Biography：Witold Pedrycz is Professor andCanada Research Chair (CRC) in Computational Intelligence in the Department ofElectrical and Computer Engineering, University of Alberta, Edmonton, Canada.He is also with the Systems Research Institute of the Polish Academy of Sciences,Warsaw, Poland. He holds an appointment of special professorship in the Schoolof Computer Science, University of Nottingham, UK. In 2009 Dr. Pedrycz waselected a foreign member of the Polish Academy of Sciences. In 2012 he waselected a Fellow of the Royal Society of Canada. Witold Pedrycz has been amember of numerous program committees of IEEE conferences in the area of fuzzysets and neurocomputing. In 2007 he received a prestigious Norbert Wiener awardfrom the IEEE Systems, Man, and Cybernetics Council. He is a recipient of theIEEE Canada Computer Engineering Medal 2008. In 2009 he has received a CajasturPrize for Soft Computing from the European Centre for Soft Computing for “pioneering and multifaceted contributions toGranular Computing”. In 2013 he was awarded a Killam Prize. In the sameyear he received a Fuzzy Pioneer Award 2013 from the IEEE ComputationalIntelligence Society.
His main research directions involveComputational Intelligence, fuzzy modeling and Granular Computing, knowledge discoveryand data mining, fuzzy control, pattern recognition, knowledge-based neuralnetworks, relational computing, and Software Engineering. He has publishednumerous papers in this area. He is also an author of 16 research monographscovering various aspects of Computational Intelligence, data mining, andSoftware Engineering.
Dr. Pedrycz is intensively involved ineditorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chiefof WIREsData Mining and Knowledge Discovery (Wiley), and Journal of Granular Computing(Springer). He currently serves as anAssociate Editor of IEEE Transactions on Fuzzy Systems and is a member of a numberof editorial boards of other international journals.
Abstract：The apparentchallenges in data analytics inherently associate with large volumes of data,data variability, and a quest for transparency and interpretability of obtainedresults. We advocate that information granules play a pivotal role inaddressing these key challenges. We demonstrate that a framework of GranularComputing along with a diversity of its formal settings offers a badly neededconceptual and algorithmic setting instrumental for data analytics.
We elaborate on selectedways in which information granules and their processing address help in copingwith abundance of data. A suitable perspective built with the aid ofinformation granules is advantageous in realizing a suitable level ofabstraction and forming sound, problem-oriented tradeoffs among precision ofresults, easiness of their interpretation, value of the results and their stability.All those aspects emphasize importance of actionability and interestingness ofthe produced findings.
Discussed are ways offorming information granules carried out on a basis of abundant data. We showan involvement of efficient granular mechanisms facilitating an inclusion ofdomain knowledge and making the results of ensuing data analytics user-centric.The development of information granules of higher type and higher order is advocatedand their unique role in realizing a hierarchy of processing and coping with adistributed nature of available data is presented.
The facet ofvariability of data is addressed effectively by invoking the mechanisms oftransfer learning applied to the adjustment of information granules.
Lecture language：English