Professor Zongben Xu is an academician of Chinese Academy of Sciences, mathematician, signal and information processing expert in Xi'an Jiaotong University. He was a Vice President of Xi'an Jiaotong University, and is currently the Deputy Director of the Information Technology Science Department of the Chinese Academy of Sciences; Dean of Xi'an Institute of Mathematics and Mathematics Technology, Xi'an Jiaotong University; Director of the National Engineering Laboratory of Big Data Algorithms and Analysis Technology. He is a member of the National Advisory Committee of Big Data Experts, and a member of the National New Generation Open Innovation Platform for Artificial Intelligence.
Title: On Hypotheses of Machine Learning: A Best-fitting Theory
Abstract: Machine learning (ML) has been run and applied by premising a series of presuppositions, which contributes both the great success of AI and the bottleneck of further development of ML. These presuppositions include (i) the independence assumption of loss function on dataset (Hypothesis I); (ii) the large capacity assumption on hypothesis space including solution (Hypothesis II); (iii) the completeness assumption of training data with high quality (Hypothesis III); and (iv) the Euclidean assumption on analysis framework and methodology (Hypothesis IV).
Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data” and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,200 referred conference and journal papers cited more than 125,000 times with an H-index of 162. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).
Title: Broad Learning: A New Perspective on Mining Big Data
Abstract: In the era of big data, there are abundant of data available across many different data sources in various formats. “Broad Learning” is a new type of learning task, which focuses on fusing multiple large-scale information sources of diverse varieties together and carrying out synergistic data mining tasks across these fused sources in one unified analytic. Great challenges exist on “Broad Learning” for the effective fusion of relevant knowledge across different data sources, which depend upon not only the relatedness of these data sources, but also the target application problem. In this talk we examine how to fuse heterogeneous information to improve mining effectiveness over various applications, including social network, recommendation, mobile health (m-health) and Question Answering (QA).
Jianhong Wu is a Senior Canada Research Chair, an NSERC Industrial Research Chair, and a University Distinguished Research Professor of Industrial and Applied Mathematics at York University, Canada. His research interest includes dynamical systems, neural networks and pattern recognition, mathematical biology and epidemiology. He is a co editor-in-chief for Infectious Disease Modelling and for Big Data and Information Analytics. He has been a member of the Editorial Board for journals including IEEE Transactions on Pattern Analysis and Machine Intelligence.
Title: Data analytics, modelling and optimization to inform decision making in a rapidly evolving pandemic
C. L. Philip Chen (S’88–M’88–SM’94–F’07) is the Chair Professor and Dean of the College of Computer Science and Engineering, South China University of Technology and was the former Dean of the Faculty of Science and Technology, University of Macau (2010-2017). He is a Fellow of IEEE, AAAS, IAPR, CAA, and HKIE; a member of Academia Europaea (AE), European Academy of Sciences and Arts (EASA). He received IEEE Norbert Wiener Award in 2018 for his contribution in systems and cybernetics, and machine learnings. He received IEEE Tran. On Neural Networks and Learning Systems best transactions paper award two times for his papers in 2014 and 2018. He is a highly cited researcher by Clarivate Analytics in 2018 and 2019.
His current research interests include cybernetics, systems, and computational intelligence. He was the IEEE Systems, Man, and Cybernetics Society President from 2012 to 2013, the Editor-in-Chief of the IEEE Transactions on Systems, Man, and Cybernetics: Systems (2014-2019), and currently, he is the Editor-in-Chief of the IEEE Transactions on Cybernetics, and an Associate Editor of the IEEE Transactions on AI, and IEEE Transactions on Fuzzy Systems. He was the Chair of TC 9.1 Economic and Business Systems of International Federation of Automatic Control from 2015 to 2017.
Title: Recurrent and Gated Broad Learning Systems: an LSTM-Like Architecture and its Applications in Time-Related Data Analysis
Abstract: In this talk, the broad learning system (BLS) will be discussed first. Its variations in recurrent structure, Recurrent BLS (RBLS) and long-short term memory (LSTM)-like architectures by adding forget gates function, Gated BLS (GBLS), will be discussed along with the learning algorithms. The proposed two architectures and learning algorithms possess three advantages: 1) higher accuracy due to the simultaneous learning of multiple information, even compared to deep LSTM that extracts deeper but single information only; 2) significantly faster training time due to the noniterative learning in BLS, compared to LSTM; and 3) easy integration with other discriminant information for further improvement. The proposed methods have been evaluated over 13 real-world datasets from various types of text classification. From the experimental results, the proposed methods achieve higher accuracies than that of LSTM while taking significantly less training time on most evaluated datasets, especially when the LSTM is in deep architecture. Compared to RBLS, GBLS has an extra forget gate to control the flow of information (similar to LSTM) to further improve the accuracy on text classification.
Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in the Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large scale big data-powered artificial intelligence (AI) systems, and machine learning (ML) algorithms and analytics, including performance, availability, privacy, security and trust. Prof. Liu is an elected IEEE Fellow, a recipient of IEEE Computer Society Technical Achievement Award (2012), and a recipient of the best paper award from numerous top venues, including IEEE ICDCS, WWW, ACM/IEEE CCGrid, IEEE Cloud, IEEE ICWS. Prof. Liu served on editorial board of over a dozen international journals, including the editor in chief of IEEE Transactions on Service Computing (2013-2016) and currently, the editor in chief of ACM Transactions on Internet Computing (TOIT). Prof. Liu is a frequent keynote speaker in top-tier venues in Big Data, AI and ML systems and applications, Cloud Computing, Services Computing, Privacy, Security and Trust. Her current research is primarily supported by USA National Science Foundation under CISE programs and IBM.
