Prof. Nikola Kasabov, IEEE Fellow and RSNZ Fellow, Auckland University of Technology, New Zealand
Bio: Nikola Kasabov
is Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the
INNS College of Fellows, DVF of the Royal Academy of Engineering UK. He is
the Founding Director of the Knowledge Engineering and Discovery Research
Institute (KEDRI), Auckland and Professor at the School of Engineering,
Computing and Mathematical Sciences at Auckland University of Technology,
New Zealand. He also holds the George Moore Chair Professor of data
analytics at the University of Ulster UK and Honorary Professorships at the
Teesside University UK and the University of Auckland NZ. Kasabov is a Past
President of the Asia Pacific Neural Network Society (APNNS) and Past
President of the International Neural Network Society (INNS). He is member
of several technical committees of IEEE Computational Intelligence Society
and Distinguished Lecturer of IEEE (2012-2014). He is Editor of Springer
Handbook of Bio-Neuroinformatics, Springer Series of Bio-and Neuro-systems
and EIC of Springer journal Evolving Systems. He is Associate Editor of
several journals, including Neural Networks, IEEE TrNN, Tr CDS, Information
Sciences, Applied Soft Computing. Kasabov holds MSc and PhD from TU Sofia,
Bulgaria. His main research interests are in the areas of neural networks,
intelligent information systems, soft computing, bioinformatics,
neuroinformatics. He has published more than 650 publications. He has
extensive academic experience at various academic and research organisations
in Europe and Asia, including: TU Sofia Bulgaria; University of Essex UK;
University of Otago, NZ; Advisory Professor at Shanghai Jiao Tong University
and CASIA Beijing, Visiting Professor at ETH/University of Zurich. Prof.
Kasabov has received a number of awards, among them: Doctor Honoris Causa
from Obuda University, Budapest; INNS Ada Lovelace Meritorious Service
Award; NN Best Paper Award for 2016; APNNA ‘Outstanding Achievements Award’;
INNS Gabor Award for ‘Outstanding contributions to engineering applications
of neural networks’; EU Marie Curie Fellowship; Bayer Science Innovation
Award; APNNA Excellent Service Award; RSNZ Science and Technology Medal;
2015 AUT Medal; Honorable Member of the Bulgarian, the Greek and the
Scottish Societies for Computer Science. He has supervised to completion
more than 50 PhD students. More information of Prof. Kasabov can be found
from: https://academics.aut.ac.nz/nkasabov.
Prof. Deshuang Huang, IAPR Fellow, IEEE Fellow, Institute of Machine Learning and Systems Biology, Tongji University, China
Bio: De-Shuang Huang is a Professor in Big Data and Intelligent Computing Research Center, Guangxi Academy of Science,China and Department of Computer Science and Director of Institute of Machine Learning and Systems Biology at Tongji University, China. He is currently the Fellow of the International Association of Pattern Recognition (IAPR Fellow), Fellow of the IEEE (IEEE Fellow) and Senior Member of the INNS, Bioinformatics and Bioengineering Technical Committee Member of IEEE CIS, Neural Networks Technical Committee Member of IEEE CIS, the member of the INNS, Co-Chair of the Big Data Analytics section within INNS, and associated editors of IEEE/ACM Transactions on Computational Biology & Bioinformatics, and Neural Networks, etc. He founded the International Conference on Intelligent Computing (ICIC) in 2005. ICIC has since been successfully held annually with him serving as General or Steering Committee Chair. He also served as the 2015 International Joint Conference on Neural Networks (IJCNN 2015) General Chair, July 12-17, 2015, Killarney, Ireland, the 2014 11th IEEE Computational Intelligence in Bioinformatics and Computational Biology Conference (IEEE-CIBCBC) Program Committee Chair, May 21-24, 2014, Honolulu, USA. He has published over 470 papers in international journals, international conferences proceedings, and book chapters. Particularly, he has published over 230 SCI indexed papers. His Google Scholar citation number is over 18900 times and H index 73. His main research interest includes neural networks, pattern recognition and bioinformatics.
Prof. Feifei Gao, IEEE Fellow, Tsinghua University, ChinaA
Bio: Feifei Gao, Associate Professor, IEEE Fellow, Department
of Automation, Tsinghua University, China. Prof. Gao's research interest
include signal processing for communications, array signal processing,
convex optimizations, and artificial intelligence assisted communications.
He has authored/ coauthored more than 150 refereed IEEE journal papers and
more than 150 IEEE conference proceeding papers that are cited more than
10000 times in Google Scholar. Prof. Gao has served as an Editor of IEEE
Transactions on Communications, IEEE Transactions on Wireless
Communications, IEEE Journal of Selected Topics in Signal Processing (Lead
Guest Editor), IEEE Transactions on Cognitive Communications and Networking,
IEEE Signal Processing Letters, IEEE Communications Letters, IEEE Wireless
Communications Letters, and China Communications. He has also served as the
symposium co-chair for 2019 IEEE Conference on Communications (ICC), 2018
IEEE Vehicular Technology Conference Spring (VTC), 2015 IEEE Conference on
Communications (ICC), 2014 IEEE Global Communications Conference (GLOBECOM),
2014 IEEE Vehicular Technology Conference Fall (VTC), as well as Technical
Committee Members for more than 50 IEEE conferences.
Speech Title: Deep Learning for Physical Layer Communications: An Attempt towards 6G
Abstract: Merging artificial intelligence in to the system design has appeared as a new trend in wireless communications areas and has been deemed as one of the 6G technologies. In this talk, we will present how to apply the deep neural network (DNN) for various aspects of physical layer communications design, including the channel estimation, channel prediction, channel feedback, data detection, and beamforming, etc. We will also present a promising new approach that is driven by both the communications data and the communication models. It will be seen that the DNN can be used to enhance the performance of the existing technologies once there is model mismatch. More interestingly, we will show that applying DNN can deal with the conventionally unsolvable problems, thanks to the universal approximation capability of DNN. With the well-defined propagation model in communication areas, we also attempt to explain the DNN under the scenario of channel estimation and reach a strong conclusion that DNN can always provide the asymptotically optimal channel estimations. We have also build test-bed to show the effectiveness of the AI aided wireless communications. In all, DNN is shown to be a very powerful tool for communications and would make the communications protocols more intelligently. Nevertheless, as a new born stuff, one should carefully select suitable scenarios for applying DNN rather than simply spreading it everywhere.