報(bào)告題目：Analysis and Learning of Fuzzy Systems and Function Data, and Pricing Decision Making in RevenueManagement
時(shí)間：2023年10月22日，下午16:00
地點(diǎn)：偉德國(guó)際1946bv官網(wǎng)主樓418報(bào)告廳
報(bào)告人：英國(guó)曼徹斯特大學(xué) Xiao-Jun Zeng 教授
報(bào)告人簡(jiǎn)介：
Dr Xiao-Jun Zeng is a Professor of Machine Learning in the Department of Computer Science at the University of Manchester. Before joining to the University of Manchester, he was with Knowledge Support Systems (KSS) Ltd, UK between 1996-2002 as a scientific developer, senior scientific developer, and Head of Research, researching and developing intelligent pricing decision support systems for retail, petroleum, telecommunication, and finance industries.
Prof. Zeng’s main research areas include computational intelligence, machine learning, decision support systems, and their applications in retail, finance, and energy industries. His research has been funded UK EPSRC, Innovate UK, EU 6th and 7th Framework Programmes, and EU H2020 Programme, and has published more than 200 international journal and conference papers in these areas with more than 14400 citations based on Google Scholar.
Prof. Zeng is an Associate Editor of the IEEE Transactions on Fuzzy Systems as well as several other journals. He received the European Information Society Technologies Award in 1999 and the Microsoft European Retail Application Developer Awards in 2001 and 2003 with KSS Ltd. His research in intelligent pricing decision support systems was recognized and selected by UK Computing Research Committee, Council of Professors and Heads of Computing, and British Computer Society Academy as one of 20 impact cases to highlight the impact made by UK academic Computer Science Research within the UK and worldwide over the period 2008 – 2013.
報(bào)告內(nèi)容簡(jiǎn)介：
The 1st aspect of this talk will be on the analysis and learning fuzzy systems, including the representation and analysis of fuzzy systems, and learning algorithms of fuzzy systems from data; The 2nd aspect of the talk will be the functional data analysis, including the machine learning theory and their learning algorithm for functional data. This is an area which is less well developed area in machine learning but has a great progress potential; The 3rd aspect of the talk will be about the pricing optimization and decision support systems in Revenue Management with its applications in petroleum, retail, and energy pricing.
（承辦：管理工程系、科研與學(xué)術(shù)交流中心）