Title: Adversarial Robustness of Object Detection
Abstract: Deep neural networks (DNN) have fueled the wide deployment of object detection models in a number of mission-critical domains, such as traffic sign detection on autonomous vehicles, and intrusion detection on surveillance systems. Recent studies have revealed that deep object detectors can also be compromised under adversarial attacks, causing a victim detector to detect no object, fake objects, or wrong objects. However, very few studies how to guarantee the robustness of object detection against adversarial manipulations. This keynote presents an in-depth understanding of vulnerabilities of deep object detection systems by analyzing the adversarial robustness under different DNN detector training algorithms, different attack strategies, different adverse effects and costs. Then I will describe a set of strategies and techniques that are effective for developing a robust object detection system and discuss why it is challenging to develop effective mitigation strategies that can protect a victim detector by guaranteeing high model robustness in the presence of adversarial attacks and at the same time maintain high benign model accuracy in no attack scenarios.
Professor Geyong Min is a Chair in High Performance Computing and Networking in the Department of Computer Science at the University of Exeter, UK. His research interests include Computer Networks, Cloud and Edge Computing, Mobile and Ubiquitous Computing, Systems Modelling and Performance Engineering. His recent research has been supported by European Horizon-2020, UK EPSRC, Royal Society, Royal Academy of Engineering, and industrial partners. He has published more than 200 research papers in leading international journals including IEEE/ACM Transactions on Networking, IEEE Journal on Selected Areas in Communications, IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, and IEEE Transactions on Wireless Communications, and at reputable international conferences, such as SIGCOMM-IMC, INFOCOM, and ICDCS. He is an Associated Editor of several international journals, e.g., IEEE Transactions on Computers, and IEEE Transactions on Cloud Computing. He served as the General Chair or Program Chair of a number of international conferences in the area of Information and Communications Technologies.
Title: Big Data Analytics for Smart Network Management
Abstract: The past years have witnessed an explosive growth in the volume of network data driven by the popularization of smart mobile devices and pervasive content-rich multimedia applications, creating a critical issue of Internet traffic flooding. A pressing challenge is how to handle the ever-increasing network traffic and achieve smart network management. To address this challenge, our vision is to conduct efficient data analysis in order to dig valuable knowledge and actionable insights hidden in network big data for improving the design, operation, and management of future Internet. This talk will present innovative big data modelling and processing technologies, real-time data analysis tools, and a cost-effective distributed big data processing platform developed to support intelligent decision-making for system design, anomaly detection, resource management and optimization. This talk will offer the theoretical underpinning for efficient big data analytics and open up a new horizon of research and development by exploiting the key intelligence and insights hidden in content-rich big data for effective design and smart management of Cloud computing and networking systems.
Kalyanmoy Deb is Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA. Prof. Deb's research interests are in evolutionary optimization and their application in multi-criterion optimization, modeling, and machine learning. He has been a visiting professor at various universities across the world including University of Skövde in Sweden, Aalto University in Finland, Nanyang Technological University in Singapore, and IITs in India. He was awarded IEEE Evolutionary Computation Pioneer Award for his sustained work in EMO, Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Distinguished Alumni Award from IIT Kharagpur, Edgeworth-Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research award from Germany. He is fellow of IEEE, ASME, and three Indian science and engineering academies. He has published over 545 research papers with Google Scholar citation of over 145,000 with h-index 121. He is in the editorial board on 18 major international journals. More information about his research contribution can be found from.
Title: Billion-Dimensional Problem Solving and Information Analytics using Computational Intelligence Methods
Hui Lei is Vice President and CTO of AI and Data Infrastructure at Futurewei Technologies. Previously he was CTO of Watson Health Cloud at IBM, an IBM Distinguished Engineer, and an IBM Master Inventor. He has been offered honorary appointments of Visiting Professor or Adjunct Professor at Sun Yat-set University, Hong Kong Polytechnic University, Huazhong University of Science and Technology, Fudan University, and Hong Kong University of Science and Technology. He is a Fellow of the IEEE, a member of the IEEE Computer Society Golden Core, a past Editor-in-Chief of the IEEE Transactions on Cloud Computing, a past Chair of the IEEE Computer Society Technical Committee on Business Informatics and Systems, and an inventor of over 90 patents. He has been recognized with the Edward J. McCluskey Technical Achievement Award for "pioneering contributions to scalable access to real-world data." He received the B.S. degree from Sun Yat-sen University, an M.S. degree from New York University, and a Ph.D. degree from Columbia University, all in Computer Science.
Title: Production AI: Current State and Future Research DirectionsAbstract: We are witnessing a great awakening of AI, thanks to the advances in computer hardware, the breakthroughs in machine learning techniques, and the explosion of digital data. Although many enterprises consider AI strategically important, the adoption of AI for production use has been very slow and only a small portion of companies have revenue-generating AI systems running today. Surprisingly, difficulties in production AI have little to do with core machine learning algorithms and techniques. Instead, they have a lot to do with the huge leap required from the development of machine learning prototypes in lab settings to the development of large-scale enterprise-grade AI systems. In order to adopt AI at scale and reap its full benefits, enterprises must solve problems in a wide variety of areas including infrastructure, data, insights, skills, trust, and operationalization. That, in turn, opens many opportunities for research innovations. In this talk, I will discuss the current state of production AI and sample the associated technical challenges. I will also present some research directions that will advance the state of the art and help unlock AI’s potential to businesses and society.