Tutorials

Tutorials will be held on July 9, 2017. Traditionally, tutorials attract a broad range of audiences, including professionals, researchers from academia, students, and practitioners, who wish to enhance their knowledge in the selected tutorial topic. Tutorials offer a unique opportunity to disseminate in-depth information on specific topics in fuzzy sets and systems, soft computing/computational intelligence, and related areas.
In the following, the details of tutorials which will be held at FUZZ-IEEE 2017 including the name, the description, the presenters and their biographies (by clicking on the photos) are given.


José Maria Alonso
Jesús Alcalá
Autilia Vitiello

Software for Fuzzy Computing

Fuzzy systems have been used widely thanks to their ability to successfully solve a wide range of problems in different application fields. However, their replication and application requires a high level of knowledge and experience. Furthermore, few researchers publish the software and/or source code associated with their proposals, which is a major obstacle to scientific progress in other disciplines regarding both academy and industry. In recent years, most fuzzy system software (FSS) has been developed in order to facilitate the use of fuzzy systems. Some software is commercially distributed but most software is available as free and open source software, reducing such obstacles and providing many advantages: quicker detection of errors, innovative applications, faster adoption of fuzzy systems, etc. As advised by the Task Force on Fuzzy Systems Software in the Fuzzy Systems Technical Committee of the IEEE Computational Intelligence Society, researchers and developers should think carefully about some critical considerations (interoperability, novelty, usability, and relevance) when publishing a new software.
Moreover, the Standards Committee of the IEEE Computational Intelligence Society has recently sponsored a new standard fuzzy language, namely IEEE Std. 1855TM –2016, which exploits the benefits offered by the eXtensible Markup Language (XML) specifications and related tools in order to make easier modeling fuzzy logic systems in a human-readable and hardware independent way. This tutorial is aimed at bridging the gap in designing fuzzy systems by providing its attendees with a systematic and complete description of state-of-the art of fuzzy system software.
This tutorial is made up of two main parts:
1) Fuzzy Systems Software Overview: Taxonomy, Current Research Trends and Prospects
2) Description of the IEEE Standard for Fuzzy Markup Language
In the first part of this tutorial, J. Alcala-Fdez and Jose M. Alonso (Chair and Vice-chair of the Task Force on Fuzzy Systems Software in the Fuzzy Systems Technical Committee of the IEEE Computational Intelligence Society) will present an overview of freely available and open source FSS in order to provide a well-established framework that helps researchers to find existing proposals easily and to develop well founded future work. They will first introduce a two-level taxonomy and they will briefly discuss some of the main contributions related to each field (Fuzzy Sets Theory, Control, Decision-Making, Software Engineering, etc.). Then, they will provide a snapshot of the status of the publications and citations in this field according to the Thomson Reuters Web of Science, formerly widely known as Institute for Scientific Information (ISI) Web of Knowledge. Then, they will sketch some critical considerations (interoperability, novelty, usability, and relevance) regarding recent trends and potential research directions.
In the second part of the tutorial, G. Acampora and A. Vitiello will introduce the new IEEE Std. 1855TM recently published by IEEE. The Standards Committee of the IEEE Computational Intelligence has published a new standard fuzzy language which exploits the benefits offered by the eXtensible Markup Language (XML) specifications and related tools in order to make easier modeling fuzzy logic systems in a human-readable and hardware independent way. The goal of this part of the tutorial is 1) to show how IEEE 1855 is used to model different kinds of fuzzy systems, and 2) how it can be integrated in different software architecture to enable systems’ designers to embed fuzzy reasoning in their frameworks.
Bibliographical References
1) J. Alcalá-Fdez and J.M. Alonso, A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends and Prospects, IEEE Transactions on Fuzzy Systems, 24(1):40-56, 2016, DOI:10.1109/TFUZZ.2015.2426212. Complementary material is available online at http://sci2s.ugr.es/fss
2) IEEE-SA Standards Board, IEEE Std. 1855TM –2016, IEEE Standard for Fuzzy Markup Language, IEEE Computational Intelligence Society, sponsored by the IEEE Standards Committee, 2016.


