2023 International Conference on Algorithms, High Performance Computing and Artificial Intelligence(AHPCAI 2023)
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Prof. Kun Yang

Member of Academia Europaea (MAE), IEEE Fellow, IET Fellow

University of Essex

Professor Kun Yang is a Member of Academia Europaea (MAE), a Fellow of IEEE, a Fellow of IET (Institute of Engineering and Technology), a Fellow of BCS (British Computing Society) and a Distinguished Member (DM) of ACM.

He received my PhD degree from University College London (UCL) in the area of communication networks and services. After 3 years' post-doc research in the Department of EEE at UCL working on EU research projects such as FAIN (Future Active IP Networks), MANTRIP and CONTEXT, he moved to University of Essex at 2003 to become an academic staff. Now he is a Chair Professor in the School of Computer Science and Electronic Engineering (CSEE), University of Essex, leading the Network Convergence Laboratory (NCL). 

His current research activities are focused on advancing new technologies related to communication and network systems, and on the fundamentals underlying them, including communication theory, algorithms, network science, machine learning and edge intelligence. In particular he is interested in energy aspects of future communication systems such as 6G, promoting energy self-sustainability via both energy efficiency (green communications and networking) and energy harvesting (wireless charging).

He has published 300+ technical papers and 2 monographs in the above areas. He has been managing research projects funded by various sources such as UK EPSRC, EU FP7/H2020 and industries (e.g., British Telecom). He coordinated one EU FP7 research project (EVANS). I have been a Judge of GSMA Global Mobile Awards (MWC-Barcelona) since 2019 and a member of UK EPSRC Peer Review College since 2008. I serve on editorial boards of both IEEE journals (e.g., IEEE TNSE, ComMag, WCL) and non-IEEE journals (e.g., Deputy EiC of IET Smart Cities). He has been actively organizing international conferences/symposia/workshops, such as IEEE ComSoc flagship conference Globecom. He is a Distinguished Lecturer of IEEE ComSoc (2020-2021).

Title: 6G Mobile Communication Networks Enabled by Data-driven Machine Learning

Abstract: With 5G mobile communication systems being commercialized and deployed worldwide, research into next-generation communication systems (i.e., 6G) has started since 2019. Computational intelligence, as represented by data-driven machine learning (ML) techniques, is essential for 6G to deliver its promises of being faster, greener and smarter. This talk starts with a brief introduction of 6G mobile communication systems, and then looks into how artificial intelligence (AI) and ML come into play in 6G from different perspectives. It covers new trends in 6G communication research such as data-driven communication system design, semantic communications, digital twin networks (DTN), and ISAC (Integrated Sensing and Communication), all from the perspective of how data-driven ML plays a role in advancing modern communication systems. 


Prof. Guangjie Han

IEEE Fellow, IET/IEE Fellow, AAIA Fellow

Hohai University

Guangjie Han (Fellow, IEEE) is currently a Professor with the Department of Internet of Things Engineering, Hohai University, Changzhou, China. He received his Ph.D. degree from Northeastern University, Shenyang, China, in 2004. In February 2008, he finished his work as a Postdoctoral Researcher with the Department of Computer Science, Chonnam National University, Gwangju, Korea. From October 2010 to October 2011, he was a Visiting Research Scholar with Osaka University, Suita, Japan. From January 2017 to February 2017, he was a Visiting Professor with City University of Hong Kong, China. From July 2017 to July 2020, he was a Distinguished Professor with Dalian University of Technology, China. His current research interests include Internet of Things, Industrial Internet, Machine Learning and Artificial Intelligence, Mobile Computing, Security and Privacy. Dr. Han has over 500 peer-reviewed journal and conference papers, in addition to 160 granted and pending patents. Currently, his H-index is 59 and i10-index is 249 in Google Citation (Google Scholar). The total citation count of his papers raises above 12900+ times.

Dr. Han is a Fellow of the UK Institution of Engineering and Technology (FIET). He has served on the Editorial Boards of up to 10 international journals, including the IEEE Systems, IEEE/CCA JAS, IEEE Network, etc. He has guest-edited several special issues in IEEE Journals and Magazines, including the IEEE JSAC, IEEE Communications, IEEE Wireless Communications, IEEE Transactions on Industrial Informatics, Computer Networks, etc. Dr. Han has also served as chair of organizing and technical committees in many international conferences.He has been awarded 2020 IEEE Systems Journal Annual Best Paper Award and the 2017-2019 IEEE ACCESS Outstanding Associate Editor Award. He is a Fellow of IEEE.

