Dr. Li Xiaoli 
Department Head (Machine Intellection)

Research areas: 
Deep Learning, Positive unlabelled learning, graph/network data mining, text analytics, recommendation system, Sensor data analytics for equipment health diagnostic and prognostic

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, Nanyang Technological University. 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. 

His research interests include AI, data mining, machine learning, and bioinformatics.  He has been serving as the Chair of many 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 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 1,500 citations (his top AI IJCAI 2015 paper has been cited by around 900 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 250 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 48) and won eight best paper awards.