Title: From Data to Information Granules: a Data Science Perspective
Speaker: Witold Pedrycz (IEEE Fellow, 1998)
Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
Short Bio: Witold Pedrycz 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 mining, 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 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 other international journals.
Title: State-of-the-Art Evolutionary Algorithms for Many Objective Optimization
Speaker: Gary G. Yen (Fellow of IEEE and IET)
Regents Professor in the School of Electrical and Computer Engineering, Oklahoma State University, USA.
Short Bio: Gary G. Yen received the Ph.D. degree in electrical and computer engineering from the University of Notre Dame in 1992. He is currently a Regents Professor in the School of Electrical and Computer Engineering, Oklahoma State University. His research interest includes intelligent control, computational intelligence, evolutionary multiobjective optimization, conditional health monitoring, signal processing and their industrial/defense applications.
Gary was an associate editor of the IEEE Transactions on Neural Networks and IEEE Control Systems Magazine during 1994-1999, and of the IEEE Transactions on Control Systems Technology, IEEE Transactions on Systems, Man and Cybernetics and IFAC Journal on Automatica and Mechatronics during 2000-2010. He is currently serving as an associate editor for the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Emerging Topics on Computational Intelligence and IEEE Transactions on Cybernetics. Gary served as Vice President for the Technical Activities, IEEE Computational Intelligence Society in 2004-2005 and is the founding editor-in-chief of the IEEE Computational Intelligence Magazine, 2006-2009. He was the President of the IEEE Computational Intelligence Society in 2010-2011 and is elected as a Distinguished Lecturer for the terms 2012-2014 and again 2016-2018. He chaired 2006 IEEE World Congress on Computational Intelligence and again 2016 IEEE World Congress on Computational Intelligence, both held in Vancouver, Canada. He received Regents Distinguished Research Award from OSU in 2009, 2011 Andrew P Sage Best Transactions Paper award from IEEE Systems, Man and Cybernetics Society, 2013 Meritorious Service award from IEEE Computational Intelligence Society and 2014 Lockheed Martin Aeronautics Excellence Teaching award. Currently he serves as the chair of IEEE/CIS Fellow Committee. He is a Fellow of IEEE and IET.
Title: AI 2.0: Augmented Intelligence
Speaker: Paulo Lisboa
Professor and Head of Department of Applied Mathematics and Head of Engineering and Technology Research Hub in the Faculty of Engineering and Technology, Liverpool John Moores University, UK.
Short Bio: Paula Lisboa is Professor and Head of Department of Applied Mathematics and Head of Engineering and Technology Research Hub in the Faculty of Engineering and Technology, Liverpool John Moores University, UK.also project director for a £5m ERDF funded employment creation programme in the Liverpool City Region, LCR Activate. He has over 250 refereed publications and his research focus is interpretability of machine learning models for validation by expert users. He has extensive experience in the application of machine learning to clinical decision support, sport analytics and digital marketing.
He chairs the H2020 Advisory Group for Health, Demographic Change and Wellbeing, co-chairs the Medical Data Analysis Task Force in the Data Mining Technical Committee of the IEEE Computational Intelligence Society and is past chair of the Healthcare Technologies Professional Network in the Institution of Engineering & Technology. He also has editorial and peer review roles in a number of journals and research funding bodies.
Paulo Lisboa studied mathematical physics at Liverpool University where he took a PhD in theoretical particle physics in 1983. He was appointed chair in Industrial Mathematics at Liverpool John Moores University in 1996 and Head of Graduate School in 2002.
Title: Data Driven Decision Making in Manufacturing – How did we get there and where next?
Speaker: Adrian Johnston
Principal Managing Engineer for Analytics with Seagate Technology (Northern Ireland).
Short Bio: Adrian Johnston is a Principal Managing Engineer for Analytics with Seagate Technology and has held many different roles over 18 years in its Springtown based facility. Adrian received his Ph.D. degree in Informatics from Ulster University in 2007 focusing his research on integrating computational intelligence within business improvement techniques in advanced semiconductor manufacturing. From 2008 until 2014, Adrian worked within Seagate’s Factory control division to develop hardware and software solutions to enable sensor integration, data extraction, analytics and decision support systems on factory critical data. During this time he published several papers in the areas of equipment prognostic health, virtual metrology and real time data analytics within complex manufacturing environments.
Currently, Dr. Johnston manages a team of engineers and Data Scientists in Seagate’s Northern Ireland and Minnesota facilities. Working within the Seagates Analytics division his team is responsible for advocacy, training, research and solution development across all areas of AI and ML within the Northern Ireland plant. His team continues to take advantage of the latest developments in the fields of Advanced Analytical Systems and Machine Learning on the vast network of data sources, sensors and hardware across Seagate’s supply chain. This activity is further augmented by sustained academic engagement with Ulster University, Queens University and other regional and international research institutes.