We are excited to announce the following keynote speakers:
Piero Bonissone, Piero P Bonissone Analytics, LLC
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Title: PHM Analytics for Industrial AI
Abstract: In the past, analytic model creation was an artisanal process, as models were handcrafted by experienced, knowledgeable model-builders. More recently, the use of meta-heuristics, such as evolutionary algorithms, has provided us with limited levels of automation in model building and maintenance. Now, we expect data-driven analytic models to become a commodity. We envision having access to a large number of data-driven models, obtained by a combination of crowdsourcing, cloud-based evolutionary algorithms, public-domain libraries, outsourcing, in-house development, and legacy models. In this context, the critical issue will be model ensemble selection and fusion, rather than model generation.
First, we will review the application of data-driven analytic models to assets Prognostics and Health Maintenance (PHM) such as aircraft engines, medical imaging devices, and locomotives. We will cover a few case studies on anomaly detection, diagnosis, prediction, and optimization.
Then we will describe the evolution of analytic models with the advent of cloud computing, and propose the use of customized model ensembles on demand, inspired by Lazy Learning. This approach is agnostic with respect to the origin of the models, making it scalable and suitable for a variety of applications. We successfully tested this approach in a regression problem for a power plant management application, using two different sources of models: bootstrapped neural networks, and GP-created symbolic regression models evolved in the cloud. We will present results on the fusion of models for FlyQuest, a GE-sponsored Kagglecompetition, in which we crowdsourced the generation of models predicting the estimated runway and gateway arrival (ERA, EGA) over a month of US flights.
Finally, we will explore research trends, challenges and opportunities for Machine Learning techniques in this emerging context of big data and cloud computing.
Bio: Dr. Bonissone is an independent consultant specialized in the use of analytics for Industrial AI applications. He provides consulting services in machine learning (ML) analytic applications, covering project definition and risk abatement, project evaluation, transition from development to deployment, and model maintenance. He has been an Advanced Analytics Advisor for Parkland. Stanley Black Decker, GE Oil & Gas (prior to their integration with Baker Hughes), and Schlumberger, where he played a key role in Digital Transformation initiatives, such as part forecasting, market intelligence, PHM projects related to equipment reliability, etc.
A former Chief Scientist at GE Global Research (GE GR), where he retired in 2014 after 34 years of service, Dr. Bonissone has been a pioneer in the field of analytics, machine learning, fuzzy logic, AI, and soft computing applications. Over the last decade of his tenure at GE GR, he developed multi-criteria decision making systems to support PHM applications (prescriptive models), ensemble learning to reduce the variance of predictive models, and model lifecycle automation to create, deploy, and maintain analytic models, providing customized performance while adapting to avoid obsolescence.
He is a Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow in the Association for the Advancement of Artificial Intelligence (AAAI), the International Fuzzy Systems Association (IFSA), and a Coolidge Fellow at GE Global Research. He received the 2012 Fuzzy Systems Pioneer Award from the IEEE CIS. During 2010-15, he chaired the Scientific Committee of the European Centre for Soft Computing. In 2008 he received the II Cajastur International Prize for Soft Computing from the European Centre of Soft Computing. In 2005 he received the Meritorious Service Award from the IEEE CIS. He received two Dushman Awards from GE GR. He served as Editor-in-Chief of the International Journal of Approximate Reasoning for 13 years.
He is in the editorial board of five technical journals and is Editor at Large of the IEEE Computational Intelligence Magazine. He co-edited six books and has 180+ publications in refereed journals, book chapters, and conference proceedings, with 11,500+ citations, an H-Index of 57 and an i10-index of 171 (by Google Scholar). He received 74 patents issued by the US Patent Office. From 1982 until 2005 he has been an Adjunct Professor at Rensselaer Polytechnic Institute, in Troy NY, where he supervised 5 Ph.D. theses and 34 Master theses. He co-chaired 12 scientific conferences focused on Multi-Criteria Decision-Making, Fuzzy sets, Diagnostics, Prognostics, and Uncertainty Management in AI.
He has been a member of the IEEE Fellow Committee in 2007-09; 2012-14, and 2016-17. In 2002, while serving as President of the IEEE Neural Networks Society (now IEEE CIS), he was a member of the IEEE Technical Activity Board. He has been an Executive Committee member of NNC/NNS/CIS society in 1993-2012 and 2016-18 and an IEEE CIS Distinguished Lecturer in 2004-14 and 2017-19. He has been a judge in the IEEE Fellow Committee for ten years, since 2007. Currently he is in his second term as the Vice-Chair of the IEE Fellows Committee.
