Keynote Speakers

Prof. Hao Ying
Ph.D., IEEE Fellow
Title: Fuzzy Discrete Event Systems Theory and Its Application in HIV/AIDS Treatment.
Abstract: To be ready soon.
Biography: Professor Ying has published one single-author research monograph/advanced textbook entitled Fuzzy Control and Modeling: Analytical Foundations and Applications (IEEE Press, 2000, 342 pages; foreword by Professor Lotfi A. Zadeh), which contains solely his own research results. He has coauthored another book titled Introduction to Type-2 Fuzzy Logic Control: Theory and Applications (IEEE Press and John Wiley & Sons, Inc., 2014). In addition, he has published 110 peer-reviewed journal papers and 160 peer-reviewed conference papers. Prof. Ying's work has been widely cited - his Google Scholar h-index is 43. The 43 publications included in his index have by themselves generated more than 4,000 citations whereas the total number of citations for all his publications is almost 7,000. He holds two U.S. patents. He is serving as an Associate Editor or a Member of Editorial Board for nine international journals, including the IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Systems, Man, and Cybernetics: Systems. He serves as a member of the Fuzzy Systems Technical Committee of the IEEE Computational Intelligence Society and is a member of the Fellow Evaluation Committee of the IEEE Systems, Man, and Cybernetics Society. He was elected to serve as a board member of the North American Fuzzy Information Processing Society (NAFIPS) for two terms (2005-2008 and 2008-2011). He served as Program Chair for the 2005 NAFIPS Conference and Program Co-Chair for the 2010 NAFIPS Conference as well as for the International Joint Conference of NAFIPS Conference, Industrial Fuzzy Control and Intelligent System Conference, and NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic held in 1994. He served as the Publication Chair for the 2000 IEEE International Conference on Fuzzy Systems and the Competition Chair for this annual conference in 2009 and 2011. He also served as a Program/Technical Committee Member for 90 international conferences.
Prof. Joao Gama
Ph.D., D.Sc.
Title: Two-layer Learning

Data Mining is faced with new challenges. In emerging applications (like financial data, traffic TCP/IP, sensor networks, etc) data continuously flow eventually at high speed. The processes generating data evolve over time, and the concepts we are learning change. In this talk we present a one-pass classification algorithm able to detect and react to changes. We present a framework that identify contexts using drift detection, characterise contexts using meta-learning, and select the most appropriate base model for the incoming data using unlabelled examples.

Evolving data requires that learning algorithms must be able to monitor the learning process and the ability of predictive self-diagnosis. A significant and useful characteristic is diagnostics - not only after failure has occurred, but also predictive (before failure). These aspects require monitoring the evolution of the learning process, taking into account the available resources, and the ability of reasoning and learning about it.

Biography: João Gama is an Associate Professor at the University of Porto, Portugal. He is also a senior researcher and member of the board of directors of the Laboratory of Artificial Intelligence and Decision Support (LIAAD), a group belonging to INESC Porto. João Gama serves as the member of the Editorial Board of Machine Learning Journal, Data Mining and Knowledge Discovery, Intelligent Data Analysis and New Generation Computing. He served as Co-chair of ECML 2005, DS09, ADMA09 and a series of Workshops on KDDS and Knowledge Discovery from Sensor Data with ACM SIGKDD. He was also the chair for the conference of Intelligent Data Analysis 2011. His main research interest is in knowledge discovery from data streams and evolving data. He is the author of more than 200 papers reviewed by peers and author of a recent book on Knowledge Discovery from Data Streams. He has extensive publications in the area of data stream learning. His H-index is 41 (Google scholar).
Title: Urban Computing: Enable Intelligent Cities with Big Data and AI

Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in cities to tackle urban challenges, e.g. air pollution, energy consumption and traffic congestion. Urban computing connects unobtrusive and ubiquitous sensing technologies, advanced data management and AI models, and novel visualization methods, to create win-win-win solutions that improve urban environment, human life quality, and city operation systems. Urban computing is an inter-disciplinary field where computer science meets urban planning, transportation, economy, the environment, sociology, and energy, etc., in the context of urban spaces. In this talk, I will overview the framework of urban computing, discussing its key challenges and methodologies from computer science’s perspective. This talk will also present a diversity of urban computing applications, ranging from big data-driven environmental protection to transportation, from urban planning to urban economy. The research has been not only published at prestigious conferences but also deployed in the real world. More details can be found on

Biography: Dr. Yu Zheng is a senior research manager in Urban Computing Group at Microsoft Research. He is also a Chair Professor at Shanghai Jiao Tong University and an Adjunct Professor at Hong Kong University of Science and Technology. Zheng currently serves as the Editor-in-Chief of ACM Transactions on Intelligent Systems and Technology (5-year IF: 10.41) and the founding Secretary of SIGKDD China Chapter. He has served as chair on over 10 prestigious international conferences, e.g. as the program co-chair of ICDE 2014 and CIKM 2017 (Industrial Track). He publishes referred papers frequently as a leading author at prestigious conferences and journals, such as KDD, VLDB, UbiComp, and IEEE TKDE. Those papers have been cited over 14,000 times in recent five years (Google Scholar H-Index: 56). One of his projects, entitled Urban Air, has been deployed with the Chinese Ministry of Environmental Protection, predicting air quality for over 300 Chinese cities based on big data. In 2013, he was named one of the Top Innovators under 35 by MIT Technology Review (TR35) and featured by Time Magazine for his research on urban computing. In 2014, he was named one of the Top 40 Business Elites under 40 in China by Fortune Magazine, because of the business impact of urban computing he has been advocating since 2008. In 2016, he is honored as an ACM Distinguished Scientist.

Tutorial Speakers

Prof. Jie Lu
Title: Concept Drift Detection and Adaptation
Abstract: Concept Drift is known as unforeseeable change in underlying streaming data distribution over time. The phenomenon of concept drift has been recognized as the root cause of decreased effectiveness in many decision-related applications. Adaptive learning under concept drift is a relatively new research field and is one of the most pressing and fundamental problems in the current age of big data. Building an adaptive system is a highly promising solution for coping with persistent environmental change and avoiding system performance degradation. This talk will present a set of methods and algorithms that can effectively and accurately detect concept drift and react to it, with knowledge adaptation, in a timely way.
Biography: Distinguished Professor Jie Lu is an internationally renowned scientist in the areas of computational intelligence, specifically in decision support systems, fuzzy transfer learning, concept drift, and recommender systems. She is the Associate Dean in Research Excellence in the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS) and the Director of Centre for Artificial Intelligence (CAI) at UTS. She is also the co-Director of the Joint Research Centre Wise Information Systems (WIS) between UTS and Shanghai University. She has published six research books and 400 papers in Artificial Intelligence, IEEE transactions on Fuzzy Systems and other refereed journals and conference proceedings (H-index 43, Google Scholar). She has won eight Australian Research Council (ARC) discovery grants and 10 other research grants for over $4 million. She serves as Editor-In-Chief for Knowledge-Based Systems (Elsevier) and Editor-In-Chief for International Journal on Computational Intelligence Systems (Atlantis), has delivered 15 keynote speeches at international conferences, and has chaired 10 international conferences. She is an ARC panel member (2016-2018) and Fellow of IFSA.
Kejia Chen
Title: Machine Learning in Social Network Analysis and Mining

A network is a set of items (vertices or nodes) with connections (edges) between them. Systems taking the form of networks abound in the real world such as online social networks, information network (e.g. citation network, preference network), biological network like gene-disease network or protein interaction network, and many others. The above networks are usually called complex networks.

