Schedule of tutorials - 19th July, 2020

11:30 - 13:30
14:00 - 16:00
16:30 - 18:30
19:00 - 21:00
11:30 - 13:30
Tutorial Title Presenter Conference Email
Adversarial Machine Learning: On The Deeper Secrets of Deep Learning Danilo Vargas IJCNN
Brain-Inspired Spiking Neural Network Architectures for Deep, Incremental Learning and Knowledge Evolution       Nikola Kasabov IJCNN
Fundamentals of Fuzzy Networks Alexander Gegov, Farzad Arabikhan FUZZ
Instance Space Analysis for Rigorous and Insightful Algorithm Testing Kate Smith-Miles, Mario Andres, Munoz Acosta WCCI
Advances in Deep Reinforcement Learning Thanh Thi Nguyen, Vijay Janapa Reddi,Ngoc Duy Nguyen, IJCNN 
Selection Exploration and Exploitation Stephen Chen, James Montgomery CEC
Visualising the search process of EC algorithms Su Nguyen, Yi Mei, and Mengjie Zhang CEC
Evolutionary Machine Learning  Masaya Nakata, Shinichi Shirakawa, Will Browne CEC
Evolutionary Many-Objective Optimization Hisao Ishibuchi, Hiroyuki Sato CEC
14:00 - 16:00
Tutorial Title Presenter Conference Email
Deep Learning for Graphs Davide Bacciu IJCNN
Randomization Based Deep and Shallow Learning Methods for Classification and Forecasting P N Suganthan IJCNN
Multi-modality Helps in Solving Biomedical Problems: Theory and Applications Sriparna Saha, Pratik Dutta WCCI
Deep Stochastic Learning and Understanding Jen-Tzung Chien IJCNN
Paving the way from Interpretable Fuzzy Systems to Explainable AI Systems José M. Alonso, Ciro Castiello, Corrado Menca, Luis Magdalena FUZZ
Pareto Optimization for Subset Selection: Theories and Practical Algorithms Chao Qian, Yang Yu CEC
Benchmarking and Analyzing Iterative Optimization Heuristics with IOHprofiler Carola Doerr, Thomas Bäck, Ofer Shir, Hao Wang CEC
Differential Evolution Rammohan Mallipeddi,  Guohua Wu         CEC
Evolutionary computation for games: learning, planning, and designing

Julian Togelius , Jialin Liu 

16:30 - 18:30
Tutorial Title Presenter Conference Email
From brains to deep neural networks Saeid Sanei, Clive Cheong Took IJCNN
Evolution of Neural Networks Risto Miikkulainen IJCNN
Experience Replay for Deep Reinforcement Learning Abdul Rahman Al Tahhan, Vasile Palade IJCNN
Fuzzy Systems for Neuroscience and Neuro-engineering Applications Javier Andreu, CT Lin FUZZ
Dynamic Parameter Choices in Evolutionary Computation Carola Doerr, Gregor Papa  CEC
Evolutionary Computation for Dynamic Multi-objective Optimization Problems Shengxiang Yang CEC
Evolutionary Algorithms and Hyper-Heuristics Nelishia Pillay CEC
Large-Scale Global Optimization Mohammad Nabi Omidvar, Antonio LaTorre CEC 
Bilevel optimization Ankur Sinha, Kalyanmoy Deb CEC
19:00 - 21:00
Tutorial Title Presenter Conference Email
How to combine human and computational intelligence? Peter Erdi WCCI
Machine learning  for data streams in Python with scikit-multi flow Jacob Montiel, Heitor Gomes,Jesse Read, Albert Bifet IJCNN
Deep randomized neural networks Claudio Gallicchio, Simone Scardapane IJCNN
Patch Learning: A New Method of Machine Learning, Implemented by Means of Fuzzy Sets Jerry Mendel FUZZ
Self-Organizing Migrating Algorithm - Recent Advances and Progress in Swarm Intelligence Algorithms

Roman Senkerik CEC
Large-Scale Global Optimization - PART 2 Mohammad Nabi Omidvar, Antonio LaTorre CEC 
Recent Advances in Particle Swarm Optimization Analysis and Understanding Andries Engelbrecht, Christopher Cleghorn CEC
Nature-Inspired Techniques for Combinatorial Problems Malek Mouhoub


Fundamentals of Fuzzy Networks

Alexander Gegov
University of Portsmouth, UK


The tutorial focuses on the theoretical foundations of fuzzy networks. The nodes of these networks are fuzzy systems represented by rule bases and the connections between the nodes are outputs from and inputs to these rule bases [1]-[6].

