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Keynote Speakers of ISCMI2018

Prof. Witold Pedrycz

University of Alberta, Canada

Biography: Witold Pedrycz (IEEE Fellow, 1998) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society.
His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data science, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 16 research monographs and edited volumes covering various aspects of Computational Intelligence, data mining, and Software Engineering.
Dr. Pedrycz is vigorously involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Int. J. of Granular Computing (Springer). He serves on an Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of international journals.

Title of Speech: System Modeling: At the Junction with Data Analytics  

Abstract: The apparent challenges encountered in system modeling inherently associate with large volumes of data, data variability, and an evident quest for transparency and interpretability of established constructs and obtained results. Along with the emergence and increasing visibility and importance of data analytics, we start to witness a paradigm shift where several dominant tendencies become apparent: (i) reliance on data and building structure-free and versatile models spanned over selected representatives of experimental data, (ii) emergence of models at various levels of abstraction, and (iii) building a collection of individual local models and supporting their efficient aggregation.
We advocate that information granules play a pivotal role in the realization of this paradigm shift. We demonstrate that a framework of Granular Computing along with a diversity of its formal settings offers a critically needed conceptual and algorithmic environment. Information granules and information granularity are synonyms of levels of abstraction. A suitable perspective built with the aid of information granules is advantageous in realizing a suitable level of abstraction and becomes instrumental when forming sound, practical problem-oriented tradeoffs among precision of results, their easiness of interpretation, value, and stability (as lucidly articulated in the form of the principle of incompatibility coined by Zadeh). All those aspects emphasize importance of actionability and interestingness of the produced findings either for purpose of control or decision-making. Granular models built on a basis of available numeric models deliver a comprehensive view at the real-world systems. More specifically, granular spaces, viz. spaces of granular parameters of the models and granular input and output spaces play a pivotal role in making the original numeric models more realistic.
The data-oriented models tend to depart from analytical descriptions (in the form of nonlinear mappings) but directly exploit subsets of meaningful/representative data over which such models are developed. A representative class of models with this regard concerns associative memories, which realize both one-directional and bidirectional recall (mapping). We carefully revisit and augment the concept of associative memories by proposing some new design directions. We focus on the essence of structural dependencies in the data and make the corresponding associative mappings spanned over a related collection of landmarks (representatives od data). We show that a construction of such landmarks is supported by mechanisms of collaborative fuzzy clustering. In the sequel, structural generalizations of the discussed architectures to multisource and multi-directional memories involving associative mappings among various data spaces are proposed and their design is discussed.



Prof. Mammo Muchie

Tshwane University of Technology, South Africa

Biography: Professor Mammo Muchie did his undergraduate degree in Columbia University, New York, USA and his postgraduate MPhil and DPhil in Science, Technology, and Innovation for Development (STI&D) from the University of Sussex, UK. He is currently a DST-NRF research chair in Innovation Studies at the Faculty of Management Sciences, Tshwane University of Technology and a rated research Professor. He is a fellow of the South African Academy of Sciences, the African Academy of Sciences and the African Science Institute. He is also currently adjunct Professor at the Adama Science, Technology University, Arsi University, Addis Ababa University and University of Gondar, Ethiopia. He is a faculty associate professor at SPRU and Senior Research Associate at the TMD Centre of Oxford University. He was part of the founding members of the Globelics initiative and participates as board member and actively contributes to the Globelics Doctoral Academy. He is the founder as Chief Editor of the African Journal on Science, Technology, Innovation and Development that has been running since 2009 ( He is also editor of the Globelics Journal of Innovation and Development ( He helped also in the founding of AfricaLics ( He is part of the founding scientific board members of the network that connects North Africa, with the Middle East and southern Europe ( and Indialics. He is scientific advisor of the Africa Innovation Summit. Perhaps one of the most significant contributions to promote the emerging field on innovation studies in Africa was the South African research Chairs Initiative (SARChI). The first chair on innovation studies supported by the DST/NRF in South Africa was awarded to Prof. Muchie to promote doctoral and post-doctoral research in Africa. He is chairman of the Network of Ethiopian scholars ( and is chief editor of the open access electronic journal The Ethiopian Electronic Journal for Research & Innovation Foresight (Ee-JRIF) ( He was a co- founder of The GKEN-Global Knowledge Exchange Network, the African Unity for Renaissance, the African Talent hub and the African Union Youth for Change (AUY4C). He is also editor of the Journal of Agriculture and Economic Development, Associate Editor of the Journal of Economics and Institutions, University of Malaysia, Journal of Social Epistemology, and many others. He is also an editorial member of the Thinker Magazine and he is also the chief editor of a new TUT Journal of Creativity, Innovation and Social Entrepreneurship (JCISE). .Professor Muchie has widely published in the areas of international political economy, development economics of innovation and the making of African systems of innovation and new technologies and development. Since 1985, he has produced over 400 publications, including books, chapters in books, and articles in internationally accredited journals and entries in institutional publications. He has done community service through the media: Television, Morning Live, Radios and articles in newspapers regularly in South Africa and internationally.