Isaac Triguero
Alberto Fernández
Francisco Herrera

Fuzzy Models for Data Science and Big Data

In the era of the information technology, the problem of managing Big Data applications is becoming the main focus of attention in a wide variety of disciplines such as science, business, industry, and so on. Data and the ability to process and extract knowledge from it are the "new gold" in the digital economy in which we move. As a result, it has emerged an area called Data Science. It collects all scenarios in which data has a starring role with the aim of turning it into knowledge. Data Science encompasses the areas known as machine learning, data mining, social mining, Big Data, and so on.
Addressing Big Data becomes a very interesting and challenging task where we must consider new paradigms to develop scalable algorithms. The MapReduce framework, introduced by Google, allows us to carry out the processing of these large amounts of information. Its open source implementation, named Hadoop, led the development of a popular platform with a wide use. Recently, new alternatives to the standard Hadoop-MapReduce framework have arisen to improve the performance in this scenario, being the most relevant one the Apache Spark project.The MapReduce framework implies that existing algorithms have to be redesigned or that new ones need to be developed in order to take advantage of their capabilities in the big data context.
The challenges posed by real-world Big Data problems are manifold. Apart from the straightforward computational complexity, researchers in this field must deal with vague, imprecise or uncertain data. Among different techniques for the data modeling, those based on fuzzy sets and fuzzy logic are a valuable tool for developing robust solutions. In this tutorial, we will first provide a gentle introduction to the problem of Big Data as well as the presentation of recent technologies (Hadoop ecosystem, Spark). Then, we will dive into the field of Big Data analytics, introducing machine learning libraries such as Mahout and MLlib.
Afterward, we will go across the topic of fuzzy modeling in the Big Data context. We start by introducing the features and design for the most recent approaches in this field. We aim at defining the direction for the design of powerful algorithms based on fuzzy systems, and how the information extracted with these models can be useful for the experts.
The last part of this tutorial will dig into the concept of Data Science. To do so, we will shortly introduce non-classical problems that have acquired much relevance in the last years, such as multi-label problems, transfer learning, multi-view learning, and so on, discussing the usefulness on the use of fuzzy modeling. Additionally, we will focus on the interpretability to raise some questions to reflect the comprehensibility, beyond the dialectical interpretability-accuracy debate, looking for models performing well, understandable and simple. Finally, we analyze the future opportunities that fuzzy modeling may have with respect to Data Science in terms of the knowledge represented by fuzzy and linguistic rules.
Table of contents and estimated duration:
- A gentle introduction to big data
- Big Data Analytics
- Fuzzy Modeling for big data
- Data Science: New scenarios and Non-classical problems
- Fuzzy Models in Data Science - Beyond interpretability vs accuracy trade-off
- Fuzzy Models in Data Science: Opportunities


Christian Wagner
Jonathan Garibaldi
Robert John
Josie McCulloch

Type-2 Fuzzy Sets and Systems

General type-2 fuzzy sets and systems are paradigms which enable fine-grained capturing, modelling and reasoning with uncertain information. While recent years have seen increasing numbers of applications from control to intelligent agents and environmental management, the perceived complexity of general type-2 fuzzy sets and systems still makes their adoption a daunting and not time-effective proposition to the majority of researchers.
This tutorial is designed to give researchers a practical introduction to general type-2 fuzzy sets and systems. Over three hours, the modular tutorial will address three main aspects of using and working with general type-2 fuzzy sets and systems:
1. Introduction to General Type-2 Fuzzy Sets and Systems The first component of the tutorial will provide attendees with a concise and practice-led overview of general type-2 fuzzy sets and systems, reviewing the motivation behind their definition, their structure in relation to type-1 and interval type-2 fuzzy sets and systems, as well a set of recent applications.
2. Designing General Type-2 Fuzzy Sets and Systems In the second part of the tutorial, two distinct aspects will be discussed. First, attendees will be given a practical introduction to designing their own general type-2 fuzzy system. Using the online browser-based toolkit JuzzyOnline, participants will be guided in the design of a general type-2 fuzzy system, relating their own design to the design of type-1 fuzzy systems at each stage. Second, the design of general type-2 fuzzy sets will be discussed through a presentation of a key set of recently introduced processes to create general type-2 fuzzy sets from data. 3. Coding General Type-2 Fuzzy Sets and Systems
The final part of the tutorial will focus on the programmatic implementation and use of general type-2 fuzzy sets and systems. Currently available software tools and toolkit for general type-2 fuzzy sets and system applications will be briefly reviewed, highlighting usage areas from inference to the computation of measures such as similarity and distance. Finally, interested participants will be supported in the development of a simple general type-2 fuzzy system based on the freely available Juzzy, Phthon and/or R based general type-2 APIs.


