Keynote Speakers

1 The AI Journey:the road traveled and the (long) road ahead

Ramon Lopez de Mantaras ,Spanish National Research Council (CSIC) .

In this talk I will first briefly summarize the many impressive results we have achieved along the road so far traveled in the field of AI including some concrete results obtained at the IIIA-CSIC. Next I will describe some of the future challenges to be faced along the (long) road we still have ahead of us with an emphasis on integrated systems, a necessary step towards human-level AI. Finally I will comment on the importance of interdisciplinary research to build such integrated systems (for instance, sophisticated robots having artificial cartilages, artificial muscles, artificial skin, etc) using some examples related to materials science.


Master of Sciences in Computer Science from the University of California Berkeley, PhD in Physics (Automatic Control) from the University of Toulouse, and PhD in Computer Science from the Technical University of Barcelona. A pioneer of Artificial Intelligence in Spain, with contributions, since 1976, in Pattern Classification, Approximate Reasoning, Expert Systems, Machine Learning, Case-Based Reasoning, Autonomous Robots, and AI & Music. Author of around 250 papers. Invited plenary speaker at numerous international conferences. Former Editor-in-Chief of Artificial Intelligence Communications, current editorial board member of several international journals, including AI Magazine and Associate Editor of the Artificial Intelligence Journal. Conference Chair of UAI-94, ECML’00, ECAI-04, ECML-07, PKDD-07, and IJCAI-07. ECCAI Fellow. Co-recipient of four best paper awards at international conferences. Recipient of the “City of Barcelona” Research Prize, and the “2011 Association for the Advancement of Artificial Intelligence (AAAI) Robert S. Engelmore Memorial Award”. President of the Board of Trustees of IJCAI from 2007 to 2009. Presently working on case-based reasoning, machine learning for autonomous robots and AI applications to music. For additional information please visit:


2 Transfer learning and Applications

Qiang Yang ,Hong Kong University of Science and Technology.

In machine learning and data mining, we often encounter situations where we have an insufficient amount of high-quality data in a target domain, but we may have plenty of auxiliary data in related domains.  Transfer learning aims to exploit these additional data to improve the learning performance in the target domain. In this talk, I will give an overview on some recent advances in transfer learning for challenging data mining problems.  I will present some theoretical challenges to transfer learning, survey the solutions to them, and discuss several innovative applications of transfer learning, including learning in heterogeneous cross-media domains and in online recommendation, social media and social network mining.


Qiang Yang is a professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology and a director of the new Huawei Noah's Ark Research Lab in Hong Kong, which specializes in Big Data Mining and Artificial Intelligence.  Qiang Yang is an IEEE Fellow and ACM Distinguished Scientist. His research interests include machine learning, data mining and artificial intelligence.  Qiang received his PhD from the University of Maryland, College Park in 1989.  His research teams won the 2004 and 2005 ACM KDDCUP competitions on data mining.  He is a vice chair of ACM SIGART, a founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST),  a PC Co-chair for ACM KDD 2010 and the General Chair for ACM KDD 2012 in Beijing.


3 Semantics of Cyber Physical Systems

Tharam S. Dillon,La Trobe University, Melbourne, Australia.

The very recent development of Cyber-Physical Systems (CPS) pro-vides a smart infrastructure connecting abstract computational artifacts with the physical world. The solution to CPS must transcend the boundary between the cyber world and the physical world by providing integrated models addressing issues from both worlds simultaneously. This needs new theories, conceptual frameworks and engineering practice. In this paper, we set out the key require-ments that must be met by CPS

systems,and review and evaluate the progress that has been made in the development of theory, conceptual frameworks and practical applications. We then discuss the need for semantics and a proposed approach for addressing this. Grand challenges to informatics posed by CPS are raised in the paper.


Professor Tharam Dillon is Professor of Computer Science and Head of Research and Development at the Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, Perth, Western Australia. He was the Dean of the Faculty of Information Technology at the University of Technology, Sydney till 2006, a position he held since 2003. Professor Tharam was the Chair Professor of Computer Science and Computer Engineering at La Trobe University, a position he assumed at the beginning of 1986. In December 1998 he took up the position of Professor of Computing at Hong Kong Polytechnic University and Acting Head of Department of Computing until July 2001, at which time he resumed his role as Chair Professor and Head of the School of Engineering at La Trobe University. He is a Fellow of the Institution of Electrical and Electronic Engineers (USA), Fellow of the Institution of Engineers (Australia), Fellow of the Safety and Reliability Society (UK), and Fellow of the Australian Computer Society.


4 Big Data Mining in the Cloud

Zhongzhi Shi,Institute of Computing Technology, Chinese Academy of Sciences.

Big Data is the growing challenge that organizations face as they deal with large and fast-growing sources of data or information that also present a complex range of analysis and use problems. Digital data production in many fields of human activity from science to enterprise is characterized by an exponential growth. Big data technologies will become a new generation of technologies and architectures which is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time.

