Irwin King, President of International Neural Network Society, Professor of the Chinese University of Hong Kong

Title: Graph Embedding from Theory to Applications

Abstract
Graph embedding refers to the problem of projecting the elements in a graph, including nodes, edges, substructures, or the whole graph, to a low-dimensional space while preserving the graph's structural information. Graph embedding is an essential technique for analyzing various types of large-scale networks such as social networks, traffic networks, semantic networks, etc. To cope with the growing scale and diversifying structure of modern networks, researchers have proposed novel methods for graph embedding for feature engineering. In this talk, we present the recent advances and future directions in the theoretical development graph embedding and introduce some common applications for social network analysis, such as node classification, link prediction, community detection, and social recommendation.

Bio:
Prof. King's research interests include machine learning, social computing, web intelligence, data mining, and multimedia information processing. In these research areas, he has over 210 technical publications in journals and conferences. In addition, he has contributed over 30 book chapters and edited volumes. Moreover, Prof. King has over 30 research and applied grants. One notable patented system he has developed is the VeriGuide System, previously known as the CUPIDE system, which detects similar sentences and performs readability analysis of text-based documents in both English and in Chinese to promote academic integrity and honesty. Prof. King is the Book Series Editor for "Social Media and Social Computing" with Taylor and Francis. He is also an Associate Editor of the ACM Transactions on Knowledge Discovery from Data and a former Associate Editor of the IEEE Transactions on Neural Networks and IEEE Computational Intelligence Magazine. He is a member of the Editorial Board of the Open Information Systems Journal, Journal of Nonlinear Analysis and Applied Mathematics, and Neural Information Processing-Letters and Reviews Journal. He has also served as Special Issue Guest Editor for Neurocomputing, International Journal of Intelligent Computing and Cybernetics, Journal of Intelligent Information Systems, and International Journal of Computational Intelligent Research. He is a IEEE Fellow and a senior member of ACM. Currently, he is serving the Neural Network Technical Committee and the Data Mining Technical Committee under the IEEE Computational Intelligence Society. He also serves International Neural Network Society as the President. Moreover, he is the General Co-Chair of the WebConf2020, ICONIP2020 and in various capacities in a number of top conferences such as WWW, NIPS, ICML, IJCAI, AAAI, etc. Prof. King is Professor at the Department of Computer Science and Engineering, and a former Associate Dean (Education), Faculty of Engineering at The Chinese University of Hong Kong. He had worked at the AT&T Labs Research, San Francisco and was also teaching Social Computing and Data Mining as a Visiting Professor at UC Berkeley in early 2010s. He received his B.Sc. degree in Engineering and Applied Science from California Institute of Technology, Pasadena and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles.

Sabah Mohammed, Editor in Chief of the IGI Global International Journal of Extreme Automation and Connectivity in Healthcare, Professor of Lakehead University

Title: Research Trend on Developing Smart Remote Patient Monitoring Systems for the COVID-19 Era and Beyond

Abstract
As information technology continues to dramatically evolve, many researchers and strategy decision making believe the Internet of Things (IoT) could play a pivotal role in reshaping industry all together. It is the domino effect wind of change that we need to understand and plan for. This particularly important in the COVID-19 era and beyond where we need to create a robust connected healthcare ecosystem. In healthcare, IoT may just redefine how apps, devices and people interact and connect with one another to deliver healthcare solutions. The benefits are more than obvious. It could help reduce costs, improve outcomes and disease management, and enhance patient experiences. This invited talk will shed light on the current and future trends taking place in the patient monitoring field and the key market drivers that design engineers need to consider.

Bio:
Dr. Sabah Mohammed research interest is in intelligent systems that have to operate in large, nondeterministic, cooperative, highly connected, survivable, adaptive or partially known domains. His continuous research is inspired by his PhD work back in 1981 from Brunel University (UK) on the employment of the Brain Activity Structures for decision making (planning and learning) that enable processes (e.g. agents, mobile objects) and collaborative processes to act intelligently in their environments to timely achieve the required goals. Having trained in medicine with a computer science PhD in Artificial Intelligence (AI), Dr. Mohammed is full Professor at the department of Computer Science at Lakehead University (Ontario Canada) since 2002 and core professor at the BioTechnology program at Lakehead. Dr. Mohammed efforts in establishing healthcare related programs at Lakehead are notable like the specialization Health informatics, BioTechnology and the Bioinformatics programs at Lakehead. With a research background in industry and academia, he has a strong international research reputation for his work on clinical decision support systems supporting remote areas, ubiquitous and extreme environments. Prior to his work at Lakehead University, Dr. Mohammed was the chair of three computer science departments at HCT, Philadelphia and Applied Science Universities. Dr. Mohammed is the Editor in Chief of the of the IGI Global International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH), Associate Editor of the IEEE Access and the founding EiC of the Int. J. of Emerging Technologies of Web Intelligence (JETWI) . He is currently the supervisor of the Smart Health FabLab at Lakehead University. Dr. Mohammed chair the special interest group on Smart and Connected Health with the IEEE ComSoc eHealth TC. Dr. Mohammed is currently working on the development of IoT and mobility technologies that have impact on remote patient monitoring in Rural Areas . Dr. Mohammed is also a Professional Engineer of Ontario, Information Processing Professional with CIPS and Senior Member of IEEE with research supported by major granting organizations like NSERC, MITACS, ONCAT and CFI. More information on Dr. Mohammed can be found on his institution website http://flash.lakeheadu.ca/~mohammed.

Lean Yu, Professor of Beijing University of Chemical Technology, Elsevier Most Cited Chinese Researchers

Title: Time Series Forecasting with Artificial Neural Networks

Abstract
In this talk, artificial neural networks (ANNs) are used as an important prediction tool for time series forecasting. The four main forms using ANNs to predict time series are introduced. In particular, some important issues about data preparation in ANN forecasting are involved in this talk. Finally, some related research progress on ANNs in time series forecasting done by presenters are briefly given.

Bio:
Lean Yu received his Ph.D. degree in Management Sciences and Engineering from Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS) in 2005. He is currently a professor and PhD supervisor of School of Economics and Management, Beijing University of Chemical Technology. He is a winner of National Science Fund for Distinguished Young Scholars, National Program for Support of Top-Notch Young Professionals and "Hundred Talents Program" of Chinese Academy of Sciences. He is acted as a guest editor, managing editor, associate editor and editorial members of many international journals including Computers & Operations Research and Journal of Computer Science. So far, he has published five monographs (two monographs have been published by Springer-Verlag) and over 100 SCI/SSCI articles in some top journals including IEEE Transactions on Evolutionary Computation and IEEE Transactions on Knowledge and Data Engineering. At the same time, he received many awards and honors, such as "Elsevier Most Cited Chinese Researchers" from Elsevier, "China Youth Science and Technology Award" from the Organization Department of the Central Committee of the CPC, "The 100 National Best PhD Theses Award" from Academic Degrees Committee of State Council and Ministry of Education of China, "First Class Prize for Beijing Science and Technology Award" from the Beijing Municipal Government, First Class Prize for Natural Science Award of Ministry of Education (MOE), Beijing Mao Yisheng Youth Science and Technology Award, and "Lu Jia-xi Young Talent Award" of Chinese Academy of Sciences. His research interests include business intelligence, big data mining, economic forecasting and intelligent financial management.