Erdal Kayacan
Changhong Fu

Vision-Based Control of UAVs Using Type-1 and Type-2 FLCs with ROS

Unmanned aerial vehicles (UAVs), commonly known as drones, are commonly used for a number of missions such as traffic surveillance, search and rescue, orchard monitoring, wildlife protection and infrastructure inspection. However, developing almost perfect flight controllers for UAVs for varying working conditions remains a challenging task due to under actuated dynamics, nonlinearities in their models, aerodynamic damping, and internal and external uncertainties. On the other hand, modelling of these complex systems is also tedious, costly and time consuming. Therefore, an advanced (preferably model-free and learning) control approach may be appropriate to improve the control performance and manoeuvrability of UAVs instead of using conventional controllers, e.g., proportional-integral-derivative (PID) controller. As a model-free control technique, type-1 and type-2 fuzzy logic controllers (FLCs) have already been implemented in many industrial control systems. However, they still lack in real-time implementations for UAVs, especially for type-2 FLCs. Therefore, the main aim of this tutorial is to discuss and present real-time implementations of type-1 and type-2 FLCs for controlling UAVs.
An introduction of state-of-art computer vision algorithms used for six degree-of-freedom estimation of UAVs will be presented, and implementation details with FLCs elaborated. Robotic operating system (ROS) will be introduced, and programming FLCs in ROS environment will be shown.
A real quadrotor UAV flight demo related to vision-based autonomous trajectory tracking will be shown indoors with various FLCs in ROS using computer vision algorithms. Online parameter tuning will also be discussed during the demo. Different scenarios will be considered, e.g. performance of different FLCs will be investigated under different noise conditions.
This tutorial will consist of two parts:
Part 1: Theoretical framework for type-1 and type-2 FLCs, vision-based control and ROS
1) We will focus on theoretical basis and definitions of type-1 and type-2 FLCs, introduce state-of-art computer vision algorithms for six degree of freedom pose estimation, various commercial applications of UAVs and discuss their limitations and some reasons why FLCs may be useful.
2) We will present a complete computer vision-based control structure of type-1 and type-2 FLCs for navigating UAVs in ROS environment. The main focus will be implementation of type-1 and type-2 FLCs for control of UAVs, and integration of computer vision algorithms and fuzzy controls.
Part 2: Real-Time implementation using ROS
1) We will demonstrate autonomous navigation of UAVs and discuss different fuzzy controller performances. We will also talk about online tuning of FLCs and its advantages under different noisy working conditions.
2) A sample program will be provided so that attendees can explore real-time implementations of UAV control using different FLCs.

References:
1. Erdal Kayacan and Mojtaba A. Khanesar, “Fuzzy Neural Networks for Real Time Control Applications, Concepts, Modeling and Algorithms for Fast Learning", 1st Edition, Butterworth-Heinemann, Print Book ISBN:9780128026878. (17 Sept 2015)
2. Erdal Kayacan, Reinaldo Maslim. "Type-2 Fuzzy Logic Trajectory Tracking Control of Quadrotor VTOL Aircraft With Elliptic Membership Functions," in IEEE/ASME Transactions on Mechatronics , vol.PP, no.99, pp.1-1 doi: 10.1109/TMECH.2016.2614672
3. Changhong Fu, Andriy Sarabakha, Erdal Kayacan, Christian Wagner, Robert John, Jonathan M. Garibaldi. "A comparative study on the control of quadcopter UAVs by using singleton and non-singleton fuzzy logic controllers," 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, BC, Canada, 2016, pp. 1023-1030. doi: 10.1109/FUZZ-IEEE.2016.7737800
4. Changhong Fu, Ran Duan, Dogan Kircali, Erdal Kayacan. Onboard Robust Visual Tracking for UAVs Using a Reliable Global-Local Object Model. Sensors, 16(9), 2016. doi:10.3390/s16091406


Chee Seng Chan
Derek T. Anderson
James M. Keller

Fuzzy Set Theory in Computer Vision

The goal of this tutorial is to educate and highlight challenges in modern computer vision (CV) and possible directions, tools and novel ideas that the fuzzy community may contribute. Focus will be placed on fuzzy set (FS) theory and some neural networks (NN), in particular deep learning. We will discuss challenges in CV that are recognized by researchers in the areas of low-, mid- and high-level CV. We review a few standard and modern CV approaches, discuss data sets currently used in the CV community, and we present some FS and NN techniques employed in each area, always with an eye towards where soft computing can make the largest impact. We will also provide an open source fuzzy computer vision toolbox (FCVT). At the end of this tutorial, we have an hour of hands on learning and Q&A with the FCVT for synthetic and community benchmark CV datasets. So this tutorial is a combination of theory and application!

Below is a tentative list of topics.

