ICMLT 2024 Keynote Speaker
Assoc. Prof. Daoyi Dong (IEEE Fellow), Australian National University, Australia
Daoyi Dong is currently a Professor at the Australian National University. Before moving to the Australian National University, he had worked at the University of New South Wales, Australia for 15 years. He was with the Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Zhejiang University. He had/has visiting positions at Princeton University, USA, RIKEN, Japan, the University of Hong Kong, Hong Kong, University of Duisburg-Essen, Germany, the University of Sydney, and the University of Melbourne, Australia. He received a B.E. degree in automatic control and a Ph.D. degree in engineering from the University of Science and Technology of China, in 2001 and 2006, respectively.
His research interests include machine learning, quantum control, system identification and renewable energy. He was awarded an ACA Temasek Young Educator Award by the Asian Control Association and is a recipient of a Future Fellowship, an International Collaboration Award, a Discovery International Award and an Australian Post-Doctoral Fellowship from the Australian Research Council, a Humboldt Research Fellowship from the Alexander von Humboldt Foundation in Germany, and a Scientia Fellowship from the University of New South Wales.
He currently serves as an Associate Editor of IEEE Transactions on Cybernetics and IEEE/CAA Journal of Automatica Sinica, and a Technical Editor of IEEE/ASME Transactions on Mechatronics. He was an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, and a Guest Editor of Annual Reviews in Control. He is a Fellow of the IEEE.
Prof. Xiao-Zhi Gao, University of Eastern Finland, Finland
Tentative Speech Title: An Introduction to Nature-inspired Computing Methods and Applications
Xiao-Zhi Gao received his B.Sc. and M.Sc. degrees from the Harbin Institute of Technology, China in 1993 and 1996, respectively. He obtained his D.Sc. (Tech.) degree from the Helsinki University of Technology (now Aalto University), Finland in 1999. He has been working as a professor at the University of Eastern Finland, Finland since 2018. Prof. Gao has published more than 500 technical papers in refereed journals and international conferences. His current Google Scholar H-index is 42. Prof. Gao’s research interests are nature-inspired computing methods with their applications in optimization, data mining, machine learning, control, signal processing, and industrial electronics.
Prof. Jerry Chun-Wei Lin (FIET, ACM Distinguished Member, IEEE Senior Member), Western Norway University of Applied Sciences, Norway
Jerry Chun-Wei Lin is currently working as the full Professor at the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. He has many papers in IEEE/ACM journals and international conferences. His research interests include data mining and analytics, soft computing, deep learning/machine learning, optimization, IoT applications, and privacy-preserving and security technologies. He is the Editor-in-Chief of Data Science and Pattern Recognition (DSPR) journal, Associate Editor/Editor for 14 SCI journals including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Dependable and Secure Computing, Information Sciences, among others. He has served as the Guest Editor for 50+ SCI journals. He is the leader of the well-known SPMF project, which provides more than 190 data mining algorithms and has been widely cited in many different applications. He has been awarded as the Most Cited Chinese Researcher in 2018, 2019, and 2020 by Elsevier/Scopus and Top-2% Scientist in 2019, 2020, 2021, and 2022 respectively by Stanford University report. He is the Fellow of IET (FIET), ACM Distinguished Scientist, and IEEE Senior Member.
Speech Title: Intelligent Utility-Driven Data Analysis
Abstract: As a large amount of data is collected daily from individuals, businesses, and other organizations or applications, various algorithms have been developed to identify interesting and useful patterns in data that meet a set of requirements specified by a user. The main purpose of data analysis and data mining is to find new, potentially useful patterns that can be used in real-world applications. For example, analyzing customer transactions in a retail store can reveal interesting patterns about customer buying behavior that can then be used for decision making. In recent years, the demand for utility-oriented pattern mining and analytics has increased because it can discover more useful and interesting information than basic binary-based pattern mining approaches, which has been used in many domains and applications, e.g., cross-marketing, e-commerce, finance, medical and biomedical applications. In this talk, I will first highlight the benefits by using the utility-oriented pattern mining and analytics compared to the past studies (e.g., association rule/frequent itemset mining). I will then provide a general overview of the state of the art in utility-oriented pattern mining and analytic techniques according to three main categories (i.e., data level, constraint level, and application level). Several techniques and modeling on different aspects (levels) of utility-oriented pattern mining will be presented and reviewed.