On the Edge of Innovation: A Conversation with Song Guo Technical Achievement Award Recipient

IEEE Computer Society Team
Published 03/25/2024
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GuoProfessor Song Guo is a distinguished figure in edge computing and edge intelligence technology. In addition to being EIC of the IEEE Open Journal of the Computer Society, Guo is a full professor and Changjiang Chair Professor in the Department of Computer Science and Engineering at Hong Kong University of Science and Technology.  He has reached fellowship status at IEEE and the Canadian Academy of Engineering while being a member of Academia Europaea. His contributions span a wide array of research interests such as edge AI, 6G, big data, cloud computing, mobile computing, and distributed systems. This also includes his groundbreaking work in the development of Edge Learning, a novel machine-learning paradigm for training and inference at the edge.

He has authored more than 500 publications, including articles in prestigious journals, conference proceedings, and patents. In addition, he has devoted considerable efforts to serving the Society through membership on various committees and editorial boards, as well as taking on leadership roles in organizing conferences and workshops.


In honor of his many achievements, he has received the 2024 Edward J. McCluskey Technical Achievement Award for, “…pioneering contributions to edge computing and edge intelligence technologies.


First and foremost, congratulations on your recent awards, and thank you for your service to IEEE and the Society! How did your journey within both organizations begin, and how has being a part of this community shaped your career path? Have you formed any long-standing connections through that experience?

Receiving these accolades is truly an honor, and it’s a testament to the vibrant community and the robust network that IEEE and the Society represent. My engagement with these organizations began during my doctoral studies in Canada, where I dove into academic research and developed energy-efficient algorithms for the IoT. The move to Japan was a pivotal point, allowing me to blend Big Data, AI, and other technologies to advance the IoT framework, leading to the evolution of AIoT. This work, especially in critical areas like post-disaster management, has been recognized and celebrated within the IEEE community. As my career progressed, my journey took me to Hong Kong to focus on edge intelligence, striving to create technologies that are not just advanced but also accessible and efficient for real-world applications. It’s this journey that IEEE and the Society have been a part of – shaping my path, influencing my work, and enabling connections with peers who have been instrumental in my growth.

The long-standing connections formed through IEEE and the Society have been pivotal to my career. Collaborations, mentorship, and the exchange of ideas have all played crucial roles. The awards, such as the IEEE Fellowship and the Edward J. McCluskey Technical Achievement Award, are not just personal achievements but also reflections of the collective wisdom and support of this incredible community. For that, I am deeply grateful.

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Additionally, how has your involvement influenced your perspective on the development of edge computing?

Engagement with IEEE and the Society has been a catalyst for innovation, particularly in the realm of edge computing. Our work at the Edge Intelligence Lab embodies the fusion of Edge Learning, AI, Big Data, and Distributed Systems, setting a visionary course for the development of edge computing. Being at the forefront, even before established industry leaders like NVIDIA, we’ve embraced the challenges and opportunities presented by Edge AI. This approach is about bringing AI applications closer to the data source, right on the edge devices, enabling responsive and localized data processing.

Our pioneering efforts in Edge AI have not only pushed the boundaries of what’s possible but have also been acknowledged through various distinguished awards, a testament to the groundbreaking nature of our research. What’s more impactful is our ability to convert theoretical advancements into tangible applications, significantly advancing the edge computing landscape. This practical translation of research to real-life solutions exemplifies the profound influence that my involvement with IEEE and the Society has had on my viewpoint and contributions to the field of edge computing.


Can you elaborate more on your machine learning paradigm, Edge Learning, and how it has advanced training and inference processes?

Edge Learning represents a paradigm shift in machine learning, fundamentally restructuring the way we handle AI by positioning the learning process at the edge of the network, closer to the origin of data. This innovative approach enables training and inference on IoT devices and mobile phones, mitigating the dependency on centralized facilities. By doing so, it significantly enhances the scalability, efficiency, and privacy of machine learning operations.


In the realm of training, Edge Learning democratizes the creation of knowledge. Each edge device contributes to intelligence by training its own model on locally generated data, which are then aggregated to refine a global model. This distributed learning architecture not only fosters real-time adaptation to new information but also minimizes the need for frequent, large-scale data transfers to a central repository.

When it comes to inference, Edge Learning is transformative, equipping edge devices to perform real-time analytics, thus enabling immediate, on-site decision-making. This significantly reduces latency and bolsters the real-time responsiveness of applications, especially those necessitating swift and informed actions.

Underpinning this paradigm is our commitment to an accessible, high-performance AI infrastructure we refer to as the “computing power network”. Throughout a model’s lifecycle, from development through to maintenance and application, this infrastructure is designed to simplify the use of world models. By harnessing a diversity of computing resources, employing intelligent scheduling, and leveraging serverless AI computing, we aim to unify and optimize the utilization of computational resources. The end goal is as ambitious as it is practical: to make the application of AI as ubiquitous and straightforward as the use of electricity.


Your technical contributions have been cited in more than 500 peer-reviewed publications! Could you highlight a breakthrough moment or project you are most proud of?

One of the most defining moments of my career that I hold in high esteem is the development of the resilient information and network system tailored for disaster management. This system integrates edge computing with cloud resources to optimize resource use and management in scenarios where network isolation is a critical challenge. This has been a particularly groundbreaking innovation, given its deployment by NTT in Japan for disaster rescue and management operations. It has proven its worth time and again during natural disasters such as earthquakes, tsunamis, and forest fires, significantly aiding response efforts and positively impacting over 10 million people. This embodies the real-world impact and application of our research, transcending academic discussions to provide tangible societal benefits.

