Invited Speakers

Invited Speakers(Alphabetize by Last Name)

Dr. Riccardo Bassoli

Technische Universität Dresden, Germany

Riccardo Bassoli is a Junior professor (US Assistant Professor, UK Lecturer) at the Deutsche Telekom Chair of Communication Networks and Head of the Quantum Communication Networks Research Group, at the Faculty of Electrical and Computer Engineering, at Technische Universitat Dresden. He is also “ member of the Centre for Tactile Internet with Human-in-the-loop (CeTI), Cluster of Excellence, Dresden. He got his Ph.D. from 5G Innovation Centre at University of Surrey (UK), in 2016. Between 2016 and 2019, he was postdoctoral researcher at Università di Trento (Italy). He is IEEE and ComSoc member. He is also member of Glue Technologies for Space Systems Technical Panel of IEEE AESS. His research interests include: quantum communication networks and their integration with 6G and the Tactile Internet, 6G three-dimensional networking, network virtualization, and low-latency resilient communications.
Title: Can quantum computing play a role in 6G?
Abstract: The research and design of 6G has involved the scientific community and industry since 2020, and in the upcoming years the standardisation efforts are expected to start. Artificial intelligence (AI) and softwarization are going to play a pivotal role in 6G, creating an unprecedented challenge to in-network computing paradigms. That is why, it is important to research and identify novel computing technologies that may help in achieving the envisioned objectives for 6G. In this context, can quantum computing provide the additional computing resources to 6G in-network computing to avoid the intrinsic limitations and trade-offs of 'classical' technologies?

Dr. Muhammad Asif Khan

Qatar Mobility Innovations Center, Qatar

Muhammad Asif Khan (SMIEEE) is a Research Scientist at Qatar Mobility Innovations Center (QMIC). He received a Ph.D. degree in Electrical Engineering from Qatar University (2020), an M.Sc. degree in Telecommunication Engineering from the University of Engineering and Technology Taxila, Pakistan (2013), and a B.Sc. degree in Telecommunication Engineering from the University of Engineering and Technology Peshawar, Pakistan (2009). He also serves/served as an associate editor (AE) of the IEEE Technology Policy and Ethics newsletter and guest editor at Frontiers in Communications and Networks. He also serves on the review panel of high-impact IEEE transactions and journals. He has also served/serving as a TPC member of major IEEE conferences including IEEE ICC, IEEE CCNC, IEEE SusTech, IEEE BigData, IEEE Sensors, IEEE ICASSP, etc. He has published more than 40 peer-reviewed articles in high-impact journals and international conferences. He is a Senior Member of the IEEE and a Member of IET. He is a chartered engineer (CEng) with the Engineering Council (EC-UK). His current research focuses on edge intelligence, distributed ML, computer vision, and cybersecurity.
Title: Intelligent Edge for 6G and Massive IoT Systems
Abstract: The Massive Internet of Things (MIoT) systems are emerging as new platforms potentially connecting billions of devices and sensors, enabling a wide range of applications in various domains such as healthcare, agriculture, transportation, and smart cities. The traditional cloud computing platforms are not designed to serve IoT systems and may face several challenges including scalability, reliability, and security to serve the unprecedented demands and QoS requirements of these MIoT systems. Thus, edge computing due to its proximity and an integrated part of 6G networks can not only solve these challenges but also open new opportunities to deploy new IoT applications and services. In addition to efficient and on-demand caching and computing, intelligent edge platforms also enable distributed AI-based services for several industry verticals.
This talk discusses several current edge computing platforms and novel state-of-the-art (SOTA) proposals such as edge-based computation offloading, cooperative edge systems, and distributed training and inference. New research directions will be discussed for prospective researchers in the area of IoT and mobile computing.