Fuzzy networks have an underlying two-dimensional grid structure with horizontal levels and vertical layers. The levels represent spatial hierarchy in terms of network breadth and the layers represent temporal hierarchy in terms of network depth.

The nodes of fuzzy networks are modelled by Boolean matrices or binary relations. The connections between the nodes are modelled by block schemes or topological expressions. Each network node is located in a cell within the underlying grid structure.

Nodes in fuzzy networks are manipulated by merging and splitting operations. The merging operations are for network analysis and the splitting operations are for network design. These operations are used for converting a fuzzy network into a fuzzy system and vice versa.

The operations are illustrated on feedforward and feedback fuzzy networks. Feedforward networks include combinations of narrow/broad and shallow/deep network structures. Feedback networks include combinations of single/multiple and local/global feedback loops.

Fuzzy networks are applied to case studies from engineering, computing, transport and finance. They are validated successfully against standard and hierarchical fuzzy systems. The validation uses performance evaluation indicators for feasibility, accuracy, efficiency, transparency.

[1] A.Gegov, Fuzzy Networks for Complex Systems: A Modular Rule Base Approach, Series in Studies in Fuzziness and Soft Computing (Springer, Berlin, 2011)

[2] F.Arabikhan, Telecommuting Choice Modelling using Fuzzy Rule Based Networks, PhD Thesis (University of Portsmouth, UK, 2017)

[3] A.Gegov, F.Arabikhan and N.Petrov, Linguistic composition based modelling by fuzzy networks with modular rule bases, Fuzzy Sets and Systems 269 (2015) 1-29

[4] X.Wang, A.Gegov, F.Arabikhan, Y.Chen and Q.Hu, Fuzzy network based framework for software maintainability prediction, Uncertainty, Fuzziness and Knowledge Based Systems 27/5 (2019) 841-862

[5] A.Yaakob, A.Serguieva and A.Gegov, FN-TOPSIS: Fuzzy networks for ranking traded equities, IEEE Transactions on Fuzzy Systems 25/2 (2016) 315-332

[6] A.Yaakob, A.Gegov and S.Rahman, Fuzzy networks with rule base aggregation for selection of alternatives, Fuzzy Sets and Systems341 (2018) 123-144

Presenters Names andAffiliations:

Alexander Gegov, University of Portsmouth, UK,

Farzad Arabikhan, University of Portsmouth, UK,

Presenters Bios:

Alexander Gegov is Reader in Computational Intelligence in the School of Computing, University of Portsmouth, UK. He holds a PhD in Control Systems and a DSc in Intelligent Systems – both from the Bulgarian Academy of Sciences. He has been a recipient of a National Annual Award for Best Young Researcher from the Bulgarian Union of Scientists. He has been Humboldt Guest Researcher at the University of Duisburg in Germany. He has also been EU Visiting Researcher at the University of Wuppertal in Germany and the Delft University of Technology in the Netherlands.

Alexander Gegov’s research interests are in the development of computational intelligence methods and their application for modelling and simulation of complex systems and networks. He has edited 6 books, authored 5 research monographs and over 30 book chapters – most of these published by Springer. He has authoredover 50 articles and 100 papers in international journals and conferences – many of these published and organised by IEEE. He hasalso presented over 20 invited lectures and tutorials – most of these at IEEE Conferences on Fuzzy Systems, Intelligent Systems, Computational Intelligence and Cybernetics.

Alexander Gegov is Associate Editor for ‘IEEE Transactions on Fuzzy Systems’, ‘Fuzzy Sets and Systems’, ‘Intelligent and Fuzzy Systems’ and ‘Computational Intelligence Systems’. He is also Reviewer for several IEEE journals and Assessor for several National Research Councils. He is Member of the IEEE Computational Intelligence Society and the Soft Computing Technical Committee of the IEEE Society of Systems, Man and Cybernetics. He is also Guest Editor for the forthcoming Special Issue on Deep Fuzzy Models of the IEEE Transactions on Fuzzy Systems.

Farzad Arabikhan joined the University of Portsmouth as a lecturer in 2017. He completed his PhD on 2017 at the University of Portsmouth and his thesis focus was on Modelling Telecommuting using Fuzzy Networks.  In his research, he optimised Fuzzy Networks using Genetic Algorithms and data mining approaches. Having published his research results in several journal and conference papers, he has also secured funding from European Cooperation in Science and Technology (COST) to collaborate with European scholars in University Paris 1 Pantheon Sorbonne, Paris, France and Mediterranean University of Reggio Calabria to pursue his research activities. He holds BSc and MSc degrees in Civil and Transportation Engineering from the Sharif University of Technology, Tehran, Iran.