Part I: Introduction to Computer Vision (CV)
- Why study CV?
- What are the different so-called “levels” of CV?
- CV is steeped in probabilistic and neural approaches
- CV heavy communities include PAMI, ICCV, CVPR, ECCV, NIPS, etc.
- CV is a comparison heavy field (methods and data sets)
- Available CV tools: OpenCV, SimpleCV, VLFeat, etc.
- Push to provide open source code: educational and basis for benchmarking
- David Marr: principles of least commitment and graceful degradation
- Where does FS theory fit, e.g., offer the most pay off and/or make the most sense?

Part II: Feature Learning via Deep Learning
- Benefits of learning vs designing features, what is deep learning, and brief survey of deep learning approaches
- Mathematics, architecture and implementation details related to convolutional neural networks (CNNs)
- Existing work and role of FS theory in deep learning

Part III: Data and Information Fusion in CV
- What is data/information fusion and why does CV need it?
- Introduction to fuzzy integrals, extensions and data-driven learning techniques
- Where to fuse in CV? We will focus on signal, spectrum, feature and decision level fusion

Lessons and Q&A: Open Source Fuzzy Computer Vision Toolbox (FCVT)
- Crisp and fuzzy features
- Crisp and fuzzy classification
- Fuzzy integrals and data driven learning algorithms
- Convolutional neural networks
- Fuzzy deep learning


Naoyuki Kubota

Fuzzy Robotics

Fuzzy Computing has been applied to various types of research and development in intelligent robotics until now. This tutorial consists of state of the art in intelligent robotics with a focus on fuzzy computing. First, we explain the history on intelligent robotics and the introduction to intelligent robotics including map building, path planning, navigation, and control. The second part of the talk includes how to apply fuzzy computing to two interesting research topics; (1) simultaneous localization and mapping, and (2) multi-robot formation behaviors. Finally, we show several other research topics related with human-robot communication, and discuss the future direction of intelligent robotics.