Massive data sets are hard to understand, and models and patterns hidden within them cannot be identified by humans directly, but must be analyzed by computers using data mining techniques. The world of big data present rich cross-media contents, such as text, image, video, audio, graphics and so on. For cross-media applications and services over the Internet and mobile wireless networks, there are strong demands for cross-media mining because of the significant amount of computation required for serving millions of Internet or mobile users at the same time. On the other hand, with cloud computing booming, new cloud-based cross-media computing paradigm emerged, in which users store and process their cross-media application data in the cloud in a distributed manner. Cross-media is the outstanding characteristics of the age of big data with large scale and complicated processing task. Cloud-based Big Data platforms will make it practical to access massive compute resources for short time periods without having to build their own big data farms. We propose a framework for cross-media semantic understanding which contains discriminative modeling, generative modeling and cognitive modeling. In cognitive modeling, a new model entitled CAM is proposed which is suitable for cross-media semantic understanding. A Cross-Media Intelligent Retrieval System (CMIRS), which is managed by ontology-based knowledge system KMSphere, will be illustrated.

This talk also concerns Cloud systems which can be effectively employed to handle parallel mining since they provide scalable storage and processing services, as well as software platforms for developing and running data analysis environments. We exploit Cloud computing platforms for running big data mining processes designed as a combination of several data analysis steps to be run in parallel on Cloud computing elements. Finally, the directions for further researches on big data mining technology will be pointed out and discussed.


Zhongzhi Shi is a professor at the Institute of Computing Technology, Chinese Academy of Sciences, leading the Intelligence Science Laboratory. His research interests include intelligence science, machine learning, data mining, image processing, cognitive computing and etc. Professor Shi has published 14 monographs, 15 books and more than 450 research papers in journals and conferences. He has won a 2nd-Grade National Award at Science and Technology Progress of China in 2002, two 2nd-Grade Awards at Science and Technology Progress of the Chinese Academy of Sciences in 1998 and 2001, respectively. He is a fellow of CCF and CAAI, senior member of IEEE, member of AAAI and ACM, Chair for the WG 12.2 of IFIP. He serves as Editor-in-Chief of Series on Intelligence Science, Editor-in-Chief of International Journal of Intelligence Science.


5 Research on Semantic Programming Language

Shi Ying,Wuhan University.

As technologies of Semantic Web Service are gradually matured, developing intelligent web applications with Semantic Web Services becomes an important research topic in Software Engineering. This speech introduces our efforts on Semantic Web Service oriented programming. Employing the concept of semantic computing into service-oriented programming, we proposed a programming language SPL, Semantic Programming Language, which supports the expression and process of semantic information. Based on collaboration of semantic space and information space, the running mechanism of SPL program is presented,

which provides SPL program with higher flexibility and stronger adaptability to changes. Furthermore, with the introduction of semantic operators, a kind of searching conditional expression is offered to facilitate the search of Semantic Web Services with greater preciseness and higher flexibility. Besides, semantic based policy and exception mechanism are also brought in to improve the intelligence of policy inference and exception handing in SPL program. At the same time, a platform that supports design and running of SPL program is developed.


Dr. Shi Ying is the Deputy Director of the State Key Laboratory of Software Engineering (SKLSE), Wuhan University and also serves as the Vice Dean of the School of Computer Science in Wuhan University. He is a member of the CCF TCSE (China Computer Federation – Technical Council on Software Engineering), a member of CIE CCEA (Chinese Institute of Electronics – Cloud Computing Experts Association) and the secretary of Wuhan Software Engineering Society.

Dr. Shi Ying devotes his major efforts in teaching and research on Software Engineering as well as development of application software. His primary research is conducted in several research areas, which includes web service oriented, aspect oriented, and semantic software development, the development approaches in the Era of cloud computing, software development methodology based on intelligent technologies, and software reusability and high trustworthiness.

As the project leader, Dr. Shi Ying has engaged in a number of research projects supported byKey (Key grant) Project of Chinese Ministry of Education, Youth Science & Technology Chen'guang Plan Project of Wuhan City, Youth Outstanding Talent Foundation Project of Hubei Province, National High-tech R&D Program of China (863 Program), National Natural Science Foundation of China, Ph.D. Programs Foundation of Chinese Ministry of Education, etc. He has published more than 100 academic papers in the domestic core journals, among which many are indexed by SCIE, EI and ISTP.

Besides, Dr. Shi Ying has supervised more than 30 PhD students and more than 70 master students, andhas rich experience in teaching courses for undergraduate and graduate students over multiple years, especially software engineering, UML modeling, and formal methods for software development.

Key Laboratory of Intelligent Information Processing
Institute of Computing Technology, Chinese Academy of Science