Keynote Speech



Zhi-Hua Zhou
Professor
Keynote Title:

TBA

Abstract:

TBA

Brief Biography:

Zhi-Hua Zhou is a Professor and Founding Director of the LAMDA Group at Nanjing University, China. His main research interests are in artificial intelligence, machine learning and data mining. He authored the book "Ensemble Methods: Foundations and Algorithms", and published more than 100 papers in top-tier international journals and conference proceedings. His work have received more than 23,000 citations, with a h-index of 75. He also holds 18 patents and has good experiences in industrial collaborations. He has received various awards, including the National Natural Science Award of China (premium science award in China), the PAKDD Distinguished Contribution Award, the Microsoft Professorship Award, 12 international paper/ competition awards, etc. He serves as the Executive Editor-in-Chief of Frontiers of Computer Science, Associate Editor-in-Chief of Science China, and Associate Editor of ACM TIST, IEEE TNNLS, etc. He founded ACML (Asian Conference on Machine Learning) and served as General co-chair of IEEE ICDM 2016, Program co-chair IJCAI 2015 Machine Learning track, etc. He also serves as the Chair of the IEEE CIS Data Mining and Big Data Analytics Technical Committee, the CCF Artificial Intelligence Technical Committee, etc. He is a Fellow of the AAAI, AAAS, IEEE, IAPR, IET/IEE and CCF, and ACM.



Geoff Webb
Professor
Keynote Title:

Learning from non-stationary distributions

Abstract:

The world is dynamic – in a constant state of flux – but most learned models are static. Models learned from historical data are likely to decline in accuracy over time. This talk presents theoretical tools for analyzing non-stationary distributions and some insights that they provide. Shortcomings of standard approaches to learning from non-stationary distributions are discussed together with strategies for developing more effective techniques.

Brief Biography:

Geoff Webb is a Professor of Information Technology Research in the Faculty of Information Technology at Monash University, where he heads the Centre for Data Science. His primary research areas are machine learning, data mining, user modelling and computational structural biology. Many of his learning algorithms are included in the widely-used Weka machine learning workbench and a commercial implementation of his association discovery techniques, Magnum Opus, is widely used. He is co-editor of the Springer Encyclopedia of Machine Learning, a member of the advisory board of Statistical Analysis and Data Mining, a member of the editorial board of Machine Learning, was editor-in-chief of Data Mining and Knowledge Discovery and was a foundation member of the editorial board of ACM Transactions on Knowledge Discovery from Data. He is PC Co-Chair of the 2015 ACM SIGKDD International Conference on Knowledge Discovery from Data, was PC Co-Chair of the 2010 IEEE International Conference on Data Mining and General Co-Chair of the 2012 IEEE International Conference on Data Mining. He is a technical advisor to BigML. He is an IEEE Fellow and has received the 2013 IEEE ICDM Service Award, the 2014 Australian Research Council Discovery Outstanding Researcher Award, the 2016 Australian Computer Society's ICT Researcher of the Year Award, and the 2016 Australasian Artificial Intelligence Distinguished Research Contributions Award.