Dr. 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 Information Technologies (Centro Singular de Investigacion en Tecnoloxias da Informacion, CiTIUS). He is currently secretary of the European Society for Fuzzy Logic and Technology (EUSFLAT), Vice-chair of the Task Force on "Fuzzy Systems Software" in the Fuzzy Systems Technical Committee of the IEEE Computational Intelligence Society, and Associate Editor of the IEEE Computational Intelligence Magazine (ISSN:1556-603X). He has published more than 85 papers in international journals, book chapters and in peer-review conferences. According to Google Scholar (accessed on October 29, 2016) he has h-index=14 and i10-index=24. His research interests include computational intelligence, natural language generation, development of free software tools, fuzzy modeling for control and classification problems, assessing interpretability of fuzzy systems, knowledge extraction and representation, integration of expert and induced knowledge, sensory analysis, advance multi-sensor fusion, WiFi localization, and autonomous robotic navigation in complex environments.
Prof. Jesus Alcala-Fdez received the M.Sc. degree in Computer Science in 2002 and the Ph.D. degree in Computer Science in 2006, both from the University of Granada, Spain. From 2005 to 2007, he was with Department of Computer Science, University of Jaén. He is currently an Associate Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada. He has published over 75 papers in international journals, book chapters and conferences, carrying an h index of 14 (source: Thomson Reuters's Web of Knowledge) and an i10 index of 31 (source: Google Scholar). He has worked on several research projects supported by the Spanish government, the Andalusia government and business. As edited activities, he has co-edited the IEEE Transactions on Fuzzy Systems Special Issue on "Genetic Fuzzy Systems: What's next?", the Journal of Multiple-Valued Logic and Soft Computing Special Issue on "Soft Computing Techniques in Data Mining", International Journal of Computational Intelligence Systems Special Issue on "Software Tools for Soft Computing", and IEEE Computational Intelligence Magazine Special Issue on "Computational Intelligence Software". He has co-organized special sessions on "Software for Soft Computing" in each FUZZ-IEEE conference since 2012. He was Publicity-Chair at GEFS 2010, GEFS 2011 and GEFS 2013, and Special Sessions & Workshops-Chair at ISDA 2011. His current research interests include data mining, association rules, genetic fuzzy systems, multiobjective evolutionary algorithms, and data mining software.
Autilia Vitiello took the master degree cum laude in Computer Science at the University of Salerno (Italy) in July 2009, defending a thesis in Time Sensitive Fuzzy Agents: formal model and implementation. From November 2009 to October 2012 she attended Ph.D. Program at Department of Computer Science of the University of Salerno. From September to December 2012, she was a visiting student at the School of Industrial Engineering, Information Systems, Eindhoven University of Technology, The Netherlands. She got Ph.D. in Computer Science on April 15th, 2013, defending a thesis titled Memetic Algorithms for Ontology Alignment. From July 2013, she is research fellow at the Department of Computer Science of the University of Salerno. She is also Member of the IEEE CIS Standards Committee and Chair of the Task Force named Datasets for Computational Intelligence. She is part of the IEEE Standard Association 1855 Working Group for Fuzzy Markup Language Standardization where she also serves as Secretary. Her main research area is Computational Intelligence, and in particular, Fuzzy Logic and Evolutionary Algorithms. Her recent interests include the integration between Computational Intelligence and Computer Vision to address Bloodstain Pattern Analysis and the integration between Evolutionary Algorithms and Machine Learning techniques to tackle Big Data Challenges.
Prof. Giovanni Acampora earned his Master’s degree in Computer Science at the University of Salerno in 2003. He earned his Ph.D. in Computer Science at the University of Salerno in 2007. He is Associate Professor in Artificial Intelligence and Quantum Computing at University of Naples Federico II and Working Group Chair of the IEEE 1855 Working Group at the IEEE Standards Association. Giovanni was previously Reader in Computational Intelligence at Nottingham Trent University, Chair of Standards Committee at IEEE Computational Intelligence Society; Hoofddocent Tenure Track in Process Intelligence at Eindhoven University of Technology; Secretary and Treasurer at IEEE CIS Italian Chapter; Research Fellow at University of Salerno; Chair of the Task Force on Terminology and Taxonomy – IEEE CIS Standards Committee at IEEE Computational Intelligence Society; and Professor at UniPegaso. His papers include Using FML and fuzzy technology in adaptive ambient intelligence environments, Diet assessment based on type-2 fuzzy ontology and fuzzy markup language, A Survey on Ambient Intelligence in Health Care, Fuzzy control interoperability and scalability for adaptive domotic framework, Interoperable and adaptive fuzzy services for ambient intelligence applications, and A hybrid evolutionary approach for solving the ontology alignment problem.
Isaac Triguero received the M.Sc. and Ph.D. degrees in Computer Science from the University of Granada, Granada, Spain, in 2009 and 2014, respectively. He is currently an Assistant Professor in Data Science at the School of Computer Science of the University of Nottingham. He has published more than 25 international journal papers as well as more than 20 contributions to conferences. His research interests include data mining, data reduction, biometrics, optimization, evolutionary algorithms, semi-supervised learning, bioinformatics and big data learning.