Title of Speech: Intelligent Systems for Transformative Innovation System Road Map for Smart, Inclusive, Green and Integrated African Development

Abstract: There is an urgent need to apply and promote intelligent systems to address the development challenges that continue to confront the existing states from all regions of Africa. We are now in the 4th industrial revolution known as the period of the knowledge economy where exponential technology, digital age and quantum computing, Nano technology, emerging technologies, biotechnology and advanced materials have impacted on the global value chain. How to do development in this time for those that are at the lower end of the global value chain being in the agricultural, mineral and raw material phase requires that they combine intelligent systems to bring innovative transformation combining artificial intelligence, emerging technologies and information and computer sciences and STEM. A system that integrates the science, technology and innovation system by adding mathematics and engineering incubation is highly needed now more than at any time before.
The keynote will examine intelligent system by exploring the theories, principles, and properties of abstract and concrete problems to link with catching up and leap frogging approaches to connect the raw material, agricultural and mineral based global value chain with the knowledge economy and society stage to create green, smart, integrated, inclusive and innovative development with collaboration, partnership and principles of mutual benefit that brings win-win quality output. System theories and mathematical modelling and STEM design and incubation by employing innovation and entrepreneurship will be explored in finding ways to manage the global value chain from the lower end of agriculture to the higher end of the knowledge economy to transform structurally the African social-economic landscape. There will be an exploration of rigorously formalised intelligent systems that connect all the emerging exponential technologies to address the transformation challenges from agriculture to the knowledge economy. Some cases that can assist to develop models from the existing development patterns will be explored. The integration of agriculture to the knowledge economy intelligent systems requires without fail the identification of different smart adaptive systems such as modelling, analysers, operating control and intelligent actuators that can lead to a hybrid system that include fuzzy set systems, neural networks and evolutionary computing to reinforce the whole integrated intelligent system employing statistical analysis, signal processing and mechanistic modelling and simulation.
Thus the transformation of the global value chain from agriculture, raw material and minerals division of labour to the knowledge economy has to address tangible and measurable applications in system engineering, intelligent engineering, cognitive informatics, cognitive robotics, software engineering, cognitive linguistics, and cognitive computing to demonstrate the structural and behavioural complexities involved in bringing about effective, measurable, tangible and efficient implementation. The intelligent system has to be designed to manage the global value chain either by breaking it through the application of the STEM and all the new technologies or utilise and apply the technological applications to go for catching up or leapfrogging to bring about the African integrated, innovative, inclusive and smart knowledge and digital economy and society. The Intelligent system will have to make a difference and the real challenge is to bring together systematically how to integrate together new knowledge discovery and acquisition, new data mining, bring about learning through digital machine technologies including optimisation, planning, and evolutionary computation to do the transformation effectively and timely. The keynote will address this timely and relevant challenge that Africa is currently facing to scale –up and reach the knowledge based international division of labour.



Prof Saman K. Halgamuge

The Australian National University, Australia

Biography: Saman Halgamuge, Fellow of the IEEE, is a Professor and the Director/Head of Research School of Engineering, The Australian National University. He has previously held appointments as Professor and Associate Dean International, Associate Professor and Reader, Senior Lecturer at the University of Melbourne (1997-2016). He graduated with Dipl.-Ing and PhD degrees in Data Engineering (“Datentechnik”) from Technical University of Darmstadt, Germany and B.Sc. Engineering from University of Moratuwa, Sri Lanka. He is an Associate Editor of BMC Bioinformatics, IEEE Transactions on Circuits and Systems II and Applied Mathematics (Hindawi). His research that lead to 25o publications has been funded over the last 22 years by Australian Research Council (16 grants), National Health and Medical Research Council (2 grants), industry and other external organisations (13 grants or contracts) and funding to support stipends for 45 PhD students. His research record is in Data engineering, which includes Data Analytics based on Unsupervised and Near Unsupervised Learning and Optimization focusing on applications in Mechatronics, Energy, Biology and Medicine. His publication profile is at

Title of Speech: Beyond Unsupervised and Supervised Learning: Biological Data Analytics

Abstract: 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.
Application: Exploring novel inter-drug interactions and re-purposing of known drugs
Given the vast number of clinical drugs, only a small portion of inter-drug interactions are known and there is minute knowledge of non-interacting drug pairs. Therefore, we expand this knowledge base by detecting inter-drug interactions as well as null interactions (label completeness). Most drugs function through multiple mechanisms. Knowing these mechanisms that are effective for a particular disease paves way to discover novel compounds that share similar functions.
Application: Understanding niche environmental ecology by shedding light on microbial ‘dark matter’
We are only at the very brink of understanding the intricacies of the hidden world of microbes. Most samples will contain a majority of microbial ‘dark matter’, a collection of data that cannot be matched to any known or previously discovered organism. As such, many methods which rely purely on Supervised Learning (i.e. those that require knowledge of all microbes in a sample) cannot be used to analyze such data sets.