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Prof. Nikhil R. Pal
Indian Statistical Institute, Calcutta, India
South Asian University, New Delhi, India
Speech Title:
Making Fuzzy Systems Practical and Interpretable for High-Dimensional Data
Fuzzy systems are widely recognized for their interpretability and explainability; however, designing them for high-dimensional data remains a significant challenge. In particular, computing rule firing strengths using product T-norm or soft-min operators often suffers from numerical underflow and overflow, respectively. To overcome these issues, we recently developed a numerically stable framework for constructing high-dimensional fuzzy systems. Although effective, these systems can involve an excessive number of antecedent clauses, rendering the extracted rule base difficult for humans to comprehend.
To improve interpretability without compromising predictive performance, we introduce a series of approaches based on a universal feature-attenuating gate. The proposed framework automatically suppresses irrelevant and redundant features while learning fuzzy rule-based models for both classification and regression. Feature modulation is performed at both the antecedent and consequent levels, leading to compact and parsimonious rule bases. We also discuss an alternative sparse modeling formulation for problems such as switching regression, where feature selection in the consequent part is achieved through LASSO regularization instead of feature-attenuating gates.
Feature selection preserves the identity of the selected variables, but the resulting feature subset may fail to retain the intrinsic manifold structure of the original data. To address this limitation, we further propose a framework for learning low-dimensional feature representations that preserve the geometric structure of the original high-dimensional data while remaining suitable for fuzzy system design. This talk will present these developments and demonstrate how they contribute to the design of interpretable, parsimonious, and effective fuzzy systems for high-dimensional learning tasks.
Biography:
Prof. Nikhil R. Pal is an INSA Senior Scientist in the Electronics and Communication Sciences Unit of the Indian Statistical Institute (ISI). He is an Honorary Visiting Professor of the South Asian University, India. He was a former professor of ISI and was the founding Head of the Center for Artificial Intelligence and Machine Learning. He served as an INAE Chair Professor at ISI as well as a Chair Professor at the National Chiao Tung University, Taiwan. He also served as a visiting Professor of China University of Petroleum, East China; Huazhong University of Science and Technology, Wuhan; and the City University of Hong Kong. His current research interest includes brain science, computational intelligence, machine learning and artificial intelligence.
He was the Editor-in-Chief of the IEEE Transactions on Fuzzy Systems for the period January 2005-December 2010. He served/has been serving on the editorial /advisory board/ steering committee of several journals including the International Journal of Approximate Reasoning, Applied Soft Computing, International Journal of Neural Systems, Fuzzy Sets and Systems, IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Cybernetics.
He is a recipient of the 2015 IEEE Computational Intelligence Society (CIS) Fuzzy Systems Pioneer Award and 2021 IEEE CIS Meritorious Service Award. He has given many plenary/keynote speeches in different premier international conferences in the area of computational intelligence. He has served as the General Chair, Program Chair, and co-Program chair of several conferences. He was a Distinguished Lecturer of the IEEE CIS (2010-2012, 2016-2018, 2022-2024) and was a member of the Administrative Committee of the IEEE CIS (2010-2012). He has served as the Vice-President for Publications of the IEEE CIS (2013-2016) and the President of the IEEE CIS (2018-2019).
He is a Fellow of the West Bengal Academy of Science and Technology, Institution of Electronics and Tele Communication Engineers, National Academy of Sciences, India, Indian National Academy of Engineering, Indian National Science Academy, International Fuzzy Systems Association (IFSA), The World Academy of Sciences, and a Fellow of the IEEE, USA. (www.isical.ac.in/~nikhil).
Prof. Sung-Bae Cho
Underwood Distinguished Professor,
Yonsei University, South Korea
Speech Title:
Hybrid Artificial Intelligence for Sustainable Development and Social Good
Large language models open another renaissance of artificial intelligence (AI) that is a long dream of human-beings. Nowadays, they are recognized not only as one of promising techniques of AI, but also as a game changer for everyday life. In this talk, I will give the general overview of AI, and explore the transformative role of AI in dealing with several challenges, such as phishing attack detection, visual question answering, and brain disease diagnosis from fMRI images. AI offers immense potential to human society, but collaborative efforts among governments, private sectors, and civil society will be key to maximizing the benefits of AI for social good.