Paving the way from Interpretable Fuzzy Systems to Explainable Artificial Intelligence Systems

José M. Alonso
Research Centre in Intelligent Technologies (CiTIUS)
University of Santiago de Compostela (USC)
Campus Vida, E-15782, Santiago de Compostela, Spain
Email: (


Ciro Castiello
Department of Informatics
University of Bari “Aldo Moro”, Bari, Italy


Department of Informatics
University of Bari “Aldo Moro”, Bari, Italy


Luis Magdalena
Department of Applied Mathematics, School of Informatics
Universidad Politécnica de Madrid (UPM), Spain



In the era of the Internet of Things and Big Data, data scientists are required to extract valuable knowledge from the given data. They first analyze, cure and pre-process data. Then, they apply Artificial Intelligence (AI) techniques to automatically extract knowledge from data. Actually, AI has been identified as the most strategic technology of the 21st century” and is already part of our everyday life. The European Commission states that “EU must therefore ensure that AI is developed and applied in an appropriate framework which promotes innovation and respects the Union’s values and fundamental rights as well as ethical principles such as accountability and transparency”. It emphasizes the importance of eXplainable AI (XAI in short), in order to develop an AI coherent with European values:to further strengthen trust, people also need to understand how the technology works, hence the importance of research into the explainability of AI systems. Moreover, as remarked in the last challenge stated by the USA Defense Advanced Research Projects Agency (DARPA), “even though current AI systems offer many benefits in many applications, their effectiveness is limited by a lack of explanation ability when interacting with humans”. Accordingly, users without a strong background on AI, require a new generation of XAI systems. They are expected to naturally interact with humans, thus providing comprehensible explanations of decisions automatically made.

XAI is an endeavor to evolve AI methodologies and technology by focusing on the development of agents capable of both generating decisions that a human could understand in a given context, and explicitly explaining such decisions. This way, it is possible to verify if automated decisions are made on the basis of accepted rules and principles, so that decisions can be trusted and their impact justified.

Even though XAI systems are likely to make their impact felt in the near future, there is a lack of experts to develop the fundamentals of XAI, i.e., ready to develop and to maintain the new generation of AI systems that are expected to surround us soon. This is mainly due to the inherent multidisciplinary character of this field of research, with XAI researchers coming from heterogeneous research fields. Moreover, it is hard to find XAI experts with a holistic view as well as wide and solid background regarding all the related topics.

Consequently, the main goal of this tutorial is to provide attendees with a holistic view of fundamentals and current research trends in the XAI field, paying special attention to fuzzy-grounded knowledge representation and how to enhance human-machine interaction.

The tutorial will cover the main theoretical concepts of the topic, as well as examples and real applications of XAI techniques. In addition, ethical and legal aspects concerning XAI will also be considered.

Tutorial Presenters (names with affiliations):

José M. Alonso (
Research Centre in Intelligent Technologies (CiTIUS)
University of Santiago de Compostela (USC)
Campus Vida, E-15782, Santiago de Compostela, Spain


Ciro Castiello (
Department of Informatics
University of Bari “Aldo Moro”, Bari, Italy


CorradoMencar (
Department of Informatics
University of Bari “Aldo Moro”, Bari, Italy


Luis Magdalena (
Department of Applied Mathematics, School of Informatics
Universidad Politécnica de Madrid (UPM), Spain


Tutorial Presenters’ Bios:

Jose M. Alonso received his M.S. and Ph.D. degrees in Telecommunication Engineering, both from the Technical University of Madrid (UPM), Spain, in 2003 and 2007, respectively. Since June 2016, he is postdoctoral researcher at the University of Santiago de Compostela, in the Research Centre in Intelligent Technologies (CiTIUS). He is currently Chair of the Task Force on “Fuzzy Systems Software” in the Fuzzy Systems Technical Committee of the IEEE Computational Intelligence Society, Associate Editor of the IEEE Computational Intelligence Magazine (ISSN:1556-603X), secretary of the ACL Special Interest Group on Natural Language Generation, and chair of the Doctoral Consortium at the 2020 European Conference on Artificial Intelligence. He is currently coordinating the H2020-MSCA-ITN-2019 project entitled “Interactive Natural Language Technology for Explainable Artificial Intelligence” (NL4XAI). He has published more than 130 papers in international journals, book chapters and in peer-review conferences. According to Google Scholar (accessed: February 15, 2020) he has h-index=21 and i10-index=42. His research interests include computational intelligence, explainable artificial intelligence, interpretable fuzzy systems, natural language generation, development of free software tools, etc.

