Keynote Speakers

Keynote 1

Big Data Analytics through Efficient Modeling and Visualization

Dr. Ming Dong
Associate Professor,
Department of Computer Science
Wayne State University, USA

Today digital data are accumulated at the faster than ever speed in science, engineering, biomedicine, and real-world sensing. The term of Big Data refers to large, diverse, complex, longitudinal, and/or distributed data sets, which has raised new research challenges in data analytics: they are tremendous in size; they come unstructured with heterogeneous features; they are intrinsically dynamic in nature, and knowledge is usually sparsely encoded in large amounts of noisy. This talk presents our recent efforts addressing some of these challenges by incorporating low-rank, sparse approximations into matrix decomposition-based clustering techniques. Specifically, (1) we developed EMD: Exemplar-based low-rank sparse Matrix Decomposition method to fast analyze large-scale datasets; (2) we developed ECKF, a general framework for Evolutionary Clustering large-scale data based on low-rank Kernel matrix Factorization; and (3) we developed a novel technique, Exemplar-based Visualization (EV), to interactively visualize extremely large datasets through efficient parameter embedding. Applications of our techniques in text mining, image segmentation and video analysis will also be discussed.

Ming Dong received his B. S. degrees in electrical engineering and industrial management engineering from Shanghai Jiao Tong University, Shanghai, China, in 1995. He received his Ph. D degree in electrical engineering from University of Cincinnati in 2001. He is currently an associate professor in the Department Computer Science and the director of Machine Vision and Pattern Recognition Laboratory at Wayne State University. Dr. Dong's areas of research include pattern recognition, data mining, and multimedia content analysis. His research is funded by National Science Foundation, State of Michigan, and Industries (e.g., APB Investment, Ford Motor Company). He has published over 100 technical articles in premium journals and conferences in related fields, e.g., TMM, TPAMI, TKDE, TNN, TVCG, TC, IEEE CVPR, IEEE InfoVis, ACM MM and WWW, and received over 1,500 citations until now. He was an associate editor of IEEE Transactions on Neural Networks (2008-2011) and Pattern Analysis and Applications (2007-2010), and served in many conference program committees and US National Science Foundation panels. He also served as senior research consultant in Baidu Inc. and Ford Motor Company, and has given over twenty keynotes or invited talks in various institutes.

Keynote 2

Agent-Oriented Collective Decision Making

Dr. Oscar Lin
Professor and Chair
School of Computing and Information Systems
Athabasca University, Canada

Agent-oriented modeling is a new conceptual model for developing software systems that are open, intelligent, and adaptive. Agents are ideally suited for modeling real people or organizations¨C they are active and social, similar to the way people are. A multi-agent system (MAS) is well suited to domains where virtual entities, called agents, are self-directed and can actively pursue their goals within an environment that they can interact with, including interactions with other agents that are also in pursuit of their own goals. Through monitoring, communication, and coordination, MAS technologies not only address real-world problems in a human-like way but to transcend human performance. This has had a transformative impact on many application domains, such as e-commerce, logistics, manufacturing, robotics, entertainment, e-learning, and health-care.

This talk presents what my research team has done over the past several years and what we are doing now applying agent-oriented modeling approaches to solve collective decision-making problems e.g. Resource Management and Well Scheduling in oil and gas industry, Multi-user Social Educational Games, and Course-offering Determination of academic programs.

Dr. Fuhua (Oscar) Lin is a Professor and Chair of School of Computing and Information Systems, Faculty of Science and Technology of Athabasca University, Canada. Dr. Lin obtained his PhD from Virtual Reality Lab at Hong Kong University of Science and Technology (Hong Kong) in 1998. Prior to working in Athabasca University, Dr. Lin was a Research Officer of Institute for Information Technology of National Research Council (NRC) of Canada. Dr. Lin did post-doc research at University of Calgary during 1998-1999.
Since 1986, Dr. Lin has been conducting research in Intelligent Systems, Multi-Agent Systems, Virtual Reality, and their applications. Dr. Lin has more than 90 publications, including edited books, journal papers, book chapters, conference papers, and reviews. He has been invited to provide more than twenty invited keynotes/lectures/seminars at different academic institutions and international conferences. Furthermore, he has acted as principal and co-principal investigator on more than 12 funded research including two NSERC Discovery Grants, two NSERC Engage grants, one grant from CFI of Canada, others funding from Athabasca University. He served as the Editor-in-Chief of International Journal of Distance Education Technologies during 2011-2013. Dr. Lin is leading a research group --- Intelligent Enterprise and Educational Systems Research Group at Athabasca University. His current main research interest is in collective decision-making, using the multi-agent systems (MAS) approach, multiagent learning, and complex systems modeling.


New Issues and Challenges for Information Science and Control Engineering in the Big Data Era

Qun Jin, Waseda University, Japan (Moderator)
Ming Dong, Wayne State University, USA
Shaozi Li, Xiamen Unierversity, China
Fuhua Oscar Lin, Athabasca University, Canada
Julong Pan, China Jiliang University, China

In this panel, the panelists will highlight emerging issues and challenges for Information Science and Control Engineering in the Big Data Era, present their views on potential solutions and key technologies to tackle these issues, and discuss promising integrated approaches from the perspectives of their different fields. Topics, such as Ubiquitous Computing, Human-Centric Computing, Social Computing, Cyber-Physical Systems, Internet of Things, Smart City, Mobile Internet, and Big Data, will be covered.

Short Bio :
Qun Jin is a professor and the chair in the Department of Human Informatics and Cognitive Sciences, Faculty of Human Sciences, Waseda University, Japan. He has been engaged extensively in research works in the fields of computer science, information systems, and social and human informatics. He seeks to exploit the rich interdependence between theory and practice in his work with interdisciplinary and integrated approaches. His recent research interests cover human-centric ubiquitous computing, human-computer interaction, behavior and cognitive informatics, big data, personal analytics and individual modeling, MOOCs and learning analytics, and computing for well-being. He is a member of IEEE, IEEE CS and ACM, USA, IEICE, IPSJ and JSAI, Japan, and CCF, China. Contact him at

1 International Conference on Information Science and Control Engineering