Keynote and Invited Speakers

1. Advances in Transfer Learning


QiangYang, Chair Professor at Computer Science and Engineering Department, Hong Kong University of Science and Technology, China. https://www.cse.ust.hk/admin/people/faculty/profile/qyang

Abstract: Transfer learning aims to leverage knowledge from existing tasks to solve new tasks.  In this talk, I will give an overview of recent advances of transfer learning and point to future works that both have practical significance and theoretical potential.


Qiang Yang is a chair professor at Computer Science and Engineering Department at Hong Kong University of Science and Technology (HKUST).  His research interest is transfer learning in AI.  His research interests are artificial intelligence, machine learning, data mining and planning.  He is a fellow of AAAI, ACM, IEEE, IAPR, AAAS and CAAI.   He received his PhD from the Department of Computer Science at the University of Maryland, College Park in 1989 and had been a faculty member at the University of Waterloo between 1989 and 1995.  He was a professor and NSERC Industrial Research Chair at Simon Fraser University in Canada from 1995 to 2001.   He had been the founding director of the Huawei's Noah's Ark Research Lab between 2012 and 2015 and the founding director of HKUST’s Big Data Institute.  He was the head of the Computer Science and Engineering Department from 2015 to 2017, and co-founder of the 4th Paradigm Inc.  He was the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST) and is currently the founding Editor in Chief of IEEE Transactions on Big Data (IEEE TBD).  He has served as a PC Chair or General Chair of several international conferences, including ACM KDD, IJCAI, RecSys, IUI and ICCBR.  He received the ACM SIGKDD Distinguished Service Award in 2017, ACM KDDCUP championships in 2004/2005. He is currently the President of IJCAI (2017-2019) and an executive council member of AAAI.

2. Grounding & Learning about  Human Environments & Activities  for Autonomous Robots

Anthony G Cohn 

Director of Research and Innovation, School of Computing, University of Leeds, Leeds, LS2 9JT, UK. Distinguished Visiting Professor at Tongji University. T: 0113 3435482  

Abstract: With the recent proliferation of human-oriented robotic applications in domestic and industrial scenarios, it is vital for robots to continually learn about their environments and about the humans they share their environments with. In this paper, we present a novel, online, incremental framework for \emph{unsupervised} symbol grounding in real-world, human environments for autonomous robots. We demonstrate the flexibility of the framework by learning about colours, people names, usable objects and simple human activities, integrating state-of-the-art object segmentation, pose estimation, activity analysis along with a number of sensory input encodings into a continual learning framework. Natural language is grounded to the learned concepts, enabling the robot to communicate in a human-understandable way. We show, using a challenging real-world dataset of human activities as perceived by a mobile robot, that our framework is able to extract useful concepts, ground natural language descriptions to them, and, as a proof-of-concept, generate simple sentences from templates to describe people and the activities they are engaged in.


Tony Cohn holds a Personal Chair at the , where he is Professor of Automated Reasoning and Director of Research and Innovation in the . He is a , and is also a Fellow of , ,  (formerly ECCAI; Founding Fellow), the , and the .  He was Programme Chair of the  ECAI-94,  and ,  Conference Chair of ,  He is Emeritus Editor-in-Chief of the journal Artificial Intelligence, and Editor-in-Chief of the journal Spatial Cognition and Computation. He has received Distinguished Service awards from and . He is currently a Distinguished Visiting Professor at Tongji University.

3. Artificial Intelligence overview and impacts

Eunika Mercier-Laurent

Abstract: The recent craze for AI and limitation to data, deep learning and chat bots cover only a very small part of AI patrimony. Facing various and difficult challenges requires knowing the whole spectrum in aim to select the best approach and techniques. Environmental impact and climate change can be easily faced by right AI and alternative thinking. Smart software (and hardware) conceived using eco-design approach have a potential to reduce our impact and bring a contribution to the Planet protection.


Eunika Mercier-Laurent is electronic engineer, PhD in computer science, expert in artificial intelligence, associate researcher with University Jean Moulin Lyon 3 and University of Reims Champagne Ardennes and Professor of Knowledge & Innovation Management at EPITA and others engineering schools and universities.

