报告时间：

2023年12月3日上午9点30

报告地点：

学院210会议室

报告题目：

Collaborative Learning and Optimisation

报告简介：Machine learning (ML) and optimisation are two essential missions that Computational Intelligence (CI) aims to address. Accordingly, many CI-based ML and optimisation techniques have been proposed, where deep neural networks (used for ML) and evolutionary algorithms (used for optimisation) are the most well-known representatives. Intrinsically, CI-based ML and optimisation are closely related. On the one hand, CI-based ML consists of various model-centric or data-centric optimisation tasks. On the other hand, CI-based optimisation is often formulated into ML-assisted search problems. In recent years, there emerges a new research direction in CI, namely Collaborative Learning and Optimisation (COLO), which studies how to synergise CI-based ML and optimisation techniques while unleashing the unprecedented computing power (e.g., via supercomputers) to generate more powerful ML and optimisation techniques for solving challenging problems. In this talk, I will first introduce CI, CI-based ML and optimisation techniques, and their relationships, and then describe COLO from three aspects, i.e., how to make use of ML techniques to assist optimisation (Learn4Opt), how to leverage optimisation techniques to facilitate ML (Opt4Learn), and how to synergise ML and optimisation techniques to deal with real-world problems which involve ML and optimisation as two indispensable and interwoven tasks (LearnOpt), where the most representative research hotspot in each of these three aspects, i.e., automated construction of deep neural networks, data-driven evolution optimisation, and predictive optimisation will be discussed.

报告人简介：

Kai Qin is a Professor at Swinburne University of Technology, Melbourne, Australia. Currently, he is the Director of Swinburne Intelligent Data Analytics Lab and the Deputy Director of Swinburne Space Technology and Industry Institute. Before joining Swinburne, he worked at Nanyang Technological University (Singapore), the University of Waterloo (Canada), INRIA Grenoble Rhône-Alpes (France), and RMIT University (Australia). His major research interests include machine learning, evolutionary computation, collaborative learning and optimization, computer vision, remote sensing, services computing, and edge computing. He was a recipient of the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award and the 2022 IEEE Transactions on Neural Networks and Learning Systems Outstanding Associate Editor. He is currently the Chair of the IEEE Computational Intelligence Society (CIS) Student Activities and Young Professionals Sub-committee, the Vice-Chair of the IEEE CIS Neural Networks Technical Committee, the Vice-Chair of the IEEE CIS Emergent Technologies Task Force on “Multitask Learning and Multitask Optimization”, the Vice-Chair of the IEEE CIS Neural Networks Task Force on “Deep Edge Intelligence, and the Chair of the IEEE CIS Neural Networks Task Force on “Deep Vision in Space”. He serves as the Associate Editor for several top-tier journals, e.g., IEEE TEVC, IEEE TNNLS, IEEE CIM, IEEE TCDS, NNs and SWEVO. He was the General Co-Chair of the 2022 IEEE International Joint Conference on Neural Networks (IJCNN 2022) held in Padua, Italy, and was the Chair of the IEEE CIS Neural Networks Technical Committee during the 2021-2022 term.