CiroCastiello, Ph.D. graduated in Informatics in 2001 and received his Ph.D. in Informatics in 2005. Currently he is an Assistant Professor at the Department of Informatics of the University of Bari Aldo Moro, Italy. His research interests include: soft computing techniques, inductive learning mechanisms, interpretability of fuzzy systems, eXplainable Artificial Intelligence. He participated in several research projects and published more than seventy peer-reviewed papers. He is also regularly involved in the teaching activities of his department. He is member of the European Society for Fuzzy Logic and Technology (EUSFLAT) and of the INdAM Research group GNCS (Italian National Group of Scientific Computing).

CorradoMencar is Associate Professor in Computer Science at the Department of Computer Science of the University of Bari “A. Moro”, Italy. He graduated in 2000 in Computer Science and in 2005 he obtained the title of PhD in Computer Science. In 2001 he was analyst and software designer for some Italian companies. Since 2005 he has been working on research topics concerning Computational Intelligence and Granular Computing. As part of his research activity, he has participated in several research projects and has published over one hundred peer-reviewed international scientific publications. He is also Associate Editor of several international scientific journals, as well as Featured Reviewer for ACM Computing Reviews. He regularly organizes scientific events related to his research topics with international colleagues. Currently, research topics include fuzzy logic systems with a focus on Interpretability and Explainable Artificial Intelligence, Granular Computing, Computational Intelligence applied to the Semantic Web, and Intelligent Data Analysis. As part of his teaching activity, he is, or has been, the holder of numerous classes and PhD courses on various topics, including Computer Architectures, Programming Fundamentals, Computational Intelligence and Information Theory.

Luis Magdalena is with the Dept. of Applied Mathematics for ICT of the Universidad Politécnica de Madrid. From 2006 to 2016 he was Director General of the European Centre for Soft Computing in Asturias (Spain). Under his direction, the Center was recognized with the IEEE-CIS Outstanding Organization Award in 2012. Prof. Magdalena has been actively involved in more than forty research projects. He has co-author or co-edited ten books including “Genetic Fuzzy Systems”, “Accuracy Improvements in Linguistic Fuzzy Modelling”, and “Interpretability Issues in Fuzzy Modeling”. He has also authored over one hundred and fifty papers in books, journals and conferences, receiving more than 6000 citations. Prof. Magdalena has been President of the “European Society for Fuzzy Logic and Technologies”, Vice-president of the “International Fuzzy Systems Association” and is Vice-President for Technical Activities of the IEEE Computational Intelligence Society for the period 2020-21.

External website with more information on Tutorial (if applicable):


Fuzzy Systems for Neuroscience and Neuro-Engineering

Javier Andreu-Perez
University of Essex

Ching-Teng Lin
University of Technology Sydney

Abstract: This tutorial will be an introduction to new researchers to the field of Neuroscience and Neuro-engineering from a fuzzy perspective. Attendees are not required prior knowledge on fuzzy systems or neuroscience to attend this tutorial. We will focus on brain research/decoding methods that use non-invasive modalities of neuroimaging. We will present the latest and most outstanding works that have applied Fuzzy Systems in brain signals to date. Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, fuzzy sets and systems have been proposed as a useful and effective framework for the analysis of brain activity as well as to enable a direct communication pathway between the brain and external devices (brain computer/machine interfaces). While there has been increasing interest in these questions, the contribution of fuzzy logic sets, and systems has been diverse depending on the area of application in neuroscience. With regard to the decoding of brain activity, fuzzy sets and systems represent an excellent tool to overcome the challenge of processing extremely high signals that are most likely to be affected by high uncertainty. In this tutorial we will also provide an introduction about the foundations of fuzzy sets, logic and systems for the analysis of brain signals and neuroimaging data, including related disciplines such as computational neuroscience, brain computer/machine interfaces, neuroscience, neuroengineering, neuroinformatics, neuroergonomics, affective neuroscience and neurotechnology. After the tutorial, we will conduct an interactive survey and a panel discussion among the attendees.

