Prof. CAO JIANNONG, The Hong Kong Polytechnic University, Hong Kong
Fellow of IEEE (Computer Society)
Distinguished Member of ACM
Senior Member of CCF
BSc (Nanjing University)
MSc, Ph.D. (Washington State University)
Dr. Cao is currently a Chair Professor of Department of Computing at The Hong Kong Polytechnic University, Hong Kong. He is also the director of the Internet and Mobile Computing Lab in the department and the director of University's Research Facility in Big Data Analytics. His research interests include parallel and distributed computing, wireless sensing and networks, pervasive and mobile computing, and big data and cloud computing. He has co-authored 5 books, co-edited 9 books, and published over 500 papers in major international journals and conference proceedings. He received Best Paper Awards from conferences including DSAA'2017, IEEE SMARTCOMP 2016, ISPA 2013, IEEE WCNC 2011, etc. Dr. Cao served the Chair of the Technical Committee on Distributed Computing of IEEE Computer Society 2012-2014, a member of IEEE Fellows Evaluation Committee of the Computer Society and the Reliability Society, a member of IEEE Computer Society Education Awards Selection Committee, a member of IEEE Communications Society Awards Committee, and a member of Steering Committee of IEEE Transactions on Mobile Computing. Dr. Cao has also served as chairs and members of organizing and technical committees of many international conferences, and as associate editor and member of the editorial boards of many international journals. Dr. Cao is a fellow of IEEE and ACM distinguished member. In 2017, he received the Overseas Outstanding Contribution Award from China Computer Federation.
Speech Title: Cross-Domain Big Data Fusion and Analytics
Abstract: Big data analytics using cross-domain multi-source datasets allow us to study the phenomena of our interest by fusing views from multiple angles, facilitating us to identify meaningful problems and discover new insights. However, we need methods and techniques to solve the challenges like heterogeneity, uncertainty and high dimensionality in analyzing cross-domain datasets. In this talk, I will describe a general framework of cross-domain big data analytics and share our work of fusing and analyzing datasets from multiple domains to uncover the underlying patterns, correlations and interactions. Example applications include human and urban dynamics like predicting traffic congestions, optimize demand dispatching in emerging on-demand services, and designing wireless networks.
Prof. Tianrui Li, Southwest Jiaotong University, China
教授/主任 李天瑞 四川省云计算与智能技术高校重点实验室/西南交通大学
Tianrui Li received his B.S. degree, M.S. degree and Ph.D. degree from the Southwest Jiaotong University, China in 1992, 1995 and 2002 respectively. He was a Post-Doctoral Researcher at Belgian Nuclear Research Centre (SCK•CEN), Belgium from 2005-2006, a visiting professor at Hasselt University, Belgium in 2008, the University of Technology, Sydney, Australia in 2009 and the University of Regina, Canada in 2014. And, he is presently a Professor and the Director of the Key Lab of Cloud Computing and Intelligent Technique of Sichuan Province, Southwest Jiaotong University, China. Since 2000, he has co-edited 6 books, 10 special issues of international journals, 15 proceedings, received 5 Chinese invention patents and published over 200 research papers (e.g.,IEEE TKDE, IEEE TEC, IEEE TFS, IEEE TIFS, IEEE ASLP, IEEE TIE, IEEE TC, IEEE TVT) in refereed journals and conferences (e.g., KDD, IJCAI, UbiComp). Three papers were ESI Hot Papers and fourteen papers was ESI Highly Cited Papers. His Google H-index is 32. He serves as the area editor of International Journal of Computational Intelligence Systems (SCI), editor of Knowledge-based Systems (SCI) and Information Fusion (SCI), etc. He has served as IEEE ICCC2015-2018 chairs, ISKE2007-2018, CRSSC2015, CWI2014, JRS2012 program chairs, IEEE HPCC 2015, GrC 2009 program vice chairs and RSKT2008, FLINS2010 organizing chairs, etc. and has been a reviewer for several leading academic journals. He is an IRSS fellow, a distinguished member of CCF, a senior member of ACM, IEEE, CAAI, ACM SIGKDD member, Chair of IEEE CIS Chengdu Chapter (2013-2017), Treasurer of ACM SIGKDD China Chapter and CCF YOCSEF Chengdu Chair (2013-2014). Over fifty graduate students (including 8 Post-Docs, 13 Doctors) have been trained. Their employment units include Microsoft Research Asia, Sichuan University, Baidu, Alibaba, Tencent and Huawei. They have received 2 "Si Shi Yang Hua" Medals, Best Papers/Dissertation Awards 14 times, Champion of Sina Weibo Interaction-prediction at Tianchi Big Data Competition (Bonus 200,000 RMB), Second Place of Social Influence Analysis Contest of IJCAI-2016 Competitions.
