Keynote Speakers on ICCCBDA 2018 !

Prof. Xu Lei (IEEE Fellow)
Shanghai Jiaotong University, China

Lei Xu, Zhiyuan Chair Professor,Department of Computer Science and Engineering Shanghai Jiao Tong University (SJTU); Director, Centre for Cognitive Machines and Computational Health (CMaCH); Also, Professor of Computer Science and Engineering, Chinese University of Hong Kong (CUHK); a guest Professor of Institute of Biophysics, CAS; Completed Ph.D thesis at Tsinghua Univ by the end of 1986, joined Peking Univ as postdoc in 1987 and associate professor in 1988, became postdoc and visiting scientist in Finland, Canada and USA (Harvard and MIT) during 1989-93. Then, joined CUHK as senior lecturer in 1993, professor in 1996, and chair professor during 2002-16. He has published more than 350 papers, with more than 13000 citations (over 7400 by top-10 papers and 2574 by top-1) according to Google Scholar versus more than 5000 citations (over 3500 by top-10 papers and 1213 by top-1) according to Web of Science. Received several national and international academic awards, including 1993 National Nature Science Award, 1995 Leadership Award from International Neural Networks Society (INNS) and 2006 APNNA Outstanding Achievement Award. Elected to Fellow of IEEE in 2001; Fellow of intl. Association for Pattern Recognition in 2002 and of European Academy of Sciences (EAS) in 2002. Served as the EIC of Springer-Nature OA J. Applied Informatics, and associate editors of several academic journals, e.g., including Neural Networks (1995-2016), Neurocomputing (1995-present), IEEE Tr. Neural Networks (1994-98). Taken various roles in academic societies, e.g., INNS Governing Board (2001-03), the INNS award committee (2002-03), and the Fellow committee of IEEE Computational Intelligence society (06), and the EAS scientific committee (2014-17).

 

Prof. Guoyin Wang, Chongqing University of Posts and Telecommunications, China

Dr. Guoyin Wang received the bachelor’s degree in computer software, the master’s degree in computer software, and the Ph.D. degree in computer organization and architecture from Xi’an Jiaotong University, Xi’an, China, in 1992, 1994, and 1996, respectively. Since 1996, he has been working at the Chongqing University of Posts and Telecommunications, where he is currently a professor and PhD supervisor, the Director of the Chongqing Key Laboratory of Computational Intelligence, and the Director of the National International Cooperation Base of Big Data Intelligent Computing. He was named as a national excellent teacher and a national excellent university key teacher of China, in 2001 and 2002 respectively. He was elected as a talent of the Program for New Century Excellent Talents in University of China, in 2004, a National Level Talent of the New Century Hundred, Thousand and Ten Thousand Talents Project of China in 2009, a State Council Expert for Special Allowance in 2010, He was elected as a Science and Technology Innovation Talent of the National High-level Personnel of Special Support Program of China, and a Leading Expert of Chongqing Chief Expert Studio, in 2014. He was elected as a Chang Jiang Scholar by the Ministry of Educations, P. R. China, in 2014. The teaching group directed by Professor Wang was elected as a National Excellent Teaching Group of China in 2010. The institute (ICST) directed by Professor Wang was elected as one of the Top Ten Outstanding Youth Organizations of Chongqing, China, in 2002. The research team directed by Professor Wang was elected as an Innovation Team of Chongqing, China, in 2010. He is a Fellow and the Steering Committee Chair of International Rough Set Society (IRSS), a Vice-President of the Chinese Association for Artificial Intelligence (CAAI), a council member of the China Computer Federation (CCF), and a senior member of IEEE. He had served as the President of IRSS 2014-2016. He served or is currently serving on the program committees of many international conferences and workshops, as program committee member, program chair or co-chair. He is an editorial board member of several journals. The research interests of Professor Wang include big data, data mining, machine learning, rough set, granular computing, knowledge technology, soft computing, cognitive computing, etc. He is the author of over 10 books, the editor of dozens of proceedings of international and national conferences, and has over 200 reviewed research publications.
Speech Title: DGCC: Data-driven Granular Cognitive Computing
Abstract: Inspired by human’s granularity thinking and problem solving mechanism, the cognition law of ‘‘global precedence’’, the mechanism of unconditioned reflex neural signal processing, a new cognitive computing model, data-driven granular cognitive computing (DGCC), is introduced in this talk. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of ‘‘global precedence’’ which is a cognition process of ‘‘from coarser to finer’’ and the information processing mechanism of machine learning systems which is ‘‘from finer to coarser’’, in a multiple granularity space. The computation model of intelligent computation forwarding inspired by the mechanism of unconditioned reflex neural signal processing is also considered in DGCC. It is also based on the idea of data-driven. The research issues of DGCC to be further addressed are discussed. Based on DGCC, deep learning is neither classified into symbolism, nor connectionism. It is taken as a combination of symbolism and connectionism, and named hierarchical structuralism. The HD3 characteristics (hierarchical, distributed, data-driven, and dynamical) of the hierarchical structuralism are analyzed. DGCC provides a granular cognitive computing framework for efficient knowledge discovery from big data.

