BDAI2018 | Beijing
Big Data and Artificial Intelligence

Keynote & Plenary Speakers

Jane Doe
Prof. WANG Jun, City University of Hong Kong, Hong Kong
Fellow of IEEE & IAPR
王钧教授, 香港城市大学

Biography: Jun Wang is a Chair Professor of Computational Intelligence in the Department of Computer Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and Chinese University of Hong Kong. He also held various part-time visiting positions at US Air Force Armstrong Laboratory, RIKEN Brain Science Institute, Huazhong University of Science and Technology, Dalian University of Technology, and Shanghai Jiao Tong University as a Changjiang Chair Professor. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published about 200 journal papers, 15 book chapters, 11 edited books, and numerous conference papers in these areas. He is the Editor-in-Chief of the IEEE Transactions on Cybernetics. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009), IEEE Transactions on Cybernetics and its predecessor (2003-2013), and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a member of the editorial advisory board of International Journal of Neural Systems (2006-2013), and a member of the editorial board of Neural Networks (2012-2014) as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008, 2014, 2016), and International Journal of Fuzzy Systems (2010, 2011). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence, and a Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics (2012). He has been an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012, 2014-2016). In addition, he served as President of Asia Pacific Neural Network Assembly (APNNA) in 2006 and many organizations such as IEEE Fellow Committee (2011-2012); IEEE Computational Intelligence Society Awards Committee (2008, 2012, 2014), IEEE Systems, Man, and Cybernetics Society Board of Directors (2013-2015), He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011, Natural Science Awards from Shanghai Municipal Government (2009) and Ministry of Education of China (2011), and Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.

Title of Speech: Collaborative Neurodynamic Optimization Approaches to Nonnegative Matrix Factorization  

Abstract: Nonnegative matrix factorization (NMF) is an advanced method for nonnegative feature extraction, with widespread applications. However, the NMF solution often entails to solve a global optimization problem with a nonconvex objective function and a nonnegativity constraint. To tackle this challenging problem, I will present a collaborative neurodynamic optimization approach by employing a population of recurrent neural networks (RNNs) at the lower level and particle swarm optimization (PSO) with wavelet mutation at the upper level. The RNNs act as search agents carrying out precise constrained local searches according to their neurodynamic equations and initial conditions. The PSO algorithm coordinates and guides the RNNs with updated initial states toward global optimal solution(s). A wavelet mutation operator is added in the optimization to enhance PSO exploration capability. Through iterative interaction and improvement of the locally best solutions of RNNs and global best positions of the whole population, the population-based neurodynamic systems is almost sure to achieve the global optimality for the NMF problem. The convergence of the group best state to the global optimal solution with probability one is proven. The experimental results substantiate the efficacy and superiority of the collaborative neurodynamic optimization approach to bound-constrained global optimization with several benchmark nonconvex functions and NMF-based clustering with benchmark datasets in comparison to the state-of-the-art algorithms. 

Jane Doe
Prof. Steven Guan, Xi'an Jiaotong-Liverpool University, China
关圣威教授, 西交利物浦大学

Biography: Steven Guan received his BSc. from Tsinghua University (1979) and M.Sc. (1987) & Ph.D. (1989) from the University of North Carolina at Chapel Hill. He is currently a Professor and the Director for Research Institute of Big Data Analytics at Xi'an Jiaotong-Liverpool University (XJTLU). He served the head of department position at XJTLU for 4.5 years, creating the department from scratch and now in shape. Before joining XJTLU, he was a tenured professor and chair in intelligent systems at Brunel University, UK.
Prof. Guan has worked in a prestigious R&D organization for several years, serving as a design engineer, project leader, and department manager. After leaving the industry, he joined the academia for three and half years. He served as deputy director for the Computing Center and the chairman for the Department of Information & Communication Technology. Later he joined the Electrical & Computer Engineering Department at National University of Singapore as an associate professor for 8 years.
Prof. Guan’s research interests include: machine learning, computational intelligence, big data analytics, mobile commerce, modeling, networking, personalization, security, and pseudorandom number generation. He has published extensively in these areas, with 130+ journal papers and 180+ book chapters or conference papers. He has chaired, delivered keynote speech for 80+ international conferences and served in 180+ international conference committees and 20+ editorial boards. There are quite a few inventions from Prof. Guan including Generalized Minimum Distance Decoding for Majority Logic Decodable Codes, Prioritized Petri Nets, Self- Modifiable Color Petri Nets, Dynamic Petri Net Model for Iterative and Interactive Distributed Multimedia Presentation, Incremental Feature Learning, Ordered Incremental Input/Output Feature Learning, Input/Output Space Partitioning for Machine Learning, Recursive Supervised Learning, Reduced Pattern Training using Pattern Distributor, Contribution Based Feature Selection, Incremental Genetic Algorithms, Incremental Multi-Objective Genetic Algorithms, Decremental Multi-objective Optimization, Multi-objective Optimization with Objective Replacement, Incremental Hyperplane Partitioning for Classification, Incremental Hyper-sphere Partitioning for Classification, Controllable Cellular Automata for Pseudorandom Number Generation, Self Programmable Cellular Automata, Configurable Cellular Automata, Layered Cellular Automata, Transformation Sequencing of Cellular Automata for Pseudorandom Number Generation, Open Communication with Self-Modifying Protocols, etc.