In addition to this, I am extremely proud of our pioneering commercialization program. A notable achievement from this endeavor is the Body Posture Analysis System. By harnessing edge devices like smartphones, we’ve developed a system that not only assesses postural health but also flags potential health risks, offering personalized recommendations. This innovation was celebrated with a Gold Medal at the International Exhibition of Inventions in Geneva in 2023, a moment that stands as a testament to the practical and beneficial application of our work. The system has already positively affected the lives of over 100,000 Chinese teenagers, and we are enthusiastically working towards extending its benefits to over 5 million users. These milestones are not just personal triumphs but also exemplify the transformative power of dedicated research and its potential for widespread, positive change in the world.

Your research spans various areas, including 6G, big data, and cloud computing. How do these domains connect with edge computing, and how do you envision these connections in the future?

The convergence of 6G, big data, and cloud computing with edge computing is a cornerstone of my research. 6G, with its promise of ultra-high-speed connectivity, low latency, and massive capacity, will significantly amplify the efficiency and effectiveness of edge computing. The synergy between 6G and edge computing is poised to enable a myriad of real-time applications, from autonomous driving to remote surgery, by allowing rapid on-site data processing. Looking forward, I envision 6G and edge computing melding into a cohesive framework, yielding a seamless, hyper-connected reality where data is processed instantaneously at the point of collection.

Big data, meanwhile, benefits immensely from edge computing’s capacity to process data at its source. This local processing significantly expedites insights, curtails bandwidth consumption, and cuts down costs, addressing the scalability challenges associated with traditional centralized systems. As the deluge of data grows, edge computing’s role in enabling efficient big data analytics becomes increasingly indispensable.

Regarding cloud computing, edge computing acts as a complementary force, enabling data processing to occur proximate to data sources. This proximity reduces latency, bolstering the performance of real-time applications. My vision for the future encapsulates a hybrid model that integrates the storage and complex computational prowess of the cloud with the real-time, localized processing capabilities of the edge.

In synthesis, the interplay among 6G, big data, and cloud computing with edge computing is integral to our technological trajectory. As these domains evolve, their integration will herald a new era of connectivity, leading to a smarter, more responsive, and interconnected data-centric world.


You are the founding and current Editor-in-Chief of the IEEE Open Journal of the Computer Society, the Computer Society’s first fully open-access publication. How has the development of this publication starting back when it was first launched to where it is now changed throughout the past few years?

The IEEE Open Journal of the Computer Society (IEEE-OJCS) has undergone a remarkable evolution since its inception. Guided by the combined efforts of our Advisory and Editorial Boards, we have consistently focused on publishing articles that deliver high-impact results on emergent topics within computer and information processing science and technology. Our commitment has always been to uphold a rigorously peer-reviewed platform for our authors and readers.

The journal’s recent achievement of a first Impact Factor (IF) of 5.9, as reported by the Journal Citation Reports (JCR), signifies a monumental stride in our journey. To be ranked fourth among all IEEE Open Access journals and to secure a position in the 2nd quartile within the Computer Science field is a testament to the hard work and the high-quality research that is being showcased. Given that IEEE-OJCS was only launched in 2020, this performance is particularly impressive.

Our ambition doesn’t rest here; we are aiming to elevate the journal’s IF in the upcoming years, which would place us in the 1st quartile of the Computer Science category. As we progress, our goal is not only to increase our impact in terms of metrics but to continue to be a venue that is synonymous with academic excellence and integrity. The journey from our launch to where we stand now reflects a vibrant and growing community of contributors and readers who are dedicated to advancing the field of computer science.


How do you foster collaboration and communication among researchers, and what impact do you aim to achieve through this publication?

This publication can provide a platform for researchers and engineers to share their findings. More importantly, this high-impacting publication can also encourage students and young scientists to extend effort and dedicate hard work to increase their knowledge in the field of Computer Science, which feeds many sectors with strategic theoretical and practical contributions.

More About Song Guo

Song Guo is a full professor in the Department of Computer Science and Engineering at Hong Kong University of Science and Technology. He also holds a Changjiang Chair Professorship. His research interests are mainly in edge AI, 6G, big data and cloud computing, mobile computing, and distributed systems. He is a Fellow of the Canadian Academy of Engineering, Member of Academia Europaea, and Fellow of the IEEE.

Prof. Guo demonstrated a new machine-learning paradigm, Edge Learning, for training and inference at the edge. His research is characterized by both theoretical rigor and experimental and system thoroughness. He has named to the Web of Science Global Highly Cited Researchers consistently. His accomplishments are evidenced by foundational and technical breakthroughs in research and great commercial successes in industry. Guo’s technical contributions have been evidenced quantitatively on the basis of more than 500 peer-reviewed publications in premier journals/conferences, tens of granted patents, and numerous professional awards. He received over a dozen Best Paper Awards from IEEE/ACM conferences, journals and technical committees. He is also the recipient of Gold Medal in 2023 Geneva Inventions Expo and Intellectual Property Ambassador Award in 2020 Hong
Kong Social Enterprise Competition.

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, IEEE TETC, IEEE TSUSC, ACM Computing Surveys, 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.