Networks have first been studied extensively in the social sciences, thus generating the research field of Social Network Analysis and Mining (SNAM in short). In SNAM, the properties of network such as clustering coefficient and degree distributions are analyzed. There are also mining tasks including node ranking, link prediction, community detection, network evolution and so on. The research results of SNAM are also applicable to other complex networks.

Machine learning is a powerful tool for data analysis and mining. It has been successful applied in text mining and web mining. This tutorial will give a detailed introduction on SNAM and reviews the recent work of machine learning methods used in SNAM, especially in the task of link prediction and community detection. Finally, future research topics in SNAM and their machine learning solutions will be discussed.

Biography: Kejia Chen is an associate professor in Nanjing University of Posts and Telecommunications. She received her PhD in Université de Technologie de Compiègne in France and her master's degree in LAMDA group, Nanjing University. She joined Jiangsu Key Laboratory of Big Data Security & Intelligent Processing in 2017. Her current research focuses on data mining and machine learning with applications in social network analysis. She once published papers in TOIS, ASONAM, ICDMW, ECML, ICTAI and got the best paper award in CCML. She also serves as a reviewer for the international journals like Information Systems and Neural Networks.

Invited Talk

Ming Li
Ph. D.
Title: Learning to locate software bugs.

Software Systems are becoming larger and more complex, which places a big challenge on software quality assurance because it is almost infeasible to conduct extensive code inspection or testing for every software module. Thus, software systems are usually shipped with bugs. Locating the buggy software modules effectively may help to improve the quality of the software system as well as the productivity of the developers. A plenty of models and approaches have been proposed for locating software bugs. However, some of them may not fully capture the data properties of software. In this talk, we will discuss several attempts to address the problem of locating software bugs from a machine learning perspective, where the properties of software data are carefully considered.

Biography: Dr. Ming Li is currently an associate professor with the National Key Laboratory of Novel Software Technology, Nanjing University. His major research interests include machine learning and data mining, especially on software mining. He has published over 30 papers on refereed international journals including IEEE Trans. Knowledge and Data Engineering, Automated Software Engineering Journal, Software: Practice & Experience, and top conferences including IJCAI, ICML, etc. He has served as the senior PC member of the premium conferences in artificial intelligence such as IJCAI and AAAI, and PC members for other premium conferences such as KDD, NIPS, ACMMM, ICDM, etc., and he is the chair of the 1st – 6th International Workshop on Software Mining. He has served as the associate editor (junior) for Frontiers of Computer Science and editorial board member for International Journal of Data Warehousing and Mining. He is the executive board member of ACM SIGKDD China Chapter. He has been granted various awards including the Excellent Youth Award from NSFC, the New Century Excellent Talents program of the Education Ministry of China, the CCF Distinguished Doctoral Dissertation Award, and Microsoft Fellowship Award, etc.
Yang Yu
Title: Derivative-free Optimization -- Towards More Possibilities for Learning

Machine learning systems are commonly rooted in optimizations. Optimization ability restricts what a learning system can represent and learn. Convex programming and gradient-based methods are widely adopted optimization tools in machine learning, which, however, have limited suitable conditions. Derivative-free optimization, with recent progress in both theoretical foundation and practice advantage, is catching up. Without the restrictions of gradients, derivative-free optimization has a much broader range of applicability. In this talk, we will introduce some progress of derivative-free optimization, and demonstrate its usefulness in creating more possibilities for learning system design.

Biography: Yang Yu is an Associate Professor in the Department of Computer Science, Nanjing University, China. His research interest is in artificial intelligence, mainly on derivative-free reinforcement learning, theoretical-grounded evolutionary algorithms, and ensemble learning. His work has been published in Artificial Intelligence, IJCAI, AAAI, NIPS, KDD, etc. He has been granted several awards such as the best paper award of IDEAL'16, GECCO'11, PAKDD'08. He is/was an Area Chair of IJCAI’18 and ACML’17, Publicity Co-chair of IJCAI'16/17 and IEEE ICDM'16, Workshop Co-chair of ACML’16 and PRICAI’18.