16 October 2026. He was Board committee member, Vice-chair of Student activities of IEEE France section/Region 8; he was Publication Vice-chair and Conference Vice-Chair of the ComSoc TC of Communication and Information Security. He participates to the steering and the program committee of many IEEE international conferences. He has more than 350 refereed international publications (books, conference proceedings, journals and conferences) and more than 25 Special issues. All publications are in his research topics. He supervised more than 25 Ph.D thesis and more than 42 M.Sc. research thesis. He managed several funded projects in national and international collaboration.

Abstract

Managing trust in a distributed context, such the one that we are targeting Industrial Internet of Things (IIoT), is challenging when collaborating nodes don’t know each other, have no stable connections, can be huge, have severe resources constraints (e.g., computing power, energy, bandwidth, time), and dynamics (e.g., topology changes, node mobility, node failure, propagation channel conditions). Moreover, they have heterogeneous relationships.

This talk will introduce trust in general and then will introduce several examples where we applied modeling and managing trust. While certificate revocation strategies in wireless networks introduces a heavy complexity mainly in IoT, we introduced trust to renew/revoke certificates for a security purpose. As illustration, I will present our contribution which is a new efficient certificate verification scheme based on short-lived certificate and suitable for IIoT network requirements. Also, I will present our work that introduces trust in federated Learning. Finally, I will discuss some open researches using Trust in 6G.