Keynote Speaker 1

Emeritus Professor Ian Witten

  • Emeritus Professor (Computer Science), The University of Waikato

Specialization: Programming by example, text compression, machine learning, data mining, digital libraries, interactive systems.

Bibliography: TBA

Title: Big data, deep learning, and Weka

Abstract: When is data “big”? We examine this question with reference to the popular Weka interactive data mining system. The widely used Explorer interface is limited by the fact that datasets must fit into main memory. However, Weka also has facilities that transcend this limitation and can learn from effectively unlimited datasets – which requires machine learning methods that operate incrementally, in one pass through the data. Weka includes incremental implementations of standard classifiers. Its Knowledge Flow and command line interfaces can be used on datasets of any size. Moa, Weka’s big sister, is expressly designed to work on unlimited data streams, and includes suitable data generators and evaluation methods. Distributed Weka allows Weka to operate on multiprocessor clusters based on either the Hadoop or Spark architectures. We also survey what has been called the “deep learning renaissance”: the application of high capacity networks to overwhelmingly large quantities of data, particularly in areas of image recognition, face recognition, and language processing. High-speed GPU implementations are critical to the success of these techniques. Weka supports deep learning with a classifier that applies Deeplearning4j, an open source program library that includes distributed parallel versions – and the ability to operate on a GPU. This Weka facility is unique in that you can train a deep learning network without writing code. The aim is to defy the Oxford English Dictionary’s definition of big data as “data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges.”

Keynote Speaker 2

Professor Limsoon wong

  • KITHCT Professor of Computer Science, School of Computing, National University of Singapore,
  • Professor of Pathology, School of Medicine, National University of Singapore,
  • Leader, Bioinformatics Programme, NUS Office of Life Sciences,
  • Coordinator, Computational Biology Lab, NUS School of Computing,
  • Faculty Member, Graduate School for Integrative Sciences and Engineering, National University of Singapore.

Specialization: Knowledge discovery technologies and their application to biomedicine

Bibliography: Limsoon Wong is a KITHCT chair professor in the School of Computing and a professor in the Yong Loo Lin School of Medicine at the National University of Singapore. Before that, he was the Deputy Executive Director for Research at A*STAR's Institute for Infocomm Research. He currently works mostly on knowledge discovery technologies and their application to biomedicine. He has also done, especially in the earlier part of his career, significant research in database query language theory and finite model theory, as well as significant development work in broad-scale data integration systems. Limsoon has written about 250 research papers, some of which are among the best cited of their respective fields. He is a Fellow of the ACM, named in 2013 for his contributions to database theory and computational biology. Some of his other recent awards include the 2003 FEER Asian Innovation Gold Award for his work on treatment optimization of childhood leukemias, the 2006 Singapore Youth Award Medal of Commendation for his sustained contributions to science and technology, and the ICDT 2014 Test of Time Award for his work on naturally embedded query languages. Limsoon was also conferred, in 2014, a Public Administration Medal (Bronze) by the Singapore Government for outstanding efficiency, competence, and industry. He serves/served on the editorial boards of Journal of Bioinformatics and Computational Biology, Bioinformatics, Biology Direct, Drug Discovery Today, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Genomics Proteomics & Bioinformatics, Journal of Biomedical Semantics, Methods, Scientific Reports, Information Systems, and IEEE Transactions on Big Data. He is also an ACM Books Area Editor. He received his BSc(Eng) in 1988 from Imperial College London and his PhD in 1994 from University of Pennsylvania.

Title: TBA

Abstract: TBA

Keynote Speaker 3

Professor Wai Kiang (Albert) Yeap

  • Professor of Artificial Intelligence, Auckland University of Technology, Auckland, New Zealand
  • Director of Centre for Artificial Intelligence Research
    Auckland University of Technology, Auckland, New Zealand

Specialization: Artificial Intelligence - Space and Language

Bibliography: Professor Wai Kiang Yeap has strong interests in developing computational models of cognitive processes and in particular models for spatial cognition, language and infant learning. He did his PhD at the University of Essex in 1984. He joined the University of Otago in 1985 and moved to the Auckland University of Technology in 2000 where he is currently a Professor in AI and the Director for the Centre for AI Research. Recently, he has been a keynote speaker at the Pacific Rim International Conference on AI in 2014 and a HWK Fellow at the Institute for Advanced Study at Delmenhorst, Germany in 2012. He is also a member of the Editorial Board for Spatial Cognition and Computation Journal. 

TitleThe Mind Modelling Conundrum (and a solution using robots)

Abstract: To understand how the mind works, we need to develop computational models of various mental processes. However, developing them, one faces a conundrum: how does one create models for these processes if one does not know what they compute? For example, in spatial cognition, it is argued that what is learned is a map of one's environment but the nature of such a map has remained elusive. In language, it is well known that we acquire the rules that govern its use but these rules appear unlearnable by infants. In this talk, I will discuss this conundrum in depth and, using my recently developed computational theory of spatial cognition as an example, I will outline a solution that involves empowering a robot with a “mental” process and studying its behaviour. Would such an approach pose an ethical dilemma?

Keynote Speaker 4

Dr Mohammad Reza Beik Zadeh 

  • Big Data Analytic Consultant

Specialization: Artificial Intelligence - Machine Learning, Data Mining, Semantic Technology, Prediction and Planning

Bibliography: An experienced researcher in the fields of artificial intelligence and computer science. An inspiring team leader in international R&D Organizations in the field of ICT. A dedicated lecturer and an innovative researcher in Artificial Intelligence, Semantic Technology, Big Data Analytics. A Data Scientist.

From 2012-2017, he has been involved in teaching, advanced research in the field of machine learning, genetic algorithm, and fuzzy logic as well as conducting workshops and proposing research centre of excellences in different universities in Kuala Lumpur. He, as a data scientist, has consulted several projects related to Big Data analytics projects and Big Data analytics applications for Malaysian government 4 Big Data projects under MAMPU (price watch, crime watch, HFMD prediction, Sentiment Analysis for patriotism). In these projects, he has been mostly involved in data analysis, data modelling, prediction modelling, visual analytics, etc. Recently, he has proposed Big Data CoEs to 4 universities (MMU,UM,UTM,UKM) in order to support bi data research and development as well as academic and technical training.

TitleCognitive Cars: convergence of IoT, Machine Learning and Big Data Analytics

Abstract: An intelligent and intuitive vehicle is characterized by self-driving and self-healing capabilities, but it can also be defined by self-integrating, self-configuring, self-learning and self-socializing capabilities.  Car-to-car communication could help drivers avoid accidents by providing warnings and notification regarding the obstacles detected on the road. Understanding road and surrounding cars' situation enable each care to react intelligently to the environment. One solution for unifying disparate cognitive technologies into existing vehicles, and into the design of future vehicles, is to build cognitive technologies into an Internet of Things device for personalized experiences. In this presentation the main  challenges and complexities related to design of a cognitive car will be highlighted and the roles of Artificial Intelligent related technologies to tackle the said challenges and complexities will explained.