Jerry Mendel, University of Southern California
Title: Explainable AI (XAI) for Rule-Based Fuzzy Systems
Abstract: There is a sentiment in the fuzzy community that fuzzy rules would be of great value in XAI because such rules use words (which are modeled as fuzzy sets) and so they lend themselves naturally to XAI. This talk challenges that sentiment, in a constructive way. It explains why it is not valid to explain the output of Mamdani or TSK fuzzy systems using IF-THEN rules, but that it is valid to explain the output of such fuzzy systems as an association of the antecedents of a small subset of the original larger set of rules, using a phrase such as “These linguistic antecedents are symptomaticof this output”. It also describes a novel multi-step approach to obtain such a small subset of rules for fuzzy systems, how Linguistic Approximation can be used to express the antecedent membership functions (the symptoms) linguistically, and a method for estimating the quality of linguistic explanations.
Bio: Jerry M. Mendel (LF’04) 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. 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., Perceptual Computing: Aiding People in Making Subjective Judgments, and Introduction to Type-2 Fuzzy Logic Control: Theory and Application. 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, and a Fuzzy Systems Pioneer Award (2008) from the IEEE Computational Intelligence Society. His present research interests include: type-2 fuzzy sets and systems, and XAI.
Ljiljana Trajkovic, Simon Fraser University
Title: Data mining and machine learning for detecting traffic anomalies and intrusions
Abstract: Traffic traces collected from deployed communication networks have been used to characterize and determine traffic loads, analyze patterns of users' behavior, model network traffic, and predict future network traffic. Data have been also used to analyze network topologies and capture historical trends in their development. Of particular interest to cybersecurity is detection of network anomalies and intrusions including worms, denial of service attacks, ransomware, and blackouts. Various anomaly detection approaches such as time series and historical-based analysis, statistical validation, reachability checks, and machine learning may be applied to analyze collected datasets. Machine learning techniques have proved to be valuable tools for predicting anomalous Internet traffic behavior and for classifying various traffic routing anomalies. In this talk, we describe and evaluate performance of models based on recurrent neural networks and broad learning system that have been employed for detecting malicious intentions of network users.
Bio: Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, in 1974, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, in 1979 and 1981, respectively, and the Ph.D. degree in electrical engineering from University of California at Los Angeles, in 1986.
She is currently a Professor in the School of Engineering Science at Simon Fraser University, Burnaby, British Columbia, Canada. From 1995 to 1997, she was a National Science Foundation (NSF) Visiting Professor in the Electrical Engineering and Computer Sciences Department, University of California, Berkeley. She was a Research Scientist at Bell Communications Research, Morristown, NJ, from 1990 to 1997, and a Member of the Technical Staff at AT&T Bell Laboratories, Murray Hill, NJ, from 1988 to 1990. Her research interests include high-performance communication networks, control of communication systems, computer-aided circuit analysis and design, and theory of nonlinear circuits and dynamical systems.
Dr. Trajkovic served as IEEE Division X Delegate/Director (2019–2020) and IEEE Division X Delegate-Elect/Director-Elect (2018). She served as Senior Past President (2018–2019), Junior Past President (2016–2017), President (2014–2015), President-Elect (2013), Vice President Publications (2012–2013, 2010–2011), Vice President Long-Range Planning and Finance (2008–2009), and a Member at Large of the Board of Governors (2004–2006) of the IEEE Systems, Man, and Cybernetics Society. She served as 2007 President of the IEEE Circuits and Systems Society and a member of its Board of Governors (2004–2005, 2001–2003). She is Chair of the IEEE Circuits and Systems Society joint Chapter of the Vancouver/Victoria Sections. She was Chair of the IEEE Technical Committee on Nonlinear Circuits and Systems (1998). She was General Co-Chair of SMC 2020 and SMC 2020 Workshop on BMI Systems and served as General Co-Chair of SMC 2019 and SMC 2018 Workshops on BMI Systems, SMC 2016, and HPSR 2014, Special Sessions Co-Chair of SMC 2017, Technical Program Chair of SMC 2017 and SMC 2016 Workshops on BMI Systems, Technical Program Co-Chair of ISCAS 2005, and Technical Program Chair and Vice General Co-Chair of ISCAS 2004. She serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems (2021–2023) and served as an Associate Editor of the IEEE Transactions on Circuits and Systems (Part I) (2004–2005, 1993–1995), the IEEE Transactions on Circuits and Systems (Part II) (2018, 2002–2003, 1999–2001), and the IEEE Circuits and Systems Magazine (2001–2003). She is a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society (2020–2021) and the IEEE Circuits and Systems Society (2020–2021, 2010–2011, 2002–2003). She is a Professional Member of IEEE-HKN and a Life Fellow of the IEEE.