Alberto Fernández received the M.Sc. and Ph.D. degrees in computer science from the University of Granada, Granada, Spain, in 2005 and 2010, respectively. He is currently an Assistant Professor with the Department of Computer Science and Artificial Intelligence, University of Granada, Spain. He has published more than 100 papers in highly rated JCR journals and international conferences. In 2013 and 2014 Dr. Fernández received the University of Granada Prize for Scientific Excellence Works in the field of Engineering for the papers ``Advanced non-parametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining'' and ``KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework.'' His research interests include classification in imbalanced domains, fuzzy rule learning, evolutionary algorithms, multiclassification problems with ensembles and decomposition techniques, and data science in big data applications.
Francisco Herrera (SM'15) received his M.Sc. in Mathematics in 1988 and Ph.D. in Mathematics in 1991, both from the University of Granada, Spain. He is currently a Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada. He has been the supervisor of 38 Ph.D. students. He has published more than 300 journal papers that have received more than 46000 citations (Scholar Google, H-index 109). He is co-author of the books "Genetic Fuzzy Systems" (World Scientific, 2001) and "Data Preprocessing in Data Mining" (Springer, 2015), "The 2-tuple Linguistic Model. Computing with Words in Decision Making" (Springer, 2015), "Multilabel Classification. Problem analysis, metrics and techniques" (Springer, 2016). He currently acts as Editor in Chief of the international journals "Information Fusion" (Elsevier) and “Progress in Artificial Intelligence (Springer). He acts as an editorial member of a dozen of journals. He received the following honors and awards: ECCAI Fellow 2009, IFSA Fellow 2013, 2010 Spanish National Award on Computer Science ARITMEL to the "Spanish Engineer on Computer Science", International Cajastur "Mamdani" Prize for Soft Computing (Fourth Edition, 2010), IEEE Transactions on Fuzzy System Outstanding 2008 and 2012 Paper Award (bestowed in 2011 and 2015 respectively), 2011 Lotfi A. Zadeh Prize Best paper Award of the International Fuzzy Systems Association, 2013 AEPIA Award to a scientific career in Artificial Intelligence (September 2013), and 2014 XV Andalucía Research Prize Maimónides (by the regional government of Andalucía). His current research interests include among others, soft computing (including fuzzy modeling and evolutionary algorithms), information fusion, decision making, bibliometrics, biometric, data preprocessing, data science and big data.
Dr. Hamid Tizhoosh received the MSc degree in electrical engineering with a major in computer science from the University of Technology, Aachen, Germany, in1995. From 1993 to 1996, he worked at Management of Intelligent Technologies Ltd., Aachen, Germany in the field of industrial image processing. Dr. Tizhoosh received his Ph.D. degree from University of Magdeburg, Germany, in2000 with the subject of fuzzy processing of medical images. Dr. Tizhoosh was active as the scientist in the engineering department of IPS (Image Processing Systems Inc., now Photon Dynamics), Markham, Canada, until 2001. For six months, he visited the Knowledge/Intelligence Systems Laboratory, University of Toronto, Canada. Since September 2001, Dr. Tizhoosh is a faculty member at the department of Systems Design Engineering, University of Waterloo, Canada. At the same time, he has been the Chief Technology Officer and Chief Executive Officer of Segasist Technologies, a software company (Toronto, Canada) developing innovative software for medical image analysis. His research encompasses machine learning, fuzzy logic and computer vision. Dr. Tizhoosh has extensive experience in medical imaging including portal (megavoltage) imaging, x-rays, MRI and ultrasound. He has been a member of the European Union Projects INFOCUS and ARROW for radiation therapy to improve the integration of online images within the treatment planning of cancer patients. Dr. Tizhoosh has extensively published on fuzzy techniques in image processing. He is the author of two books, 14 book chapters, and more than 100 journal/conference papers.
Christian Wagner is a Visiting Professors at the Institute of Computing and Cybersystems at the Michigan Technological University, USA and an Associate Professor in Computer Science at the University of Nottingham, UK. He received his PhD in Computer Science from the University of Essex in 2009 after which he was involved both in the management and scientific work of the EU FP7 project ATRACO, joining the University of Nottingham in 2011. His main research interests are centred on uncertainty handling, approximate reasoning (reasoning in the face of uncertainty, lack of knowledge and vagueness), decision support and data-driven decision making using computational intelligence techniques. Recent applications of his research have focused in particular on decision support in environmental and infrastructure planning & management contexts as well as cyber-security. He has published more than 60 peer-reviewed articles in international journals and conferences, two of which recently won best paper awards (Outstanding IEEE Transactions on Fuzzy Systems paper 2013 (for a paper in 2010) and a best paper award for a Fuzz-IEEE 2012 conference paper), and several book chapters. Dr Wagner is currently active PI and Co-I on a number of research projects, with overall funding as PI of £1 million and funding as Co-I of 2 million. He is an Associate Editor of the IEEE Transactions on Fuzzy Systems journal (IF: 6.3) and is actively involved in the academic community through for example the organization of special sessions at premiere IEEE conference such as the World Congress on Computational Intelligence 2016 and the IEEE Conference on System, Man and Cybernetics 2015. He has developed and been involved in the creation of multiple open source software frameworks, making cutting edge research accessible both to peer researchers as well as to different (multidisciplinary - beyond computer science) research and practitioner communities, including R and Java based toolkits for type-2 fuzzy systems in use in more than ten countries.