After working as researcher in INRIA, computers designer and manager of innovative AI applications with Groupe Bull, she founded Global Innovation Strategies devoted to all aspects of Knowledge Innovation. Among her research topics are: Knowledge and Eco-innovation Management Systems, methods and techniques for innovation, knowledge modelling and processing, complex problem solving, sustainability, eco-design and impacts of artificial intelligence.

Among 100 world experts of Entovation Intl since 1996, she is President of Innovation3D, International Association for Global Innovation, Vice-chair IFIP TC12 Artificial Intelligence, chairman of IFIP TC12.6 (AI for Knowledge Management), expert for EU programs and author of over hundred publications and books.

4. Is knowledge Engineering out-of-date?

Yueting Zhuang

Dean of College of Computer Science, Zhejiang University

Abstract: The world is now in the era of a new wave AI technology. Though, many of us still remembered the days when knowledge Engineering along with expert system was extremely hot, in such a state that is similar to deep learning or machine learning nowadays. This talk will first give a short survey of AI, especially the concept of knowledge Engineering, rule-based expert system, and so on,  and then introduce the data-driven machine learning approaches used in systems like Wikipedia, Freebase, Google Knowledge Graph etc. It will conclude that knowledge engineering is NOT out-of-date. What indeed outdated is the method of knowledge acquisition. Finally it will introduce knowledge computing engine in order to support knowledge engineering.


Yueting Zhuang received his B.Sc., M.Sc. and Ph.D. degrees in Computer Science from Zhejiang University, China, in 1986, 1989 and 1998 respectively. From February 1997 to August 1998, he was a visiting scholar at the University of Illinois at Urbana-Champaign. He served as the Dean of College of Computer Science, Zhejiang University from 2009 to 2017, the director of Institute of Artificial Intelligence from 2006 to 2015. Currently, he is a full professor at the College of Computer Science, Director of MOE-Digital Library Engineering Research Center, Zhejiang University.

His research interests mainly include multimedia retrieval, artificial intelligence, cross-media computing, digital library. He has won various awards and honors such as National Science Fund for Distinguished Young Scholars of China from National Natural Science Foundation(2005), the “Chang Jiang Scholars Program” Professor of the Ministry of Education of China(2008), the chief scientist of 973 Project(2012CB316400). He was the leading PI of the digital library project— “China America Digital Academic Library(CADAL)”, which has now become one of the largest non-profit digital libraries in the world. Also he is the director of the technical center of UNESCO Category II---International Knowledge Center of Engineering Science and technology(IKCEST).

Yueting Zhuang now serves as the standing committee member of CAAI, a member of Zhejiang Provincial Government AI Development Committee(AI Top 30)

5. Deep learning based Image Interpretation

Lichen Jiao

IEEE Fellow. Professor in the School of Artificial Intelligence at Xidian University, Xi'an, China. Director of International Research Center for Intelligent Perception and Computation, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Joint International Research Laboratory of Intelligent Perception and Computation.


Abstract: With the development of sensor and data storage technology, the data acquisition becomes easier, but it brings “big data” problems, of which, Images are the most common information sources in daily life. Compared with other information sources, the images contain huge amounts of information, and its complexity, redundancy and other characteristics distinguish it from other information sources. The image processing is relatively difficult, and the human visual system has shown excellent capabilities in image processing, which attracting the attention of many researchers. The application of deep learning model in recent years has made a new progress in the study of deep neural networks and brought a new research boom.

    In this talk, we focus on the intelligent acquisition, interpretation mechanism and the massive, high-dimensional, heterogeneous, and dynamic complex high-resolution image data, by means of compressed sensing, sparse representation, distributed collaboration, visual salience, constraint optimization and other techniques.