Tutorial Presenters (names with affiliations):  Javier Andreu-Perez (University of Essex, United Kingdom), Ching-Teng Lin (University of Technology Sydney, Australia)


 Javier Andreu-Perez (SMIEEE) is Senior Lecturer in the School of Computer Science and Electronic Engineering (CSEE), University of Essex, United Kingdom (UK). PhD in Intelligent Systems from University of Lancaster, UK. His research expertise lies in the development of new methods in artificial intelligence and machine learning within the healthcare domain, particularly in application-driven advances of AI and Machine Learning for the analysis of bio-medical and neuroimaging data. He has expertise in the use of Big Data, machine learning models based on deep learning and methodologies of uncertainty modelling for highly noisy non-stationary signals. Javier has published relevant highly-cited papers and several prestigious IEEE Transactions and other Top Q1 journals in Artificial Intelligence and Neuroscience. In total Javier’s work in Artificial Intelligence and Biomedical engineering has attracted more than 1400+ citations. Javier has participated in awarded projects from UK research councils such us The Innovate UK Council, NIHR Biomedical Research Centre, Welcome Trust Centre for Global Health Research and private corporations. He is also member of the EPSRC peer review college. He is also Associate/Area Editor for the Journal Neurocomputing (Elsevier) and International Journal of Computational Intelligence (EUSFLAT official journal). Javier’s is also Chair if the IEEE CIS Society Task Force on Extensions to Type-1 Fuzzy  and co-chair of an international working group on Uncertainty Modelling for Neuro-Engineering. Javier is a frequent organiser of special sessions and competitions at FUZZ-IEEE and WCCI on the use of fuzzy systems in Brain research and interfaces.

Ching-Teng Lin (FIEEE), Professor Chin-Teng Lin received the B.S. degree from National Chiao-Tung University (NCTU), Taiwan in 1986, and the Master and Ph.D. degree in electrical engineering from Purdue University, USA in 1989 and 1992, respectively. He is currently the Distinguished Professor of Faculty of Engineering and Information Technology, and Co-Director of Center for Artificial Intelligence, University of Technology Sydney, Australia. He is also invited as Honorary Chair Professor of Electrical and Computer Engineering, NCTU, International Faculty of University of California at San-Diego (UCSD), and Honorary Professorship of University of Nottingham. Dr. Lin was elevated to be an IEEE Fellow for his contributions to biologically inspired information systems in 2005 and was elevated International Fuzzy Systems Association (IFSA) Fellow in 2012. Dr. Lin received the IEEE Fuzzy Systems Pioneer Awards in 2017. He served as the Editor-in-chief of IEEE Transactions on Fuzzy Systems from 2011 to 2016. He also served on the Board of Governors at IEEE Circuits and Systems (CAS) Society in 2005-2008, IEEE Systems, Man, Cybernetics (SMC) Society in 2003-2005, IEEE Computational Intelligence Society in 2008-2010, and Chair of IEEE Taipei Section in 2009-2010. Dr. Lin was the Distinguished Lecturer of IEEE CAS Society from 2003 to 2005 and CIS Society from 2015-2017. He serves as the Chair of IEEE CIS Distinguished Lecturer Program Committee in 2018~2019. He served as the Deputy Editor-in-Chief of IEEE Transactions on Circuits and Systems-II in 2006-2008. Dr. Lin was the Program Chair of IEEE International Conference on Systems, Man, and Cybernetics in 2005 and General Chair of 2011 IEEE International Conference on Fuzzy Systems. Dr. Lin is the co-author of Neural Fuzzy Systems (Prentice-Hall), and the author of Neural Fuzzy Control Systems with Structure and Parameter Learning (World Scientific). He has published more than 300 journal papers (Total Citation: 20,163, H-index: 65, i10-index: 254) in the areas of neural networks, fuzzy systems, brain computer interface, multimedia information processing, and cognitive neuro-engineering, including about 120 IEEE journal papers.

Patch Learning: A New Method of Machine Learning, Implemented by
Means of Fuzzy Sets


There have been different strategies to improve the performance of a machine learning model, e.g., increasing the depth, width, and/or nonlinearity of the model, and using ensemble learning to aggregate multiple base/weak learners in parallel or in series. The goal of this tutorial is to describe a novel and new strategy called patch learning (PL) for this problem. PL consists of three steps: 1) train an initial global model using all training data; 2) identify from the initial global model the patches which contribute the most to the learning error, and then train a (local) patch model for each such patch; and, 3) update the global model using training data that do not fall into any patch. To use a PL model, one first determines if the input falls into any patch. If yes, then the corresponding patch model is used to compute the output. Otherwise, the global model is used. To-date, PL can only be implemented using fuzzy systems. How this is accomplished will be explained. Some regression problems on 1D/2D/3D curve fitting, nonlinear system identification, and chaotic time-series prediction, will be explained to demonstrate the effectiveness of PL. PL opens up a promising new line of research in machine learning. Opportunities for future
research will be explained.