Prof. Jon Garibaldi is Head of the Intelligent Modelling and Analysis (IMA) Research Group in the School of Computer Science at the University of Nottingham. His main research interest is in developing intelligent techniques to model human reasoning in uncertain environments, with a particular emphasis on the medical domain. Prof. Garibaldi has been the PI on EU and EPSRC projects worth over £3m, and CoI on a portfolio of grants worth over £25m. He is Director of the University of Nottingham Advanced Data Analysis Centre, established in 2012 to provide leading-edge data analysis services across the University and for industrial consultancy. His experience of leading large research projects includes his roles as Lead Scientist and Co-ordinator of BIOPTRAIN, a Marie-Curie Early Stage Training network in bioinformatics optimisation worth over €2m, the local co-ordinator of the €6.4m BIOPATTERN FP6 Network of Excellence, lead Computer Scientist on a £700k MRC DPFS (Developmental Pathway Funding Scheme) project to transfer the Nottingham Prognostic Index for breast cancer prognosis into clinical use. Industrial projects include a TSB funded project for data analysis in the transport sector, and a collaborative project with CESG (GCHQ) investigating and modelling variation in human reasoning in subjective risk assessments in the context of cyber-security. He is currently the local PI for Nottingham on the £900k UKCRC Joint Funders Tissue Directory and Coordination Centre, a CoI on the £14m BBSRC/EPSRC Synthetic Biology Research Centre in Sustainable Routes to Platform Chemicals, and was CoI on the £10m BBSRC/EPSRC Centre for Plant Integrative Biology. Prof. Garibaldi has published over 200 articles on fuzzy systems and intelligent data analysis, including over 50 journal papers and over 150 conference articles, three book chapters, and three co-edited books. He is an Associate Editor of Soft Computing, was Publications Chair of FUZZ-IEEE 2007 and General Chair of the 2009 UK Workshop on Computational Intelligence, and has served regularly in the organising committees and programme committees of a range of leading international conferences and workshops, such as FUZZ-IEEE, WCCI, EURO and PPSN. He is a member of the IEEE.
Prof. Robert John is a Professor of Operational Research and Computer Science and Head of the ASAP research group. He is a senior member of IEEE, fellow of the British Computer Society and elected member of the EPSRC college. In the field of type-2 fuzzy logic, his work is widely recognised by the international fuzzy logic community as leading in the aspects of theoretical foundations, as well as practical applications. His work has produced many fundamental new results that have opened the field to new research, enabling a broadening of scope and application. He is associate editor of the journal Soft Computing and the International Journal of Information & System Sciences, and member of the editorial board of International Journal of Cognitive Neurodynamics, Grey Systems: Theory and Application, Turkish Journal of Fuzzy Systems, International Journal for Computational Intelligence and Information and System Sciences. He chaired EUSFLAT2001 organised on behalf of the European Society of Fuzzy Logic and Technology and held at De Montfort University. He has over 150 publications of which circa 50 are in international journals and many papers are very well cited.
Dr McCulloch's main research focuses on using type-2 fuzzy sets to model uncertain data that has been collected from multiple sources and may contain contradictions. Her work involves the aggregation of such information, and developing useful measures of analysis on the resulting models. She has applied this within the field of Fast Moving Consumer Goods to influence decision-making by enabling consumers to use human-like queries to find their desired product.
Dr. Erdal Kayacan holds a PhD in Electrical and Electronic Engineering from Bogazici University in Istanbul, Turkey (2011). He also holds a Bachelor of Science in Electrical Engineering from Istanbul Technical University (2003) and a Master of Science in Systems and Control Engineering from Bogazici University in Istanbul, Turkey (2006). After his post-doctoral research in KU Leuven at the division of mechatronics, biostatistics and sensors (MeBioS), Dr. Kayacan went on to pursue his research in Nanyang Technological University’s School of Mechanical and Aerospace Engineering as assistant professor (2014 – current). He has since published more than 50 refereed journal and conference papers in intelligent control, fuzzy systems and grey systems theory. His research areas cover flight dynamics and control, unmanned aerial vehicles, robotics, mechatronics, soft computing methods, sliding mode control and model predictive control. Dr. Kayacan is co-writer of a course book “Fuzzy Neural Networks for Real Time Control Applications, 1st Edition Concepts, Modeling and Algorithms for Fast Learning", Butterworth-Heinemann, Print Book ISBN:9780128026878. (17 Sept 2015). He is a Senior Member of Institute of Electrical and Electronics Engineers (IEEE) and active in IEEE SMC Technical Committee on Grey Systems. He is an editor of Journal on Automation and Control Engineering (JACE) and sits in the editorial advisory board of Grey Systems: Theory and Application journal.
Dr. Changhong Fu received his Ph.D. degree in Robotics and Automation from Technical University of Madrid (UPM), Spain. During his Ph.D., he held two research positions at Arizona State University (ASU), USA and Nanyang Technological University (NTU), Singapore. After finishing his Ph.D., he is currently working at the school of Mechanical & Aerospace Engineering (MAE) and ST Engineering (STE)-NTU Corp Lab in the NTU as a Post-Doctoral Research Fellow. He has worked on 2 international, 1 national and 4 industry technology transfer projects related to the computer vision for UAVs. He is serving as the reviewers for multiple top-level journals and conferences, e.g., IEEE T FUZZY SYST and ICRA. His research areas are Visual Tracking, Odometry, SLAM and Intelligent Control for UAV Autonomy.
