Prof. David Zhang, Fellow of IEEE and
Chinese University of Hong Kong, Shenzhen, China
David Zhang graduated in Computer Science from Peking University. He received his MSc in 1982 and his PhD in 1985 in both Computer Science from the Harbin Institute of Technology (HIT), respectively. From 1986 to 1988 he was a Postdoctoral Fellow at Tsinghua University and then an Associate Professor at the Academia Sinica, Beijing. In 1994 he received his second PhD in Electrical and Computer Engineering from the University of Waterloo, Ontario, Canada.
He has been a Chair Professor at the Hong Kong Polytechnic University where he is the Founding Director of Biometrics Research Centre (UGC/CRC) supported by the Hong Kong SAR Government since 2005. Currently he is Presidential Chair Professor in Chinese University of Hong Kong (Shenzhen). He also serves as Visiting Chair Professor in Tsinghua University and HIT, and Adjunct Professor in Shanghai Jiao Tong University, Peking University, National University of Defense Technology and the University of Waterloo. He is both Founder and Editor-in-Chief, International Journal of Image & Graphics (IJIG) (http://www.worldscinet.com/ijig/ijig.shtml) and Springer International Series on Biometrics (KISB)(http://www.springer.com/series/6191); Organizer, the first International Conference on Biometrics Authentication (ICBA); and Associate Editor of more than ten international journals including IEEE Transactions and so on. Over past 30 years, he have been working on pattern recognition, image processing and biometrics, where many research results have been awarded and some created directions, including palmprint recognition, computerized TCM and facial beauty analysis, are famous in the world. So far, he has published over 20 monographs, 400 international journal papers and 40 patents from USA/Japan/HK/China. He has been continuously listed as a Highly Cited Researchers in Engineering by Clarivate Analytics (formerly known as Thomson Reuters) in 2014, 2015, 2016, 2017 and 2018, respectively(http://highlycited.com).
He is also ranted about 80 with H-Index 103 at Top 1000 Scientists for international Computer Science and Electronics(http://www.guide2research.com/scientists). Professor Zhang is a Croucher Senior Research Fellow, Distinguished Speaker of the IEEE Computer Society, and a Fellow of both IEEE and IAPR.
Prof. Saman Halgamuge, Fellow of IEEE
The University of Melbourne, Australia
SAMAN HALGAMUGE, FIEEE is a Fellow of Institute of Electrical and Electronics Engineering (IEEE), USA, and a Distinguished Lecturer/Speaker appointed by IEEE in the area of Computational Intelligence. He is currently a Professor in the Department of Mechanical Engineering, School of Electrical, Mechanical and Infrastructure Engineering at the University of Melbourne, an honorary Professor of School of Electrical, Energy and Materials Engineering at Australian National University (ANU). He was previously the Director of the Research School of Engineering at the Australian National University (2016-18) and held Professor, Associate Dean International, Associate Professor and Reader and Senior Lecturer positions at University of Melbourne (1997-2016). He graduated with Dipl.-Ing and PhD degrees in Data Engineering from Technical University of Darmstadt, Germany and B.Sc Engineering degree in Electronics and Telecommunication from University of Moratuwa, Sri Lanka.
His research interests are in Machine Learning including Deep Learning, Big Data Analytics and Optimization and their applications in Energy, Mechatronics, Bioinformatics and Neuro-Engineering. His fundamental research contributions are in Unsupervised and Near Unsupervised type learning as well as in transparent Deep Learning and Bioinspired Optimization. His h-index is 42 (9100 citations) in Google Scholar and he graduated 50 PhD students as the primary supervisor. He has also been a keynote speaker for 40 research conferences. In China, he presented research seminars at many research institutions including Chinese Academy of Sciences, Tsinghua University, Peking University, Shanghai Jiao Tong University, Harbin Institute of Technology and Tong Ji University. He is also invited to serve as a Distinguished Professor at multiple Universities in China.
Title--Supervised and Unsupervised Deep Learning
Abstract--World saw the first image of a blackhole in April 2019 which will not be the last extraordinary image we see due to the vast advancement in data visualization happening today. The global technology landscape is undergoing a dramatic shift towards an exciting space of overwhelmingly complex and abundant data. Being prepared for this reality is paramount; however, it is quickly becoming apparent that new innovative methods are required to leverage the kind of “wicked” datasets we are increasingly confronted with. We are already witnessing this paradigm shift in wide-ranging domains such as neural engineering, pharmaceutical drug development, and microbial ecology, which are empowered by rapidly-advancing technologies that can quickly generate terabytes of data for analysis of advanced processes, compounds and organisms. These technologies have been spurred by recent advances in Deep Learning coupled with improvements in processor technology (e.g. GPU), that have allowed practitioners and researchers to overcome the computational limitations of many Neural Networks that depend on fully human curated (i.e. labeled) data (i.e. Supervised Learning). The following fundamental question then naturally arises: What happens when curated information or labels capture only a subset of critical classes, or the curation process itself is not fault- or error-free, i.e., a presence of uncertainty, as is often the case in the aforementioned domains? Undoubtedly, the algorithm’s perceived reality will distort any subsequent analysis of these data, which may have detrimental downstream effects when new discoveries and critical decisions are made on a basis of these analyses.
In such scenarios, learning algorithms that can find models –underlying structures or distinct patterns within data – without relying on labels (i.e. using Unsupervised Learning), have made great progress toward answering these sorts of questions; however, these algorithms only address part of the problem. Unsupervised Learning algorithms do not take into account any available and potentially reliable information or domain knowledge, which could prove useful in developing a robust model of the data. It can be advantageous to consider such information as well as any other available domain knowledge, not as ground truth but as a starting point to build a more complete picture of the problem under investigation.
