Keynote Speakers
 

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Speaker: Prof. Ling Liu, IEEE Fellow, Georgia Institute of Technology (USA)

Bio:  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: Brain-inspired Computation for Advanced Image Processing and Computer Vision 

Abstract: This talk will first introduce randomization-based neural networks. Subsequently, the origin of randomization-based neural networks will be presented. The popular instantiation of the feedforward model called random vector functional link neural network (RVFL) originated in the early-1990s. Other randomized feedforward models that will be briefly mentioned are random weight neural networks (RWNN), extreme learning machines (ELM), stochastic configuration network (SCN), broad learning system (BLS), etc. Recently developed deep implementations of the RVFL will be presented in detail. The talk will also include extensive benchmarking studies using tabular classification datasets.

Speaker: Prof. Nikola Kasabov , IEEE Fellow and RSNZ Fellow, Auckland University of Technology, New Zealand
 

Bio:  Professor 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.

Title: Randomization Based Deep and Shallow Learning for Classification


Abstract: This talk will first introduce randomization-based neural networks. Subsequently, the origin of randomization-based neural networks will be presented. The popular instantiation of the feedforward model called random vector functional link neural network (RVFL) originated in the early-1990s. Other randomized feedforward models that will be briefly mentioned are random weight neural networks (RWNN), extreme learning machines (ELM), stochastic configuration network (SCN), broad learning system (BLS), etc. Recently developed deep implementations of the RVFL will be presented in detail. The talk will also include extensive benchmarking studies using tabular classification datasets.
 
Speaker: Prof. Ponnuthurai Nagaratnam Suganthan, IEEE Fellow, Nanyang Technological University, Singapore

Bio:  Ponnuthurai Nagaratnam Suganthan received the B.A degree, Postgraduate Certificate and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK in 1990, 1992 and 1994, respectively. He received an honorary doctorate (i.e. Doctor Honoris Causa) in 2020 from University of Maribor, Slovenia. After completing his PhD research in 1995, he served as a pre-doctoral Research Assistant in the Dept of Electrical Engineering, University of Sydney in 1995–96 and a lecturer in the Dept of Computer Science and Electrical Engineering, University of Queensland in 1996–99. He moved to Singapore in 1999. He was an Editorial Board Member of the Evolutionary Computation Journal, MIT Press (2013-2018). He is/was an associate editor of the Applied Soft Computing (Elsevier, 2018-), Neurocomputing (Elsevier, 2018-), IEEE Trans on Cybernetics (2012 - 2018), IEEE Trans on Evolutionary Computation (2005 - ), Information Sciences (Elsevier) (2009 - ), Pattern Recognition (Elsevier) (2001 - ) and IEEE Trans on SMC: Systems (2020 - ) Journals. He is a founding co-editor-in-chief of Swarm and Evolutionary Computation (2010 - ), an SCI Indexed Elsevier Journal. His co-authored SaDE paper (published in April 2009) won the "IEEE Trans. on Evolutionary Computation outstanding paper award" in 2012. His former PhD student, Dr Jane Jing Liang, won the IEEE CIS Outstanding PhD dissertation award, in 2014. His research interests include randomization-based learning methods, swarm and evolutionary algorithms, pattern recognition, deep learning and applications of swarm, evolutionary & machine learning algorithms. He was selected as one of the highly cited researchers by Thomson Reuters Science Citations yearly from 2015 to 2020 in computer science. He served as the General Chair of the IEEE SSCI 2013. He has been a member of the IEEE (S'90, M'92, SM'00, Fellow 2015) since 1991 and an elected AdCom member of the IEEE Computational Intelligence Society (CIS) in 2014-2016. He is an IEEE CIS distinguished lecturer (DLP) in 2018-2021.
 

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Speaker: Prof. Andrew Sung, The University of Southern Mississippi, USA

Bio:  Andrew H. Sung received B.S. degree in Electrical Engineering from National Taiwan University, M.S. degree in Mathematical Sciences from University of Texas at Dallas, and Ph.D. degree in Computer Science from State University of New York at Stony Brook. He has been a faculty of Computer Science, Computer Engineering, Information Technology, and Management in three public research universities in the U.S. for over 30 years. He has supervised more than a dozen Ph.D. students and has served as the PI or co-PI of dozens of grants and contracts funded by government agencies and industry, with total funding of more than ten million USD. Since 2014, he has been the Director and Professor of the School of Computing Sciences and Computer Engineering at the University of Southern Mississippi.
Dr. Sung has over 250 publications and his current research interest includes computational intelligence, big data analytics and mining, cybersecurity, multimedia forensics, and blockchain. He has delivered keynote, plenary, and invited lectures in several international conferences and workshops in the U.S., Europe, South America, and India.