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

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.

Speech Title: An Introduction to Nature-inspired Computing Methods and Applications

Abstract: Nature-Inspired Computing (NIC) methods draw inspiration from the natural world. They encompass a large variety of approaches based on the principles and mechanisms found in physical, chemical, biological, and social systems. In this talk, the underlying working principles of a few NIC algorithms are introduced. Some typical applications of these techniques are also presented and discussed.

Prof. Jianhua Zhang, Oslo Metropolitan University, Norway

Jianhua Zhang is currently a Professor of Computer Science at Oslo Metropolitan University, Norway. His recent research interests are in the fields of computational intelligence, machine learning, intelligent systems and control, biomedical signal processing, and neurocomputing. In those fields he has published 4 books, 11 book chapters, and around 200 peer-reviewed journal and conference papers.
Dr Zhang served as Chair of IFAC (International Federation of Automatic Control) Technical Committee on Human-Machine Systems for two consecutive terms (2017-2023) and Vice Chair of IEEE Norway Section (2019-2023). He currently serves as Vice Chair of IFAC Technical Committee on Human-Machine Systems and Vice Chair for IEEE CIS (Computational Intelligence Society) Norway Chapter.
Dr Zhang is on editorial board of four international journals, including Frontiers in Neuroscience, Cognitive Neurodynamics (Springer), and Cognition, Technology & Work (Springer). He served as IPC Co-Chair for IFAC LSS 2013 (Shanghai) and HMS 2016 (Kyoto), and IPC Chair for IFAC HMS 2019 (Tallinn) and HMS 2022 (San Jose). He was also a keynote speaker or chair for a number of other international scientific conferences.

Speech Title: Forecasting Household Energy Consumption based on Deep Neuroevolution Models

Abstract: Accurate energy consumption prediction provides the basis for informed decisions on energy purchase and generation, as it prevents overloading and enables energy storage in a more efficient way. In this work, we construct a new deep learning model to predict the household energy consumption. We employ differential evolution (DE) algorithm to determine the optimal architecture of the deep neural network model. Finally, we present and analyze the real-life data analysis results to show the effectiveness of the deep neuroevolution models for energy use prediction problem under investigation.


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