2023 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE 2023)


Keynote Speakers

Yun Li (IEEE Fellow)

University of Electronic Science and Technology of China


Biography: Yun Li was an intelligent systems Engineer with the U.K. National Engineering Laboratory, Glasgow, in 1989, and a postdoctoral Research Engineer with Industrial Systems and Control Ltd, Glasgow, in 1990. From 1991 to 2018, he was an intelligent systems Lecturer, Senior Lecturer, and Professor with University of Glasgow, Glasgow, and served as Founding Director of University of Glasgow Singapore, Singapore. He later served as the Founding Director of Dongguan Industry 4.0 Artificial Intelligence Laboratory, Dongguan, China, and of i4AI Ltd, London, U.K. He is currently Professor with Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China.,Since 1991, his research has been focused on computational artificial intelligence and its applications. 

Weijia Jia (IEEE Fellow)

Beijing Normal University


Biography: Weijia Jia is currently a Chair Professor and Director of Joint AI and Future Networking Research Institute of Beijing Normal University (BNU, Zhuhai) and United International College (UIC), Zhuhai, Guangdong, China. He also serves as the VP for Research at UIC China. Prior joining BNU/UIC, he served as the Deputy Director ofState Kay Laboratory of Internet of Things for Smart City at the University of Macau and Zhiyuan Chair Professor at the Shanghai Jiaotong University, PR China. He received BSc/MSc from Center South University, China in 82/84 and PhD from Polytechnic Faculty of Mons, Belgium in 93, respectively; all in computer science. For 93-95, he joined German National Research Center for Information Science (GMD) in Bonn (St. Augustine) as a research fellow. From 95-13, he worked in City University of Hong Kong as a professor. His contributions have been recoganized for the research of optimal network routing and deployment; vertex cover; anycast and multicast protocols; sensors networking; knowledge relation extractions; NLP and intelligent edge computing. He has over 600 publications in the prestige international journals/conferences and research books and book chapters. He hasreceived thebest product awards from the International Science & Tech. Expo (Shenzhen) in 2011/2012 and the 1st Prize of Scientific Research Awards from the Ministry of Education of China in 2017 (list 2) and many provincial science and tech awards. He has served as area editor for various prestige international journals, chair and PC member/keynote speaker for many top international conferences. He is the Fellow of IEEE and the Distinguished Member of CCF. 

Title: LLM Technologies and their Impacts on Task Layer Scheduling in Edge Computing

Abstract: This talk first introduces the technologies used by the large languarge model (LLM) and then compares them with the technologies we developped for the container and task scheduling in the edge computing. Before running the container in an edge server, an image composed of several task layers must be scheduled locally. However, it has been conspicuously neglected by existing work that task scheduling at the granularity of the layers instead of the image can significantly reduce the task completion time to meet the real-time requirement and resource efficiency in the resource-limited edge servers. This talk will further discuss our recent investigations on the online container and image/layer scheduling algorithms in the edge environments with similar AI approaches as compared with the popular technologies used LLM.

Juergen Branke

University of Warwick, UK

Juergen Branke.png

Biography: Juergen Branke is Professor of Operational Research and Systems at Warwick Business School, Vice-Chair of ACM SIGEVO. His research area lies at the interface of machine learning and optimisation and falls into the broader field of prescriptive analytics, i.e., how to use data and models to make optimal decisions. In particular, he works on methods such as metaheuristics and Bayesian optimisation to tackle problems that involve uncertainty, are dynamically changing over time, or involve multiple, conflicting objectives.

Professor Branke is the Editor-in-Chief of ACM Transactions on Evolutionary Learning and Optimization, Area Editor of the Journal of Heuristics and the Journal of Multi-Criteria Decision Analysis, and Associate Editor of the IEEE Transactions on Evolutionary Computation and the Evolutionary Computation Journal. He is the director of Warwick’s Data Science for Social Good UK chapter and Turing Fellow. He has published over 200 scientific papers and has extensive experience of working with industry in logistics, telecommunications, and engineering design.

Title: Machine Learning and Optimisation - a symbiosis

Abstract: This talk discusses the relationship between machine learning and optimisation. It demonstrates that many machine learning problems are actually optimisation problems, and could benefit from advances in operational research. On the other hand, the latest challenges in optimisation, such as parameter tuning, algorithm selection, hyper heuristics or handling of uncertainty are actually closely related to machine learning. Furthermore, recent algorithmic developments such as Bayesian Optimisation very much blur the boundary between machine learning and optimisation, as they explicitly combine learning about the search space with optimisation.