    Licheng Jiao received the B.S. degree from Shanghai Jiaotong University, Shanghai, China, in 1982, the M.S. and Ph.D. degrees from Xi'an Jiaotong University, Xi'an, China, in 1984 and 1990, respectively. His research interests mainly include intelligent perception and image understanding, image understanding and object recognition, deep learning and brain-inspired computation. His research results have won the Youth Science and Technology Award, the Second Prize of National Natural Science Award and several provincial-level first Prizes. More than 20 Academic monographs have been published, which have won the National Scientific Book Award 5 times and the first “Three One-hundred” Excellent Book Award. He has published more than 2000 papers and 150 authorized patents, which have around 30200 citations. Evaluated by the Google Scholar, the H-index of his publication is 69.
    He is the chairman of IET Xi’an Network, the Xi’an Chapter of IEEE Computational Intelligence Society, the Award Commission of IEEE Xi’an Chapter, and the Xi’an Chapter of IEEE Geoscience and Remote Sensing Society. He is the Associate Editor of “IEEE Transactions on Geoscience and Remote Sensing”, the Presiding Panelist for the Innovative Team in the Ministry of Education, the member of the Subject Consultative Group of the State Council and the expert of the Undergraduate Teaching Level Evaluation of the Ministry of Education, evaluation expert of the National Natural Science Foundation Information Division, member of the Assessment Panel of National Postdoctoral Management Committees.
    He has been receiving special government allowance from the State Council since 1991. In 1996, he was included among the first batch of in the New Century Talents Project (the first and second classes)and the “Three Fives” Talent Project of Shaanxi Province. He was selected as the National Model Teacher by the Ministry of Human Resources and Social Security of China, the Outstanding Contribution Expert of Shaanxi Province.

6. Effective Utilization of Genomic Data

Yadong Wang School of Computer Science and Technology, Harbin Institute of Technology, China

Abstract: With the rapid development and wide application of high-throughput genome sequencing technology, a series of large scale international genomics study plans have been carried out. This makes an explosive and continuous growth of genomics data, and the in depth integration of genomics data and healthcare data, which triggers a “data revolution” in life science.

Nowadays, the effective use of genomics data has become an engine critical to the development of life science as well as other related fields such as healthcare, medicine, drug development, etc. Genomics data has large volume, various data structures and complex relationships, which makes it difficult to effectively analyze and utilize. State of the art genomics data analysis technologies can merely dig out 30-50% of the value of the data, i.e., the large potentials of the data cannot be fully realized. This has been one of the biggest challenges to genomics and bioinformatics.
The drawbacks of the existing analysis approaches, including (but not limited to) low sensitivity, low accuracy, low consistency, low efficiency, etc., are the bottlenecks to the effective use of genomics data. It is the main way to solve these problems by developing advanced bioinformatics algorithms, to continuously improve the quality and efficiency of data analysis. Centers for Bioinformatics of Harbin Institute of technology have made great efforts in recent years to develop a batch of innovative genomics data analysis algorithms and systems. These algorithms and systems substantially improve their performances for a series of fundamental genomics data analysis, such as sequencing read alignment, variant calling and genomics big data visualization. With these achievements, several technical bottlenecks have been breakthrough, which make large contributions to the effective use of genomics data.


Yadong Wang is mainly engaged in bioinformatics, medical informatics, machine learning, artificial intelligence and genome science, the chief scientist of the Heilongjiang Province artificial intelligence industry technology innovation strategic alliance, the national key R & D Program "precision medicine research", the chief scientist of the "China one hundred thousand person genome project", the national biotech The experts of the Expert Committee on the development strategy guidance and the national biotechnology development strategy outline, the expert group, the expert of the 2030 major project of science and technology innovation, the deputy leader of the base platform group and the "13th Five-Year" national key research and development plan "major chronic non communicable disease prevention and control research". Experts of the expert group, expert group of "13th Five-Year" national key research and development plan "research and development of Key Biotechnology for biological safety", and draft expert group on the legislation of China's regulations on the management of genetic resources are drafted by experts. He has been the chief scientist of the National 863 plan biological data project (2014-2017), the expert group expert (2007-2011) of the National 863 plan biology and medicine field (2007-2011), and the expert group (2001-2006) of the National 863 plan bioinformation technology subject group. More than 150 SCI papers have been published, and dozens of big data artificial intelligence systems with the world's leading level have been developed, and have won 1 awards for two national science and technology progress.

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