Speech Title: Big Data Analysis and Our Solutions
Abstract: Exploring efficient and effective knowledge discovery approaches to manage Big Data with rich information has become a hot research topic in the area of information science. This talk aims to show our recent work on big data analysis and our solutions. It covers the following aspects. 1) A hierarchical entropy-based approach is demonstrated to evaluate the effectiveness of data collection, the first step of knowledge discovery from data. 2) A multi-view-based method is illustrated for filling missing data since it is very common phenomenon in Big Data due to communication or device errors, etc. 3) A unified framework is outlined for Parallel Large-scale Attribute Reduction, termed PLAR, to manage Big Data with high dimension. 4) A MapReduce-based parallel method together with three parallel strategies are presented for computing rough set approximations, which is a fundamental part in rough set-based data analysis similar to frequent pattern mining in association rules. 5) Incremental learning-based approaches are shown for updating approximations and knowledge in dynamic data environments, e.g., the variation of objects, attributes or attribute values, which improve the computational efficiency by using previously acquired learning results to facilitate knowledge maintenance without re-implementing the original data mining algorithm. 6) A composite rough set model to deal with multiple different types of attributes is developed, which provides a novel approach for complex data fusion. 7) The uncertainty information processing under three-way decisions for the veracity of data is discussed.
Prof. Yan Yang, Southwest Jiaotong University, China
副院长/教授 杨燕 西南交通大学信息科学与技术学院
PhD, IEEE/ACM Member
Vice Dean of School of Information Science & Technology
Vice Chair of ACM Chengdu Chapter
Dr. Yan Yang is currently Professor and vice dean of Information Science and Technology, Southwest Jiaotong University. She worked as a visiting scholar at the Center of Pattern Analysis and Machine Intelligence (CPAMI) in Waterloo University of Canada for one and half year. She is an Academic and Technical Leader of Sichuan Province. Her research interests include artificial intelligence, big data analysis and mining, ensemble learning, cloud computing and service. Prof. Yang has participated in more than 10 high-level projects recently. And have taken charge of two programs supported by the National Natural Science Foundation of China (NSFC), one NSFC International (Regional) Cooperation and Exchanges program, one Project of National Science and Technology Support Program, and one Supporting Program for Science and Technology of Sichuan Province. She has authored and co-authored over 150 papers in journals and international conference proceedings, 1 special issue of international journal, 1 proceeding and 2 books. She also serves as the Vice Chair of ACM Chengdu Chapter, a distinguished member of CCF, a senior member of CAAI, a member of IEEE, ACM, ACM SIGKDD, CCF Education Work, CCF Artificial Intelligence and Pattern Recognition, CCF Theoretical Computer Science, CAAI Machine Learning, CAAI Grain Calculation and Knowledge Discovery Committee, Deputy Secretary General of Sichuan Computer Society and Vice Chair of Big Data Industry University Research Council of Sichuan Institute of Electronics.
Speech Title: Data-driven Condition Recognition of HST
Abstract: Real time monitoring of the running status of train and detecting their hidden failures accurately have great significance. Train bogie is an important part to guarantee the safe operation of High Speed Train (HST) and the comfort of passengers. The main techniques for recognizing conditions of HST are to collect the vibration signals by mounting sensors, analyze data features and build fault diagnosis model. Deep learning, ensemble learning and multi-view learning have attracted considerable attention in recent years. In this talk, I will discuss condition recognition of HST with Deep Belief Networks (DBNs), Empirical Mode Decomposition (EMD), Multi-view Clustering Ensemble, Multi-view Classification Ensemble, Feature Fusion, and etc.