Title of Speech: Opportunities and Challenges in Information Communications Technology 

Abstract: This talk introduces the overall trends of Information Communications Technology (ICT) and presents an overview for opportunities and challenges in ICT. Critical issues, research problems and developments of ICT in various areas are addressed, such as green computing, Internet computing, mobile computing, and intelligent computing. Opportunities and challenges in relevant areas are also covered, for example, Internet of Things, cloud computing, big data analytics. Critical development of ICT in various aspects are proposed thereafter. Finally, the challenges faced by the higher education sector are also discussed. 

Jane Doe
Prof. Jun Xu, Institute of Computing Technology, Chinese Academy of Sciences, China
徐君教授, 中国科学院大学

Biography: Jun Xu is a professor at Institute of Computing Technology, Chinese Academy of Sciences (ICT-CAS). He received his PhD in Computer Science from the Nankai University, Tianjin, China. Before joining ICT-CAS, he was a senior researcher at Noah’s Ark Lab, Huawei Technologies at Hong Kong and an associate researcher at Microsoft Research Asian. His research interests include learning to rank for information retrieval, semantic matching in search, and text data analysis. Some of his work has published on top conferences and journals and received over 2000 citations (according to Google Scholar). He is also the author of a book: “Semantic Matching in Search” (NOW publishers). On product contributions, he has transferred several technologies to Microsoft Bing Search, Microsoft SharePoint Search, and Huawei GTS Search. On professional services, he has organized SIGIR 2014 workshop on Sematic Matching in Information Retrieval, served as Senior PC for ACML, as PC of top conferences on web search and data mining, e.g., SIGIR, WWW, NIPS, IJCAI, CIKM, WSDM, as reviewer of several leading journals and publishers, e.g., TKDE, TOIS, TIST, NOW publishers, and Springer.

Title of Speech: Reinforcement Learning to Rank for Search Result Diversification 

Abstract: The goal of search result diversification is to construct a document ranking for satisfying as many different query subtopics as possible. Typically, the diverse ranking process can be formalized as greedy sequential document selection. At each position, the document that can provide the largest amount of additional information to the users is selected. Since the utility of a document depends on its preceding documents in search result diversification, constructing an optimal document ranking is NP-hard. The traditional greedy document selection usually leads to suboptimal solutions. In the talk, I will show that the problem can be alleviated with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model. Specifically, the sequential document selection process is fit into an MDP. At each time step the greedy action is further improved through the exploratory tree search by MCTS. Reinforcement learning algorithm was developed to learn the model parameters. Empirical evaluation clearly indicated the effectiveness of the approach. The MCTS enhanced MDP can also be applied to variant applications, including sequence tagging, text matching etc.  

