Prof. Nei Kato, Tohoku University, Japan
Fellow of The Engineering Academy of Japan, IEEE, and IEICE
Nei Kato is a full professor (Deputy Dean) with Graduate School of Information Sciences(GSIS) and the Director of Research Organization of Electrical Communication(ROEC), Tohoku University, Japan. He has been engaged in research on computer networking, wireless mobile communications, satellite communications, ad hoc & sensor & mesh networks, UAV networks, smart grid, AI, IoT, Big Data, and pattern recognition. He has published more than 400 papers in prestigious peer-reviewed journals and conferences. He is the Vice-President (Member & Global Activities) of IEEE Communications Society(2018-2021), the Editor-in-Chief of IEEE Transactions on Vehicular Technology(2017-), and the Chair of IEEE Communications Society Sendai Chapter. He served as the Editor-in-Chief of IEEE Network Magazine (2015-2017), a Member-at-Large on the Board of Governors, IEEE Communications Society(2014-2016), a Vice Chair of Fellow Committee of IEEE Computer Society(2016), and a member of IEEE Communications Society Award Committee (2015-2017). He has also served as the Chair of Satellite and Space Communications Technical Committee (2010-2012) and Ad Hoc & Sensor Networks Technical Committee (2014-2015) of IEEE Communications Society. His awards include Minoru Ishida Foundation Research Encouragement Prize(2003), Distinguished Contributions to Satellite Communications Award from the IEEE Communications Society, Satellite and Space Communications Technical Committee(2005), the FUNAI information Science Award(2007), the TELCOM System Technology Award from Foundation for Electrical Communications Diffusion(2008), the IEICE Network System Research Award(2009), the IEICE Satellite Communications Research Award(2011), the KDDI Foundation Excellent Research Award(2012), IEICE Communications Society Distinguished Service Award(2012), IEICE Communications Society Best Paper Award(2012), Distinguished Contributions to Disaster-resilient Networks R&D Award from Ministry of Internal Affairs and Communications, Japan(2014), Outstanding Service and Leadership Recognition Award 2016 from IEEE Communications Society Ad Hoc & Sensor Networks Technical Committee, Radio Achievements Award from Ministry of Internal Affairs and Communications, Japan (2016), IEEE Communications Society Asia-Pacific Outstanding Paper Award(2017), Prize for Science and Technology from the Minister of Education, Culture, Sports, Science and Technology, Japan(2018), Award from Tohoku Bureau of Telecommunications, Ministry of Internal Affairs and Communications, Japan(2018), IEEE Communications Society Green Communications and Computing Technical Committee Distinguished Technical Achievement Recognition Award(2019), and Best Paper Awards from IEEE ICC/GLOBECOM/WCNC/VTC. Nei Kato is a Distinguished Lecturer of IEEE Communications Society and Vehicular Technology Society. He is a fellow of The Engineering Academy of Japan, IEEE, and IEICE.
Speech Title: Deep Learning in Network Traffic Control
Abstract: Recently, the emerging communication technologies and diversified Internet services have made the networks much more complex. Extensive research has illustrated that enabling the network intelligence through the machine learning techniques is an efficient method to address the complex scenarios and reduce manual intervention in management. Among the existing research, deep learning has been widely studied to improve heterogeneous network traffic control which is an important and challenging area. To address the growing traffic demand, we have conducted some pioneering research works in network routing and resource allocation. Series of encouraging results have been achieved in terms of network throughput, average delay, and packet loss rate. In this talk, I will introduce our proposed ideas and focus on the three problems: how to characterize the input and output, how to choose the training manner considering the traffic patterns, and how to construct efficient deep learning architectures. Also, preliminary results will be discussed and I will demonstrate the significant improvement of traffic control for different network scenarios. In addition, can a new intelligent traffic control system be designed for highly dynamic networks, and can self-adjust its own parameters to match the network changes? What can we do to make the deep learning based research more meaningful and practical? I will discuss these issues according to our experience and look toward the future.
IEEE Fellow, Prof. Teng Joon Lim, National University of Singapore, Singapore
Teng Joon (T. J.) Lim (S’92-M’95-SM’02-F’17) obtained the B.Eng. degree in Electrical Engineering with first-class honours from the National University of Singapore (NUS) in 1992, and the Ph.D. degree from the University of Cambridge in 1996. From September 1995 to November 2000, he was a researcher at the Centre for Wireless Communications in Singapore, one of the predecessors of the Institute for Infocomm Research (I2R). From December 2000 to May 2011, he was Assistant Professor, Associate Professor, then Professor at the University of Toronto’s Edward S. Rogers Sr. Department of Electrical and Computer Engineering. Since June 2011, he has been a Professor at the Electrical & Computer Engineering Department of NUS, where he served as a Deputy Head from July 2014 to August 2015. Since September 2015, he has served as Vice-Dean (Graduate Programs) in the NUS Faculty of Engineering.
Professor Lim was an Area Editor of the IEEE Transactions on Wireless Communications from September 2013 to September 2018, and previously served as an Associate Editor for the same journal. He has also served as an Associate Editor for IEEE Wireless Communications Letters, Wiley Transactions on Emerging Telecommunications Technologies (ETT), IEEE Signal Processing Letters and IEEE Transactions on Vehicular Technology. He has volunteered on the organizing committee of a number of IEEE conferences, including serving as the TPC co-chair of IEEE Globecom 2017. He chaired the Singapore chapter of the IEEE Communications Society in 2017 and 2018, and is a Distinguished Lecturer of the IEEE Vehicular Technology Society for 2019-20.