The frequently used learning strategies also include generative techniques: Variational Autoencoders and Generative adversarial nets (GANs) that are widely used to learn the data sampling process. The performance of GANs and their future applications heavily depend on the improvements to learning algorithm.
Prof. Maode Ma, Fellow of IET
Nanyang Technological University, Singapore
Prof. Maode Ma, a Fellow of IET, received his Bachelor degree from Department of Computer Science and Technology in Tsinghua University in 1982, his Master degree from Department of Computer Science and Technology in Tianjin University in 1991, and his Ph.D. degree in Department of Computer Science from Hong Kong University of Science and Technology in 1999. Now, Dr. Ma is a tenured Associate Professor in the School of Electrical and Electronic Engineering at Nanyang Technological University in Singapore. He has extensive research interests including network security and wireless networking. He has led 25 research projects funded by government, industry, military and universities in various countries. He has supervised over 20 research students to get their Ph. D degree. He has been a conference chair, technical symposium chair, tutorial chair, publication chair, publicity chair and session chair for over 100 international conferences. He has been a member of the technical program committees for more than 200 international conferences. Dr. Ma has more than 400 international academic publications including about 200 journal papers and more than 200 conference papers. He has edited 4 technical books and produced over 25 book chapters. His publication has received more than 4500 citation in Google Scholar. He has delivered about 70 keynote speeches and 10 tutorials at various international conferences. He currently serves as the Editor-in-Chief of International Journal of Computer and Communication Engineering, Journal of Communications and International Journal of Electronic Transport. He also serves as a Senior Editor for IEEE Communications Surveys and Tutorials, and an Associate Editor for International Journal of Security and Communication Networks, International Journal of Wireless Communications and Mobile Computing and International Journal of Communication Systems. He had been an Associate Editor for IEEE Communications Letters from 2003 to 2011. Dr. Ma is a senior member of IEEE Communication Society and IEEE Education Society, and a member of ACM. He is now the Secretary of the IEEE Singapore Section and the Chair of the ACM, Singapore Chapter. He has served as an IEEE Communication Society Distinguished Lecturer from 2013 to 2016.
Title: A Secure and Efficient Fast Initial Link Setup Scheme against Key Reinstallation Attacks
Abstract--With the increasing demands for secure wireless link connections to the access points (APs) supporting large quantities of devices in wireless local networks (WLANs), the Fast Initial Link Setup (FILS) is a recently standardized approach specified in IEEE 802.11ai. It is a new amendment to IEEE 802.11 standard family to support massively deployed wireless nodes. However, security concerns on the link connection have not been fully eliminated, especially for the authentication process. For example, a type of recently revealed malicious attack, Key Reinstallation Attack (KRA) might be a threat to the FILS authentication. To prevent the success of the KRAs, in this talk, I will introduce the FILS scheme and present a novel protocol named as Secure and Efficient FILS (SEF) protocol as the optional substitute. The SEF is designed to eradicate potential threats from the KRAs without degrading the network performance.
Prof. Guo Song,
The Hong Kong Polytechnic University, Hong Kong
Song Guo received his Ph.D. in computer science from University of Ottawa. He is currently a full professor at Department of Computing, The Hong Kong Polytechnic University. Prior to joining PolyU, he was a full professor with the University of Aizu, Japan. His research interests are mainly in the areas of cloud and green computing, big data, wireless networks, and cyber-physical systems. He has published over 300 conference and journal papers in these areas and received multiple best paper awards from IEEE/ACM conferences. His research has been sponsored by JSPS, JST, MIC, NSF, NSFC, and industrial companies. Dr. Guo has served as an editor of several journals, including IEEE Transactions on Parallel and Distributed Systems (2011-2015), IEEE Transactions on Emerging Topics in Computing (2013-), IEEE Transactions on Green Communications and Networking (2016-), IEEE Communications Magazine (2015-), and Wireless Networks (2013-). He has been actively participating in international conferences as general chair and TPC chair. He is a senior member of IEEE, a senior member of ACM, and an IEEE Communications Society Distinguished Lecturer.
Prof. Hesheng Wang,
Shanghai Jiao Tong University, China
Hesheng Wang received the Ph.D. degree in Automation & Computer-Aided Engineering from Chinese University of Hong Kong. Currently, he is a Professor of Department of Automation, Shanghai Jiao Tong University, China. He worked as a visiting professor at University of Zurich in Switzerland. His research interests include visual servoing, service robot, robot control and artificial intelligent. He has published more than 100 papers in refereed journals and conferences. He has received a number of best paper awards from major international conferences in robotics and automation. He is an associate editor of Assembly Automation, International Journal of Humanoid Robotics and IEEE Transactions on Robotics. He was the general chair of IEEE RCAR2016 and program chair of IEEE AIM2019 and IEEE ROBIO2014. He was a recipient of Shanghai Rising Star Award in 2014, The National Science Fund for Outstanding Young Scholars in 2017 and Shanghai Shuguang Scholar in 2019. He is a Senior Member of IEEE.
Title: Visual servoing of Robots
Abstract--Visual servoing is an important technique that uses visual information for the feedback control of robots. By directly incorporating visual feedback in the dynamic control loop, it is possible to enhance the system stability and the control performance. Dynamic visual servoing is to design the joint inputs of robot manipulators directly using visual feedback. In the design, the nonlinear dynamics of the robot manipulator is taken into account. In this talk, various visual servoing approaches will be presented to work in uncalibrated environments. These methods are also implemented in many robot systems such as manipulator, mobile robot, soft robot, quadrotor and so on.