Biography:
Prof. Sung-Bae Cho received Ph.D. degree in computer science from KAIST (Korea Advanced Institute of Science and Technology). He was an invited researcher of Human Information Processing research laboratories at ATR (Advanced Telecommunications Research) institute, Japan, from 1993 to 1995, a visiting scholar at University of New South Wales, Australia, in 1998, a visiting professor at University of British Columbia, Canada, from 2005 to 2006, and at King Mongkut’s University of Technology at Thonburi, Thailand, in 2013. Since 1995, he has been a professor in department of computer science, Yonsei University, a Underwood distinguished professor from 2021, and a Yonsei Fellow from 2023. His research interests include neural networks, pattern recognition, intelligent man-machine interfaces, evolutionary computation, and artificial life. Dr. Cho was the recipient of the Richard E. Merwin prize from IEEE Computer Society in 1993. He received several distinguished investigator awards from Korea Information Science Society in 2005, and Gaheon Sindoricoh in 2017. He is also a recipient of service merit medal from Korean government in 2022. Currently he is the Fellow of IEEE, AAIA, KAST (Korean Academy of Science and Technology), and NAEK (National Academy of Engineering of Korea).
Prof. Kazuo Tanaka
The University of Electro-Communications, Tokyo, Japan
Speech Title:
Making Challenging UAVs Fly: Tractability and Open Problems in Fuzzy Control Theory
This talk will explore fuzzy model-based control, with a focus on tractability in modeling, analysis, and control design for complex real-world systems. While nonlinear control theory provides rigorous design tools, using these tools in practice often becomes difficult as system dynamics and design requirements grow more complex. Fuzzy model-based control offers a systematic and more tractable control design framework for such complex nonlinear systems.
After a brief review of representative developments based on linear matrix inequalities (LMIs) and sum-of-squares (SOS) techniques, the talk will highlight selected topics in modeling, analysis, and control design, with particular emphasis on our research group's work on challenging unmanned aerial vehicles (UAVs). Through this work, the talk will discuss both the practical potential of fuzzy control theory and open problems for its future development.
Biography:
Prof. Kazuo Tanaka is currently a Professor in the Department of Mechanical and Intelligent Systems Engineering at the University of Electro Communications, Tokyo, Japan. He received his Ph.D. in Systems Science from Tokyo Institute of Technology in 1990. He was a Visiting Scientist in Computer Science at the University of North Carolina at Chapel Hill in 1992 and 1993.
From his professional societies, he has received several prestigious awards including the IFAC World Congress Best Poster Paper Prize in 1999, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2000, the IEEE Computational Intelligence Society (CIS) Fuzzy Systems Pioneer Award in 2021, to name a few. He has been included each year in recent years on Stanford University’s list of the world’s top 2% of scientists.
His research interests include fuzzy systems control, nonlinear systems control and their applications to unmanned aerial vehicles. According to Google Scholar, his publications have received over 33,717 citations, and he has an h-index of 60 and an i10-index of 173. He is an IEEE Fellow and an IFSA Fellow.
Prof. Yusuke Nojima
Graduate School of Informatics, Osaka Metropolitan University, Japan
Speech Title:
Beyond Accuracy: Balancing Interpretability, Cost, and Fairness in Evolutionary Fuzzy Systems
Predictive accuracy alone does not determine the practical value of an AI model. This talk explores interpretability, inference cost, and fairness through three complementary studies on evolutionary fuzzy systems. First, we present a partially interpretable architecture combining an interpretable fuzzy classifier with an accurate black-box model. By delegating difficult inputs to the black-box model, it balances accuracy with interpretable coverage—the proportion of inputs handled by the interpretable component. Second, we introduce a hierarchy of fuzzy classifiers with different levels of complexity, generated through multiobjective fuzzy genetics-based machine learning. A reject option serves as a relay mechanism between classifiers, reducing the average number of attributes required for inference. Finally, we examine fairness-aware multiobjective fuzzy genetics-based machine learning that jointly optimizes accuracy and fairness. Analyses of rule complexity and attribute usage help explain the resulting accuracy–fairness relationships, showing that fairer models often have simpler rule bases. Together, these studies highlight how model cooperation, selective information use, and structural analysis can help balance multiple requirements in evolutionary fuzzy systems beyond predictive accuracy.
Biography:
Prof. Yusuke Nojima received the B.S. and M.S. degrees in Mechanical Engineering from Osaka Institute of Technology, Osaka, Japan, in 1999 and 2001, respectively, and the Ph.D. degree in System Function Science from Kobe University, Hyogo, Japan, in 2004. From 2004 to 2022, he was with Osaka Prefecture University, Osaka, Japan, where he became a Professor in the Department of Computer Science and Intelligent Systems in October 2020. Since April 2022, he has been a Professor in the Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Osaka, Japan. Since April 2026, he has also served as Vice Dean of the Graduate School of Informatics.
His research interests include evolutionary fuzzy systems and evolutionary multiobjective optimization. He has published 70 international journal papers and over 270 international conference papers. He was a guest editor for several special issues of international journals, chaired the Task Forces on Evolutionary Fuzzy Systems and on Competitions within the Fuzzy Systems Technical Committee of the IEEE Computational Intelligence Society, and served as an Associate Editor of IEEE Computational Intelligence Magazine. He is currently President-Elect of the International Fuzzy Systems Association (IFSA).
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