This tutorial is based on materials in the following journal articles:
• D. Wu and J. M. Mendel, “Patch Learning,” IEEE Trans. on Fuzzy Systems, Early Access, July 2019.
• J. M. Mendel, “Explaining the performance potential of rule-based fuzzy systems as of the state space,” IEEE Trans. on Fuzzy Systems, vol. 26, no. 4, pp. 2362–2373, Aug a. 2 g0r1ea8t. er sculpting
• J. M. Mendel, “Adaptive variable structure basis function expansions: candidates for machine learning,” Information Sciences, vol. 496, pp. 124–149, 2019.

Outline of Covered Material
• Introduction
o Machine learning
o Present approaches to improving machine learning performance
• Three questions, as the basis for the rest of the tutorial:
o What is the general idea of Patch Learning (PL)?
o How can a patch and PL be implemented?
o How well does PL perform?
• What is the general idea of PL?
o What is a patch?
o Steps of PL
o Analogy to a sculptor who is sculpting a human figure
o Determining optimal number of patch models
o PL illustrated by a simple regression example
o Logic for determining which model to use in PL
• How can a patch and PL be implemented?
o Patches
o Partitions of the state space
o Implementing a patch using fuzzy sets
o Locating a measured value in a specific patch
o Implementation of PL
• How well does PL perform?
o Example 1: 1D curve fitting
o Example 2: 2D surface fitting
o Other examples, time permitting
• Future research topics
• Conclusions

Tutorial Presenter:

Jerry M. Mendel (Life Fellow IEEE, Fuzzy Systems Pioneer of the IEEE Computational Intelligence Society), Emeritus Professor of Electrical Engineering, University of Southern California, Los Angeles, CA.

Tutorial Presenter’s Biography:

Jerry M. Mendel (; received the Ph.D. degree in electrical engineering from the Polytechnic Institute of Brooklyn, Brooklyn, NY. Currently, he is Emeritus Professor of Electrical Engineering at the University of Southern
California in Los Angeles, where he has been since 1974. He is also a Tianjin 1000-Talents Foreign Experts Plan Endowed Professor, and Honorary Dean of the College of Artificial Intelligence, Tianjin Normal University, Tianjin, China. He has published over 580 technical papers and is author and/or co-author of 13 books, including Uncertain Rule-based Fuzzy
Systems: Introduction and New Directions, 2nd ed. (Springer 2017), Perceptual Computing:
Aiding People in Making Subjective Judgments (Wiley & IEEE Press, 2010), and Introduction to Type-2 Fuzzy Logic Control: Theory and Application (Wiley & IEEE Press, 2014). He is a Life Fellow of the IEEE, a Distinguished Member of the IEEE Control Systems Society, and a Fellow of the International Fuzzy Systems Association. He was President of the IEEE Control Systems Society in 1986, a member of the Administrative Committee of the IEEE Computational Intelligence Society for nine years, and Chairman of its Fuzzy Systems Technical Committee and the Computing With Words Task Force of that TC. Among his awards are the 1983 Best Transactions Paper Award of the IEEE Geoscience and Remote Sensing Society, the 1992 Signal
Processing Society Paper Award, the 2002 and 2014 Transactions on Fuzzy Systems Outstanding Paper Awards, a 1984 IEEE Centennial Medal, an IEEE Third Millenium Medal, a Fuzzy Systems Pioneer Award (2008) from the IEEE Computational Intelligence Society for “fundamental theoretical contributions and seminal results in fuzzy systems”; and, 2015 USC
Viterbi School of Engineering Senior Research Award. His present research interests (yes, he is still performing research with many colleagues around the Globe) include: type-2 fuzzy logic systems and computing with words.

Venue: With WCCI 2020 being held as a virtual conference, there will be a virtual experience of Glasgow, Scotland accessible through the virtual platform. We hope that everyone will have a chance to visit one of Europe’s most dynamic cultural capitals and the “World’s Friendliest City” soon in the future!.