 

Prof. Domenico Talia, University of Calabria, Italy
Domenico Talia is a full professor of computer engineering at the University of Calabria, Italy. He is a partner of two startups, Exeura and DtoK Lab. His research interests include big data analysis, parallel and distributed data mining, cloud computing, social data analysis, mobile computing, peer-to-peer systems, and parallel programming.
Talia published ten books and more than 350 papers in archival journals such as CACM, Computer, IEEE TKDE, IEEE TSE, IEEE TSMC-B, IEEE Micro, ACM Computing Surveys, FGCS, Parallel Computing, IEEE Internet Computing and international conference proceedings. He is a member of the editorial boards of IEEE Transactions on Cloud Computing, the Future Generation Computer Systems journal, the International Journal on Web and Grid Services, the Scalable Computing: Practice and Experience journal, MultiAgent and Grid Systems: An International Journal, International Journal of Web and Grid Services, and the Web Intelligence and Agent Systems International journal.
Talia has been a project reviewer for several international institutions such as the European Commission, Aeres in France, Austrian Science Fund, Croucher Foundation, and the Russian Federation Government. He served as a chair, organizer, or program committee member of several international conferences and gave many invited talks, tutorials and seminars in conferences and schools. Talia is a member of the ACM and the IEEE Computer Society.
Speech Title: Exploiting Cloud Solutions for Big Data Analysis
Abstract:
Cloud computing platforms offer a scalable support for addressing both the computational and data storage needs of big data mining and parallel knowledge discovery applications. This talk addresses the main topics and research issues on efficiently using Cloud computing platforms for implementing big data mining applications on large data sets. We present data mining techniques and frameworks designed for developing distributed data analytics applications on Clouds. These systems implement data set storage, analysis tools, data mining algorithms and knowledge models as single services that are combined through a visual programming interface in distributed workflows. In particular, the talk outlines how to implement big data mining services on the Data Mining Cloud Framework, designed for developing and executing distributed data analytics applications as workflows of services. Application design and execution of data analysis use cases are presented. Programming issues and research trends will be also outlined.
The growing use of service-oriented computing is accelerating the use of cloud-based systems for scalable big data analysis. Developers and researchers are adopting the three main cloud models, software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS), to implement big data analytics solutions in the cloud. According to these approaches, data mining tasks and knowledge discovery applications are offered as high-level services available every time form everywhere. This methodology created a new way to delivery data analysis software that is called data analytics as a service (DAaaS). The talk discusses DAaaS methodology, data analysis workflows, and cloud-based data mining applications.

 

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 240 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). 3 papers were ESI Hot Papers and 12 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 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 (2013-2018), 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 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.
Speech Title: Data-Driven Intelligence: Challengues and our Solutions
Abstract:
Data-Driven Intelligence has become a hot research topic in the area of information science. This talk aims to outline the challengues on Data-Driven Intelligence. Then our 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 setbased 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  reimplementing the 
original data mining algorithm. 
6) A deep-learning-based model to deal with multiple different sources of data is developed.

 

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: Big Data Analytics and Variable Precision Model in Analyzing Information Systems
Abstract: We begin with mentioning some current data analytics trends to process information systems, which can be analyzed in many different ways. From the viewpoint of rough set theory, it is usually to represent a data set of an information system as a table. We focus on the decision tables that some attribute values are lost or incomplete. In this talk, we show how we pass from classical rough set theory to variable precision generalized rough set theory (VPGRS). We present several types of definability. We then present 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 introducing minimal neighborhood systems. We show how to determine the discernibility threshold for a reflexive relational decision system in the variable precision generalized rough set model. As applications, we present big data analytics methods in solving feature selection and big data reduction problems.

 

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 developed and widely adopted. To meet the tremendous demand of the cloud services, IaaS, PaaS, and SaaS, while the cloud service providers eagerly gain their market share, they also strive to maximize their profit through enhancing the cloud performance and optimizing their cloud services. Many optimization algorithms have been studied, developed, and applied for the cloud service optimization in the different cloud deploy models. This talk is to explore the optimization algorithms for Cloud Computing and Cloud Service Optimization applications.