Chee Seng Chan is a Senior Lecturer in the Faculty of Computer Science and Information Technology, University of Malaya, Malaysia. He received his PhD from University of Portsmouth, U.K. in 2008. His research interests spans a variety of aspects of fuzzy qualitative reasoning and computer vision; with a focus on image/video content analysis. He is the founder chair for the IEEE Computational Intelligence Society (CIS) Malaysia chapter and founder of the Malaysian Image Analysis and Machine Intelligence Association (MIAMI), a society under the International Association of Pattern Recognition (IAPR). He is/was the organizing chair for the Asian Conference on Pattern Recognition (ACPR) in 2015, general chair for the IEEE Visual Communications and Image Processing (VCIP) in 2013, and has co-chaired numerous special sessions at FUZZ-IEEE (2010-2015). He is a recipient of the Hitachi Research Fellowship in 2013 and the IET (Malaysia) Young Engineer award in 2010. Finally, he is a senior member of IEEE, a chartered engineer and member of IET.
Derek T. Anderson received his Ph.D. in Electrical and Computer Engineering (ECE) from the University of Missouri in 2010. He is an Assistant Professor in ECE at Mississippi State University (MSU). Prof. Anderson also holds an Intermittent Faculty Appointment with the U.S. Naval Research Laboratory, he is an IEEE Senior Member and Associate Editor for the IEEE Trans. on Fuzzy Systems. His research interests are new frontiers in information fusion for pattern recognition and automated decision making in signal/image understanding and computer vision with an emphasis on uncertainty and heterogeneity. Prof. Anderson’s primary research contributions to date include multisource (meaning sensor, algorithm and human) fusion, Choquet integrals (extensions, embedding’s, learning), signal/image feature learning, multi-kernel learning, cluster validation, hyperspectral image understanding and linguistic summarization of video. He received the Best Student Paper Award at FUZZ-IEEE 2008, Best Paper Award at FUZZ-IEEE 2012 and was a co-author of the Best Student Paper Award at SPIE in ATR in 2013. Prof. Anderson has led and co-organized special sessions, tutorials and workshops at international conferences. He has been funded by the U.S. Air Force Research Laboratory, Camgian, U.S. Army and Night Vision and Electronics Sensors Directorate, U.S. Army Engineering Research and Development Center, Pacific Northwest National Laboratory, the National Institute of Justice, and the Defense Advanced Research Project Agency. Prof. Anderson is the also the CoDirector of the Sensor Analysis and Intelligence Laboratory (SAIL) in the Center for Advanced Vehicular Systems, a multi-disciplinary collaborative research laboratory with sensors ranging from hyperspectral (VNIR, MWIR, and LWIR) to Radar, LiDAR, stereoscopic, to light field cameras. SAIL is focused on fusion and scene/environment understanding for robotics, autonomous systems (ground/air vehicles), and remote sensing from UAVs for agriculture and earth observations. Derek has published over 90 articles; book chapters, journal manuscripts and conference proceedings. More details can be found at: http://www.derektanderson.com
James M. Keller holds the University of Missouri Curators’ Distinguished Professorship in the Electrical Engineering and Computer Science Department on the Columbia campus. He is also the R. L. Tatum Professor in the College of Engineering. His research interests center on computational intelligence with a focus on problems in computer vision, pattern recognition, and information fusion including bioinformatics, spatial reasoning, geospatial intelligence, landmine detection and technology for eldercare. Professor Keller has coauthored over 450 technical publications. Jim is a Life Fellow of the IEEE, an IFSA Fellow, and past President of NAFIPS. He received the 2007 Fuzzy Systems Pioneer Award and the 2010 Meritorious Service Award from the IEEE Computational Intelligence Society. He finished a full six year term as Editor-in-Chief of the IEEE Transactions on Fuzzy Systems, followed by being the Vice President for Publications of the IEEE CIS from 2005-2008, and then an elected CIS Adcom member. Jim is now beginning a new term as VP Pubs for CIS. He is the IEEE TAB Transactions Chair and a member of the IEEE Publication Review and Advisory Committee. Jim has had many conference positions and duties over the years.
Naoyuki Kubota is currently a professor of the Department of System Design, Tokyo Metropolitan University, Japan. He graduated from Osaka Kyoiku University in 1992, received the M.E. degree from Hokkaido University in 1994, and received the D.E. degree from Nagoya University, Japan in 1997. He was an assistant professor and lecturer at the Department of Mechanical Engineering, Osaka Institute of Technology, Japan, from 1997 to 2000. He joined the Department of Human and Artificial Intelligence Systems, Fukui University, Japan, as an associate professor in 2000. He joined the Department of Mechanical Engineering, Tokyo Metropolitan University, Japan, as an associate professor in 2004. He was an associate professor from 2005 to 2012, and has been a professor since 2012 at the Department of System Design in Tokyo Metropolitan University, Japan. He was a visiting professor at University of Portsmouth, UK, in 2007 and 2009, and was an invited visiting professor at Seoul National University from 2009 to 2012. His current interests are in the fields of fuzzy control, spiking neural networks, co-evolutionary computation, perception-based robotics, robot partners, and informationally structured space. He has published more than 200 refereed journal and conference papers in the above research fields.