Assoc. Prof. Dr. Wael Yafooz

Taibah University, Saudi Arabia

Dr. Wael Yafooz is an associate professor in the Computer Science Department, Taibah University (TU), Saudi Arabia. He has received his PhD in Computer Science in 2014 from University of MARA Technology (UiTM)- Malaysia with research Excellence award with graduate of time status and also an M.S degree in Computer Science with distinction status in 2010 from UiTM. He was hired as a postdoctoral research fellow in the same university. He has more than 15 years of academic experience in teaching, research and leadership roles in other higher education institutions. He served at Al-Madinah international university (MEDIU), Malaysia. He headed the computer science department, deputy dean, and college dean at MEDIU, and served as chair/member of multiple committees at university level. He published approximately 90 research papers in internationally recognized conferences and indexed journals (Scopus and ISI) with high impact factors. He supervised number a of students at PhD and master levels. He served as a member of various committees in many international conferences. Additionally, he chaired IEEE international conferences in Malaysia and China. Furthermore, He delivered and conducted several workshops and presented numerous practical courses in the research area of data management, visualization and curriculum design. He was invited as a speaker in many international conferences held in Bangladesh, Thailand, India, China, Japan and Russia. His research interest includes, Data Mining, Machine Learning, Deep Learning, Natural Language Processing, Social Network Analytics and Data Management. He is the editor of springer books. He acquired multiple research grants.
Title: Enhancing Social Media Analysis with Large Language Models

Abstract: Large Language Models (LLMs) have the potential to significantly enhance social media analytics. This is by improving the ability to process, understand, and generate human-like text. In this talk, I will talk about how they leveraged and used to analyze vast amounts of unstructured social media data, such as posts, comments, and messages, in multiple languages and across diverse platforms. LLMs can accurately detect sentiments, identify trends, and extract key insights. In such way that enables businesses and researchers to understand public opinion, consumer behavior, and emerging topics. Their ability to perform tasks like summarization, topic modeling, and entity recognition further streamlines social media monitoring and content moderation. I will focus on the state of art of the LLMs that can support the detection of harmful behaviors, such as cyberbullying and misinformation, enhancing overall platform safety and user experience. In addition, I will explore the architecture and the importance of these models in saving time and effort. Thus, I will talk about new methods and approaches we used in our projects in applied natural language processing topics.

Dr. Paikun Zhu

National Institute of Information and Communications Technology (NICT), Japan

Paikun Zhu is a Researcher of the National Institute of Information and Communications Technology (NICT), Japan. He has had over 80 IEEE/OSA publications, including multiple invited ones. He received the Chairman’s Award of the IEICE Technical Committee on Communication Systems in 2021, and URSI Young Scientist Award in 2024. He is a Program Committee Member of IEEE ACP2020 & 2024 conferences and an invited speaker of OFC2023 conference. His research interests include optical communication systems/networks, 6G and FPGA.
Title: Advanced optical-layer technologies for next-generation access networks
Abstract: The rapid progress of AI, cloud and 6G calls for further R&D on advanced high-speed and cost-effective connectivity on the network edge (access, fronthaul/backhaul and datacenter networks). In this invited talk, we will discuss techniques for efficient mitigation of channel impairments by means of low-complexity optical and digital signal processing. Optical-layer synergistic design of device & system for these short-reach networks will also be covered.

Assoc. Prof. Tao Li

Tohoku University, Japan

Tao Li received his Ph.D. in Navigation, Guidance, and Control from Harbin Engineering University, China, in 2016. From 2013 to 2015, he was a Visiting Ph.D. Student at the University of Calgary, Canada. He then worked as an Academic Researcher at the Institute of Innovative Science and Technology, Tokai University, Japan, from 2016 to 2018. From 2018 to 2020, he was part of the Japan Science and Technology Agency’s Open Innovation Platform project at Tohoku University. He is currently an Associate Professor at the Center for Innovative Integrated Electronic Systems, Tohoku University, and a member of the Cross-Ministerial Strategic Innovation Promotion Program. His research focuses on non-volatile brainware processors, digital signal processing, and inertial navigation systems.
Title: Bridging Artificial Intelligence and Non-volatile Devices for Energy-efficient Edge Computing
Abstract: Recent developments in non-volatile devices and deep neural networks (DNNs) provide substantial opportunities for improving brain-inspired processors in edge computing. However, each of these fields has progressed mostly separately, leaving a gap in leveraging their combined potential capabilities. The goal of this talk is to fill the gap by combining DNN fault tolerance with non-volatile device switching properties. The new approach not only decreases power consumption but also maintains DNN accuracy, opening opportunities for more efficient and high-performance edge computing.