Prof. En Bing Lin
Wentworth Institute of Technology, Boston MA, USA
En-Bing Lin is a Professor of Applied Mathematics and Associate Dean, School of Computing and Data Science at Wentworth Institute of Technology, Boston, Massachusetts, USA. He has been associated with several institutions including Massachusetts Institute of Technology, University of Wisconsin-Milwaukee, University of California, Riverside, University of Toledo, Central Michigan University, UCLA, and University of Illinois at Chicago. He has received his PhD from Johns Hopkins University. His research interests include Data Analysis, Applied and Computational Mathematics, and Mathematical Physics. He has Supervised a number of graduate and undergraduate students. He serves on the Editorial Boards of several journals. He has organized several special sessions at regional IEEE conferences and many other professional meetings.The interplay of Applied Mathematics, Machine Learning and Granular Computing
We begin with a brief overview of the overlaps of Applied Mathematics, Machine Learning and Granular Computing. Specifically, we present several projects to illustrate the overlaps. In fact, as a powerful artificial intelligence tool, rough set theory (RST) is of fundamental importance in dealing with many aspects of processing information systems and problem solving in big data analytics. With granular computing, we incorporate RST and fuzzy set theory to establish theory and analysis for a given set and its attributes as well as topological and computational analysis through a formal context. On the other hand, based on the above concepts, we identify some inconsistent points and errors of classical model of approximations. Predictive intelligence provides informed decision, data analytics and forecasting, therefore, it helps solve problems and uncover new opportunities. We incorporate neural network method and wavelet analysis to detect time series in time-frequency domain. It is found that neurowavelet methods produce better results in predictive intelligence than other methods and better capture the dynamics of the time series.
Prof. Chua-Chin Wang
National Sun Yat-Sen University, Taiwan
Professor Chua-Chin Wang became Vice President for Research and Development at National Sun Yat-sen University in August, 2021. He is in charge of managing the academic and innovative research activities. Vice President Wang received his Ph.D. degree on Electrical Engineering from State University of New York at Stony Brook, USA in 1992. His specialties are VLSI Design, Communication Interface Circuit Design, Power electronics, and the current research topics focus on AI and underwater vehicles. He joined Department of Electrical Engineering at NSYSU in 1992 and as its Chairman from 2009 to 2012. His outstanding research performance made him assigned as the Vice President for Industry-Academe Collaboration and Advancement from 2012 to 2015. He was honorably elected as the Dean of College of Engineering during the period of time from 2014 to 2017. He became Director General of Underwater Vehicle R&D Center since 2018. He is also the Principal Investigator of the VLSI Design Laboratory and the Academia Research Center of Underwater Vehicles (ARCUV).Development of AI for Underwater Applications
The investigation is aimed at development of an under-water AUV (autonomous unman vehicle) with artificial intelligence (AI) based on those marine technology currently available in NSYSU (National Sun Yat-Sen University), Taiwan. The AUV equipped with an AI platform is expected to integrate cross-domain technologies, including underwater objection recognition and tracking, real-time navigation control and positioning, battery power management system, LED enhancement, etc., besides object recognition. The developed AUV with AI will be able to carry out many missions, including under-water inspection, seabed exploration, and perhaps specific object tracking after it is fully tested. Last but not least, a cost-effective dataset augmentation method for underwater object recognition is also addressed.
Prof. Xiaoli Li
Institute for Infocomm Research (I2R), A*STAR, Singapore
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).
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 53) and won eight best paper awards.
In this talk, Dr Li will introduce a couple of machine learning techniques, including time series data learning, positive unlabelled learning and graph learning. He will focus on presenting how to develop innovative AI solutions for analysing the real-time time-series sensor data, which is critical for diverse applications for equipment diagnostic, machine remaining useful life prediction to address some real-world challenges such as high prediction accuracy, model compression for edge computing, domain adaptation issues. In addition, he will present some of the real-world applications that his team is building by leveraging these techniques.