Marek Reformat, University of Alberta
Title: Fuzziness and Web Intelligence
Abstract: Our dependency on information stored on the Web is growing. Yet, the increased amount of data available on the web – although recognized as a positive and beneficial fact – creates challenges regarding the easiness of its exploration and understanding. It rises pressure as well as expectations for providing more human-like interaction with web applications perceived as tools for better utilization and analysis of web data.
In this talk, we present a few fuzzy-based methods and techniques that enable web systems to provide a more human-like way of retrieving data, controlling its extraction processes, and providing its linguistic summarization.
In the first part of the presentation, we postulate that the application of fuzziness in systems supporting users in their search will allow them to guide and control mechanisms that identify alternatives, and influence recommendations. Fuzzy-based methods can be applied to scenarios where users want to relax their requirements. A methodology for selecting groups of individuals that satisfy linguistically described requirements regarding a degree of matching between users’ interests and collective interests of groups is presented. Following that, we present a Question-Answering (QA) system that allows users to ask questions in natural language. The uniqueness of this system is its ability to answer questions containing linguistic terms, i.e., concepts such as SMALL, LARGE, or TALL. Those concepts are defined via membership functions drawn by users using a dedicated software designed for entering ‘shapes’ of these functions. Finally, we describe a user-defined method for constructing linguistic summarization of multi-feature data. The method is able to select suitable summarizers and quantifiers, and works with linguistic constraints imposed on the data. The method utilizes definitions of linguistic terms provided by users with an easy and simple graphical interface.
Bio: Marek Reformat received his M.Sc. degree (with honors) from Technical University of Poznan, Poland, and Ph.D. from University of Manitoba, Canada. He is a Full Professor and Associate Chair of the Department of Electrical and Computer Engineering, University of Alberta.
The goal of his research activities is to develop methods and techniques for intelligent data modeling and analysis leading to translation of data into knowledge, as well as to design systems that possess abilities to imitate different aspects of human behavior. In this context, he recognizes the concepts of Computational Intelligence – with fuzzy computing and possibility theory in particular – as key elements necessary for capturing relationships between pieces of data and knowledge, and for mimicking human ways of reasoning about opinions and facts. Dr. Reformat also works on Computational Intelligence based approaches for dealing with web information. He applies elements of fuzzy sets to social networks, Linked Open Data, and Semantic Web in order to handle inherently imprecise information, and provide users with unique facts retrieved from the data. All his activities focus on introduction of human aspects to web and software systems what will lead to more human-aware and human-like systems.
He has published over 100 peer-reviewed publications in the areas of computational intelligence, knowledge and software engineering. He is an Associate Editor of a number of international journals including IEEE TFS, Fuzzy Sets and Systems, International Journal of Intelligent Systems, Knowledge-based Systems, Human-Centric Computing and Information Sciences, and Journal of Software Engineering and Knowledge Engineering. He has been a general and program chair, as well as a member of program committees of numerous international conferences related to Computational Intelligence and Software Engineering.
He is a past president of the North American Fuzzy Information Processing Society (NAFIPS), and a president of the International Fuzzy Systems Association (IFSA).
Dimitar Filev, Ford Motor Company
Title: Trends in AI Inspired Automated Driving Policies
Abstract: The presentation reviews the main autonomous vehicles architectures. The focus is on the applications of AI to the algorithms for decision making for path planning. Included is an overview of the methods for developing AI inspired driving policies. The relationship between the reinforcement learning based solutions and the rule-based and model-based techniques for improving their explainability, robustness and safety are reviewed. Applications of fuzzy systems are also discussed. The presentation concludes with the lessons learned, on-going research and future trends in integrating AI technologies within the design of autonomous vehicles.
Bio: Dr. Dimitar Filev is Henry Ford Technical Fellow in Control and AI with Research & Advanced Engineering – Ford Motor Company. He is conducting research in computational intelligence, AI and intelligent control, and their applications to autonomous driving, vehicle systems, and automotive engineering. He holds over 100 granted US patents, has published over 200 research publications and has been awarded with the IEEE SMCS 2008 Norbert Wiener Award and the 2015 Computational Intelligence Fuzzy Pioneer’s Award. Dr. Filev is a Fellow of the IEEE and a member of the NAE. He was President of the IEEE Systems, Man, & Cybernetics Society (2016-2017) and President of NAFIPS (2006-2007).