His research interests span many topics within wireless communications, including cyber-security in the Internet of Things, heterogeneous networks, cooperative transmission, energy-optimized communication networks, multi-carrier modulation, MIMO, cooperative diversity, cognitive radio, and stochastic geometry for wireless networks, and he has published widely in these areas.
Speech Title: Statistical and Non-Statistical Machine Learning for Detection of Attacks on IoT NetworksAbstract: IoT devices are, by virtue of their inaccessibility, low energy resources, and always-on nature, more vulnerable to malicious actors aiming to disrupt network operations, eavesdrop, or otherwise attack the network. The detection and mitigation of such attacks are therefore key problems that need to be solved to unlock the full commercial potential of the Internet of Things. In this talk, we will describe the research work that has been carried out in the Singtel-NUS Corporate Laboratory located in the National University of Singapore and partially funded by Singtel, the largest telecommunication service provider in Singapore. The work includes the detection of attacks on wireless relays with the aid of a trusted sentinel, the detection of malware propagation at the early stages of botnet construction, and the detection of network anomalies. Both statistical and non-statistical machine learning techniques are employed in our detection strategies.
IEEE Fellow, Prof. Chin-Chen Chang, Feng Chia University,Taiwan
Professor Chin-Chen Chang obtained his Ph.D. degree in computer engineering from National Chiao Tung University. His first degree is Bachelor of Science in Applied Mathematics and master degree is Master of Science in computer and decision sciences. Both were awarded in National Tsing Hua University. Dr. Chang served in National Chung Cheng University from 1989 to 2005. His current title is Chair Professor in Department of Information Engineering and Computer Science, Feng Chia University, from Feb. 2005. Prior to joining Feng Chia University, Professor Chang was an associate professor in Chiao Tung University, professor in National Chung Hsing University, chair professor in National Chung Cheng University. He had also been Visiting Researcher and Visiting Scientist to Tokyo University and Kyoto University, Japan. During his service in Chung Cheng, Professor Chang served as Chairman of the Institute of Computer Science and Information Engineering, Dean of College of Engineering, Provost and then Acting President of Chung Cheng University and Director of Advisory Office in Ministry of Education, Taiwan. Professor Chang's specialties include, but not limited to, data engineering, database systems, computer cryptography and information security. A researcher of acclaimed and distinguished services and contributions to his country and advancing human knowledge in the field of information science, Professor Chang has won many research awards and honorary positions by and in prestigious organizations both nationally and internationally. He is currently a Fellow of IEEE and a Fellow of IEE, UK. And since his early years of career development, he consecutively won Institute of Information & Computing Machinery Medal of Honor, Outstanding Youth Award of Taiwan, Outstanding Talent in Information Sciences of Taiwan, AceR Dragon Award of the Ten Most Outstanding Talents, Outstanding Scholar Award of Taiwan, Outstanding Engineering Professor Award of Taiwan, Chung-Shan Academic Publication Awards, Distinguished Research Awards of National Science Council of Taiwan, Outstanding Scholarly Contribution Award of the International Institute for Advanced Studies in Systems Research and Cybernetics, Top Fifteen Scholars in Systems and Software Engineering of the Journal of Systems and Software, Top Cited Paper Award of Pattern Recognition Letters, and so on. On numerous occasions, he was invited to serve as Visiting Professor, Chair Professor, Honorary Professor, Honorary Director, Honorary Chairman, Distinguished Alumnus, Distinguished Researcher, Research Fellow by universities and research institutes. He also published over serval hundred papers in Information Sciences. In the meantime, he participates actively in international academic organizations and performs advisory work to government agencies and academic organizations.
Prof. Junyu Dong, Ocean University of China, China
Junyu Dong received his BSc and MSc from the Department of Applied Mathematics at Ocean University of China in 1993 and 1999 respectively, and received his PhD in 2003 from Heriot-Watt University, UK. He is currently a professor and the Vice Dean of College of Information Science and Technology in Ocean University of China. His research interests include computer vision and machine learning with applications to oceanographic data analysis. Junyu Dong is and has been the principal investigator of 7 research projects supported by Natural Science Foundation of China (NSFC) and Ministry of Science and Technology (MOST). He has published more than 80 journal papers. He is currently the Chairman of Qingdao Sector of China Computer Federation, as well as the Chairman of Qingdao Sector of Association of Computing Machinery.
Speech Title: From Time Series Data Analysis to 3D reconstruction: Application of Deep Learning
Abstract: Deep learning has been commonly used in computer vision in the past few years, especially for image classification. However, there are also some other areas in which deep learning is playing a more and more important role. In this talk, I will present two example areas of application of deep learning, i.e. time series data analysis and 3D reconstruction. These two research areas have been extensively studied using mathematical or physical models. We particularly focus on sea surface temperature (SST) analysis and 3D seabed reconstruction as they are very important for ocean environment: 1. Cloud removal from satellite images based on GAN; 2. Prediction of Sea Surface Temperature (SST), and 3. Accurate and dense 3D reconstruction including sea floor. We formulate prediction of Sea Surface Temperature (SST) as a time series regression problem, and dense 3D reconstruction of seabed is achieved by combining SLAM with photometric stereo, in which deep learning is also involved.