Jane Doe
Prof. Yao Liang, Indiana University Purdue University, Indianapolis (IUPUI), USA

Biography: Yao Liang received his B.S. degree in Computer Engineering and M.S. degree in Computer Science from Xi’an Jiaotong University, Xi’an, China. He received his Ph.D. degree in Computer Science from Clemson University, Clemson, USA, in 1997.
He is currently a Professor in the Department of Computer and Information Science, Purdue University School of Science, Indiana University Purdue University, Indianapolis (IUPUI), USA. His research interests include wireless sensor networks, Internet of Things, cyberinfrastructure, multimedia networking, adaptive network control and management, machine learning, neural networks, data mining, data fusion, data management and integration, and distributed systems. His research projects have been funded by NSF. Prior to joining IUPUI, he was on the faculty of Department of Electrical and Computer Engineering at Virginia Tech, USA. He also had extensive industrial R&D experiences as a Technical Staff Member in Alcatel USA. Dr. Liang has published numerous papers on various prestigious journals and international conferences, and received two US patents. He has served regularly on Program Committees for various major international conferences. Dr. Liang has given invited talks and lectures at various universities in US, Europe and China. He is a Senior Member of IEEE, and a Member of ACM.

Title of Speech: Wireless Data Acquisition in Big Data Era 

Abstract: Wireless sensor networks (WSNs) and Internet of Things (IoT) are fundamentally changing today’s practice of numerous scientific and engineering endeavors by enabling continuous monitoring and sensing of physical variables of interest at unprecedented high spatial densities over long-time durations. They have significantly impacted broad fields such as environmental sciences and engineering, ecosystems, natural hazards, precision agriculture, smart building, and smart city. We focus on outdoor large-scale WSNs/IoT that are deployed in harsh environments, such as mountainous areas, hilly watersheds, and forests, which present great challenges in WSN/IoT data acquisition, because of the severe resource constraints (e.g., battery power, bandwidth, memory size, and CPU capacity) of tiny sensor nodes. In this talk, I will share my group’s work on wireless data acquisition and present a novel compressed sensing approach. Our approach can recover the sensing data at the WSN sink with high fidelity when very few data packets are collected, leading to a significant reduction of the network transmissions and thus an extension of the WSN/IoT lifetime. I will also present and discuss our results on data acquisition from a real-world environmental WSN testbed deployed in Pennsylvania, USA. 

Jane Doe
Assoc. Prof. Yong Chen, Texas Tech University, USA

Biography: Yong Chen is an Associate Professor and Director of the Data-Intensive Scalable Computing Laboratory in the Computer Science Department of Texas Tech University. He is also a Site Director of the Cloud and Autonomic Computing center at Texas Tech. His research interests include data-intensive computing, parallel and distributed computing, high-performance computing, and cloud computing. He has published over 100 research papers in international journals and conferences, and received several awards for his research activities including the IEEE TCSC (Technical Committee on Scalable Computing) Young Achievers Award, the Ralph E. Powe Junior Faculty Enhancement Award, Texas Tech University Whitacre College of Engineering Research Award, Texas Tech University Mortar Board and Omicron Delta Kappa Outstanding Faculty Award, several Best Paper Awards including The 11th IEEE International Conference on Networking, Architecture, and Storage (NAS), The 14th and the 9th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA), The 7th International Conference on Future Information Technology, and Best Paper finalist and Best Student Paper finalist at the ACM/IEEE Supercomputing Conference (SC). His research has been funded by the National Science Foundation, Department of Defense, Department of Energy/Argonne National Laboratory, Oak Ridge Associated University, Dell Inc., Nimboxx, Jabil/Stack Velocity, and NVidia. He has also served as editors, chairs, and program committee members for numerous international journals, conferences, and workshops. More information about him can be found at

Title of Speech: High Performance Computing Revisited for Big Data Applications  

Abstract: The increasingly important data-intensive scientific discovery presents a critical question to the high performance computing (HPC) community - how to efficiently support these growing scientific big data applications with HPC systems that are traditionally designed for big compute applications? The conventional HPC systems are computing-centric and designed for computation-intensive applications. Scientific big data applications have different characteristics compared to big compute applications. These scientific applications, however, will still largely rely on HPC systems to be solved. In this talk, we try to answer this question with a rethinking of HPC system architecture. We study and analyze a decoupled HPC system architecture for data-intensive scientific applications. The fundamental idea is to decouple conventional compute nodes and dynamically provision as data processing nodes that focus on data processing capability. We present studies and analyses for such decoupled HPC system architecture. Its data-centric model and architecture can have an impact in designing and developing future HPC systems for big data applications.