The 3rd International Conference on Artificial Intelligence and Computer Engineering(ICAICE 2022)
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Prof. Xiaoli Li

Prof. Xiaoli Li

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Prof. Xiaoli Li

 School of Computer Science and Engineering, Nanyang Technological University, Singapore

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Speech Title: AI R&D research in equipment health monitoring 


Abstract

Various sensors have been widely deployed to support equipment condition monitoring tasks for different industry verticals, including manufacturing, aerospace, semi-conductor, healthcare etc. Developing innovative AI solutions for analysing the collected time-series sensor data is critical for increasing equipment availability and efficiency, effective maintenance decision-making and smart system control. In this talk, we will share some of recent AI research effort to design innovative deep learning technologies to address various real-world challenges for equipment diagnostic, machine remaining useful life prediction.


Biography

Dr. Li Xiaoli is the Department Head and Principal Scientist of the Machine Intellection (MI) department at the Institute for Infocomm Research (I2R), A*STAR, Singapore. He also holds adjunct full professor position at School of Computer Science and Engineering, NTU. He has been a member of ITSC (Information Technology Standards Committee) from ESG Singapore since 2020 and has served as joint lab directors with a few major industry partners (DBS, KPMG, Mclaren, NEC). His research interests include AI, data mining, machine learning, and bioinformatics.  He has been serving as the Chair of leading AI/data mining/machine learning related conferences & workshops (including KDD, ICDM, SDM, PKDD/ECML, ACML, PAKDD, WWW, IJCAI, AAAI, ACL, and CIKM). He currently serves as editor-in-chief of Annual Review of Artificial Intelligence, and associate editor of IEEE Transactions on Artificial Intelligence and Machine Learning with Applications (Elsevier). 
Dr Li is a pioneer researcher in the following two domains: 
1.  Positive Unlabeled learning, with more than 2,000 citations and the term Positive Unlabeled Learning was coined in his paper
2.  AI based time series sensor data analytics for equipment health monitoring, with more than 2,000 citations (his top AI IJCAI 2015 paper has been cited by around 1000 times). 

He was one of the first researchers to formulate the sensor feature learning problem using deep neural networks. 

He led his team to win various top AI and data analytics international benchmark competitions and works closely with government agencies and industry partners across different verticals, e.g., bank and insurance, healthcare, aerospace, telecom, audit firm, transportation etc, to create social and economic impact.
Dr Li has published more than 270 peer-reviewed papers in top AI, Data Mining, Machine Learning and Bioinformatics conferences and journals with more than 13,000 citations (more than 2,000 annual citations in recent years; H-index 54) and won eight best paper awards.