Keynote Speakers


Prof. En-Bing Lin, Central Michigan University, USA
Dr. En-Bing Lin is Chair and Professor of Mathematics at Central Michigan University, USA. He has taught and visited at several institutions including Massachusetts Institute of Technology, University of Wisconsin-Milwaukee, University of California, Riverside, University of Toledo, UCLA, and University of Illinois at Chicago. He received his Ph. D. in Mathematics from Johns Hopkins University. His research interests include Data Analysis, Image Processing, Applied and Computational Mathematics, Wavelet Analysis and Applications, and Mathematical Physics. He has supervised a number of graduate and undergraduate students. Dr. Lin serves on the editorial boards of several mathematics journals and several academic committees of regional and national associations. He has organized several special sessions at regional IEEE conference and American Mathematical Society national and regional meetings.

Speech Title: Generalized Fuzzy Rough Approximations in Analyzing Large Scale Information Systems
Abstract: We begin with a brief overview of current trends of big data analytics to process information systems.  As a powerful artificial intelligence tool, rough set approach is of fundamental importance. In this talk, we represent a data set of an information system as a table and show how we pass from classical rough set theory to variable precision generalized rough set theory (VPGRS). We recall the connection between the concepts of VPGRS-model and neighborhood systems through binary relations. We provide characterizations of lower and upper approximations for VPGRS-model by using minimal neighborhood systems. Fuzzy rough set theory extends rough set theory via a general approach to the fuzzification of rough sets. We develop a generalized fuzzy rough approximation by incorporating VPGRS with fuzzy rough sets and show how to determine the discernibility threshold for a reflexive relational decision system in the variable precision generalized fuzzy rough set model. As applications, we propose some parallel distributed computations to analyze the systems.



Prof. Tianrui Li, Southwest Jiaotong University, China
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 300 research papers (e.g., AI, 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, WWW, UbiComp). 3 papers were ESI Hot Papers and 12 papers was ESI Highly Cited Papers. His Google H-index is 37. 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 is an IRSS fellow, a distinguished member of CCF, a senior member of ACM, IEEE and CAAI, ACM SIGKDD member, Chair of IEEE CIS Chengdu Chapter (2013-2018), Treasurer of ACM SIGKDD China Chapter and CCF YOCSEF Chengdu Chair (2013-2014). Over fifty graduate students (including 8 Post-Docs, 14 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 15 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 and Second Place of Weather forecast Contest of AI Challenger 2018..
Speech Title: Data-Driven Intelligence: Challengues and our Solutions
 Data-Driven Intelligence has become a hot research topic in the area of information science. This talk firstly outlines the challengues on Data-Driven Intelligence. Then our recent solutions for Data-Driven Intelligence are provided, which cover the following aspects: 1) A hierarchical entropy-based approach is demonstrated to evaluate the effectiveness of data collection, the first step of Data-Driven Intelligence; 2) A multi-view-based method is illustrated for filling missing data, the preprocessing step for Data-Driven Intelligence; 3) A unified framework is outlined for Parallel Large-scale Feature Selection to manage Big Data with high dimension; 4) A MapReduce-based parallel method together with three parallel strategies are presented to compute rough set approximations for classification, 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) Deep-learning-based models to fuse multiple different sources of data are developed; 7) Several typical applications on natural language processing, high speed train and urban computing, etc. are shown.




Assoc. Prof. Qing Tan,
School of Computing and Information Systems, Athabasca University, Canada

Dr. Qing Tan is an associate professor in School of Computing and Information Systems at Athabasca University, Canada. He was born and raised in Chengdu. He left his beloved hometown in 1977 to study Aviation Automation at the Northwest Polytechnic University. He earned his PhD in Cybernetics Engineering for Robotics from the Norwegian Institute of Technology (NTNU - Norwegian University of Science and Technology) in 1993. As a foreign senior research fellow, he did the research on Telepresence Robot for the human acts simulation program at the Japan Atomic Energy Research Institute in 1994. He did his post-doctorial fellowship at University of Alberta in 1996. He joined Athabasca University in 2007 with extensive IT industrial working experiences in Canada. Dr. Tan is teaching and developing both undergraduate and graduate courses including Mobile Computing, Computer Networking, E-Commerce, Enterprise Modeling, Cloud Computing, and Big Data Analytics. Dr. Tan’s research interests and engagements include Location-Based Technologies, Mobile Computing, Adaptive Mobile Learning, Telepresence Robot, Cloud Computing, Internet of Things, Big Data Analytics, Cyber-Physical Systems, and Computer Network and Cyber Security. Dr. Tan received several Canadian national and provincial research grants. He has published many research papers on International journals and conferences. He also sits on many international journal editor boards and various conference committees.
Speech Title: Optimization Algorithms for Cloud Computing
Abstract: Cloud Computing as an effective and novel paradigm of computing technology has been rapidly advanced and widely adopted. To meet the tremendous demand for cloud services, Cloud Service Providers (CSPs) strive to gain their market share and maximize their profit through enhancing cloud performance and optimizing their cloud services. Many optimization algorithms have been studied, developed, and applied for cloud service optimization in different cloud deployment models. This speech will explore optimization algorithms and applications for Cloud Computing with a focus on Swarm Intelligence.