Yingxu Wang, University of Calgary
Title: Advances in Intelligence Mathematics (IM) following Lotfi Zadeh’s Vision on Fuzzy Logic and Semantic Computing
Abstract: Late Professor Lotfi A. Zadeh’s most important contribution to number theory is his unprecedented vision that extends the traditional domain of real numbers (R) to fuzzy numbers (F) in order to deal with 2D mathematical structures and nonquantifiable entities in contemporary knowledge and intelligence sciences. Inspired by Zadeh, the latest advances in Intelligent Mathematics (IM) is introduced as a category of contemporary denotational mathematics extending classic analytic mathematics defined in R to nD hyperstructures (H) in cognitive and computational intelligence. This keynote presents IM paradigms including fuzzy probability algebra, fuzzy semantic algebra, fuzzy causal inferences, behavioral process algebra, concept algebra, big data algebra, system algebra, image frame algebra, etc. Applications of IM to address a wide range of challenging problems are demonstrated in contexts of cognitive robots, soft computing, cognitive linguistics, computational intelligence, and autonomous systems.
Bio: Dr. Yingxu Wang is professor of cognitive systems, brain science, software science, and intelligent mathematics. He is the Founding President of International Institute of Cognitive Informatics and Cognitive Computing (ICICC). He is FIEEE, FBCS, FICICC, and FWIF. He has held visiting professor positions at Univ. of Oxford (1995, 2018-22), Stanford Univ. (2008, 16), UC Berkeley (2008), and MIT (2012). He received a PhD in Computer Science from the Nottingham Trent University, UK, in 1998 and has been a full professor since 1994. He is the founder and steering committee chair of IEEE Int’l Conference Series on Cognitive Informatics and Cognitive Computing (ICCI*CC) since 2002. He is founding Editor-in-Chiefs of Int’l Journal of Cognitive Informatics & Natural Intelligence (IJCINI), of Software Science & Computational Intelligence (IJSSCI), of Advanced Mathematics and Applications (JAMA), and of Mathematical & Computational Methods (IJMCM). He is Associate Editor of IEEE Trans. on Systems, Man, and Cybernetics-Systems (TSMC-Systems), Cognitive and Development Systems (TCDS), and SMCM, and the IEEE Computer Society Representative to the steering committee of TCDS. He is Chair of IEEE SMCS TC-BCS on Brain-inspired Cognitive Systems, and Co-Chair of IEEE CS TC-CLS on Computational Life Science. He is an IEEE FDC Steering Committee Member on Symbiotic Autonomous Systems Initiative (SASI), and members of the IEEE Brain and SPS Autonomous Systems Initiatives. His basic research has been across contemporary science disciplines of intelligence, knowledge, robotics, computer, information, brain, cognition, software, data, systems, cybernetics, neurology, and linguistics. He has published 570+ peer reviewed papers and 36 books. He has presented 58 invited keynote speeches in international conferences. He has served as honorary, general, and program chairs for 39 international conferences. He has led 10+ international, European, and Canadian research projects as PI. His h-index is 54 with 15,300+ citations. He is recognized by Research Gate as among the top 2.5% scholars worldwide with a 47.0 RG score and 335,000+ reading-index.
Jin Wei Kocsis, Purdue University
Title: Intelligent Soft Computing-Based Security Control for Smart Energy Systems
Abstract: While the integration of cyber technology enables greater efficiency as well as capacity for smart energy systems, it also creates a host of new vulnerabilities that can potentially lead to devastating physical damage and even loss of life. Due to the advances in computing and sensing, data-driven solutions with innovations in machine learning (ML) show promising potentials in mitigating the vulnerabilities. However, there still remain essential challenges for developing practical and reliable ML-powered control strategies for securing power systems due to time complexity of the ML training and uncertainties of the sensing data. In this talk, Dr. Wei-Kocsis will present some of her research work in addressing these essential challenges and developing practical and reliable intelligent soft computing-based security control strategies for securing smart energy systems.
Bio: Dr. Jin Wei-Kocsis is an Assistant Professor in the Department of Computer and Information Technology at Purdue University. She is also a director of Cyber-Physical-Social Systems Design Lab. Her research interests include deep learning, cyber-physical-social security and privacy, intelligent control, and cognitive communications and networking. Based on the research and education achievements, Dr. Wei-Kocsis has received NASA Early Career Faculty Grant, DHS/FEMA Grant, DoE/SuNLaMP Award, and DoE/BIRD Foundation Award. She has also achieved multiple best paper awards for the journal and conference publications.