Title: Multi-Dimensional Dynamic Trust Management Mechanism in Underwater Acoustic Sensor Networks

Abstract: The underwater acoustic sensor network (UASN) is the core module to realize the "smart ocean". At present, the UASN has not yet fully played its role in the complex water environment. The fundamental reason lies in the lack of effective methods to ensure network security and reliable data transmission. This report mainly introduces the team's research work on the trust management mechanism of UASNs. The main research contents include: 1) Intrusion detection algorithm based on energy consumption prediction model; 2) Multi-dimensional trust calculation algorithm based on fuzzy theory; 3) Trust evaluation algorithm based on cloud theory; 4) Trust cloud migration mechanism based on AUV; 5) Trust update mechanism based on reinforcement learning; 6) Anomaly-resilient trust model based on isolation forest. The research results have important theoretical value and practical significance for exploring the security technology and application of UASNs.


Prof. Hai Jin

IEEE Fellow, CCF Fellow, ACM Life Member

Huazhong University of Science and Technology

Jin Hai is a Chair Professor of computer science and engineering at Huazhong University of Science and Technology (HUST) in China. Jin received his PhD in computer engineering from HUST in 1994. In 1996, he was awarded a German Academic Exchange Service fellowship to visit the Technical University of Chemnitz in Germany. Jin worked at The University of Hong Kong between 1998 and 2000, and as a visiting scholar at the University of Southern California between 1999 and 2000. He was awarded Excellent Youth Award from the National Science Foundation of China in 2001.

Jin is a Fellow of IEEE, Fellow of CCF, and a life member of the ACM. He has co-authored more than 20 books and published over 900 research papers. His research interests include computer architecture, parallel and distributed computing, big data processing, data storage, and system security.

Title: Dataflow based High Efficient Graph Processing Accelerator

Abstract: With the rapid growth of big data, it is harder and harder to processing these ever-growing data with traditional computer architecture. Dataflow-based architecture provides a new way to tackle above challenge. This talk first briefly introduce the challenges in processing big data and also the difficulties in processing graph computing, then introduce some research results we have done during these years in using dataflow for graph computing. Finally, some future directions for dataflow architecture and also when used in graph computing are introduced.


Tenured Assoc. Prof. Ran Cheng

Chair of the IEEE CIS Shenzhen Chapter

Southern University of Science and Technology

Ran Cheng, IEEE Senior Member, is currently employed as a tenured associate professor and doctoral advisor of the Department of Computer Science and Engineering of Southern University of Science and Technology. He used to be Research Fellow of Birmingham University, UK, and visiting scholar of Honda European Research Institute in Germany. Focusing on computational intelligence, he published more than 30 long articles in IEEE Transactions, cited more than 7500 times, and 13 papers were selected into ESI and highly cited. Won the IEEE Institute of Computing Intelligence Outstanding Doctoral Thesis Award (2019), IEEE TEVC Outstanding Thesis Award (2018, 2021), IEEE CIM Outstanding Thesis Award (2020), etc; He was selected into the "Top 2% Scientists in the World (2020, 2021)", "Academic Influence List of Global Scholars (2022)", etc. He is currently the editorial board member of IEEE TEVC, IEEE TAI, IEEE TCDS, etc. We have carried out cooperation with COMAC, Huawei HiSilicon, State Grid, etc., and some of the achievements have been written in the "Fourteenth Five Year Plan" research report on the development strategy of electrical science and engineering science. Research Areas: Computational Intelligence, Evolutionary Computing, Deep Learning.  Achievements and Rewards: IEEE Transactions on Evolutionary Computation, 2021; IEEE Computational Intelligence Magazine, 2020; IEEE Computational Intelligence Society (CIS) , 2019; IEEE Transactions on Evolutionary Computation, 2018.

Title: Automated Hardware-Aware Deployment of Deep Learning Models

Abstract: In recent years, deep learning has achieved remarkable success in various fields. However, as the complexity of models increases, their deployment and execution on hardware-constrained devices become more challenging. This talk aims to explore how to incorporate hardware metrics (such as power consumption, latency, and resource usage) into the consideration of deep learning models to achieve efficient automated model design, ensuring a balance between model performance and hardware resource consumption. Using real-time semantic segmentation tasks in autonomous driving scenarios as an application case, this talk demonstrates the automated deployment performance on edge computing device.