Prof. Nikhil Pal, the Indian Statistical Institute, India
Bio: Nikhil R. Pal (www.isical.ac.in/~nikhil) is an INAE Chair Professor in the Electronics and Communication Sciences Unit of the Indian Statistical Institute. His current research interest includes bioinformatics, brain science, fuzzy logic, neural networks, machine learning, and data mining. He was the Editor-in-Chief of the IEEE Transactions on Fuzzy Systems (January 2005-December 2010). He has served/been serving on the editorial /advisory board/ steering committee of several journals including the International Journal of Approximate Reasoning, Applied Soft Computing, Fuzzy Sets and Systems, Fuzzy Information and Engineering : An International Journal, IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Systems Man and Cybernetics B (currently IEEE Transactions on Cybernetics). He is a recipient of the 2015 Fuzzy Systems Pioneer 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 Computational Intelligence Society (CIS) and was a member of the Administrative Committee of the IEEE CIS. At present he is the Vice President for Publications of the IEEE CIS. He is a Fellow of the National Academy of Sciences, India; the Indian National Academy of Engineering; the Indian National Science Academy, the International Fuzzy Systems Association (IFSA), and IEEE, USA.
Prof. Jacek Mańdziuk, Warsaw University of Technology, Poland
Bio: Jacek Mańdziuk (www.mini.pw.edu.pl/~mandziuk) is a Full Professor at the Faculty of Mathematics and Information Science, Warsaw University of Technology (Poland), and Head of the Division of Artificial Intelligence and Computational Methods. He is a Member of the Polish Academy of Sciences, a Fulbright Senior Research Scholar, an IEEE Senior Member, and a recipient of the Robert Schuman Foundation Fellowship. In 2015–2016, he was a Visiting Professor at Nanyang Technological University (Singapore). He served as General Chair of the 5th Polish Conference on Artificial Intelligence (2024), General Co-Chair of the IEEE Congress on Evolutionary Computation (2021), and Chair of the annual IEEE SSCI Symposium on Computational Intelligence for Human-like Intelligence (2013–2023). He publishes in leading AI / ML journals and top-tier conferences, including ICML, ICLR, AAAI, IJCAI, and AAMAS. His research interests include the application of AI to security and social good, bilevel optimization, abstract visual reasoning, games, human–machine cooperation, and human-like learning and problem-solving.
Speech Title: Assessing AI Capabilities Through Abstract Visual Reasoning
Abstract: Visual Reasoning (AVR) encompasses a class of tasks that require discovering shared underlying concepts across sets of images through analogy-making, similar to the processes humans employ when solving IQ tests. In this talk, I will provide an overview of the main types of AVR problems and discuss potential solution approaches. In the second part of the talk, I will focus on Bongard Problems (BPs), which represent a fundamental challenge in AVR, primarily due to the need to integrate visual reasoning with verbal description. Specifically, I will investigate whether multimodal large language models (MLLMs)—which are explicitly designed to combine vision and language—are capable of solving BPs. To address this question, I will present and analyze results from applying several MLLMs to BPs composed of both synthetic and real-world images. The findings reveal certain limitations in the AVR capabilities of contemporary models.
Prof. Yaochu Jin, Westlake University, China
Bio: Yaochu Jin is Chair Professor of AI, Director of the Trustworthy and General AI Lab, Head of Artificial Intelligence Department, School of Engineering, Westlake University, Hangzhou, China. Prior to that, he was Alexander von Humboldt Professor for Artificial Intelligence endowed by the German Federal Ministry of Education and Research, with the Faculty of Technology, Bielefeld University, Germany from 2021 to 2023, and Surrey Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K. from 2010 to 2021. He was also “Finland Distinguished Professor” with University of Jyväskylä, Finland, and “Changjiang Distinguished Visiting Professor” with the Northeastern University, China from 2015 to 2017. His main research interests include intelligent optimization of complex systems, trustworthy AI, brain-like computing, and brain-like embodied AI. Prof Jin was the President of the IEEE Computational Intelligence Society and the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems. He is the recipient of the 2025 IEEE Frank Rosenblatt Award. He is a Member of Academia Europaea and Fellow of IEEE.
Speech Title: From brain-like computing to brain-like embodied intelligence
Abstract: This talk begins with an introduction to information process in the human brain and the evolution of neural self-organization in nature. This is followed by a presentation of recent advances in constructing large spiking neural networks that take advantage of multiple pathways as observed in the brain. In the second part of the talk, we point out the limitations of the data-driven approach to embodied intelligence and demonstrate the benefit of incorporating brain-inspired mechanisms in embodied AI systems with preliminary results on robot navigation. The talk is concluded by a discussion of future opportunities in brain-inspired embodied systems that can autonomously learn and adapt for accomplishing complex tasks.
Prof. TAN Kay Chen, The Hong Kong Polytechnic University, China
Bio: Professor Kay Chen Tan is the founding Head and Chair Professor of Computational Intelligence at the Department of Data Science and Artificial Intelligence at The Hong Kong Polytechnic University. He has co-authored eight books and published over 300 peer-reviewed journal articles in the fields of computational intelligence and evolutionary computation. His works have collectively been cited more than 39,000 times, and he has an h-index of 99. His contributions to the scientific community have garnered him various accolades, including being named an IEEE Fellow and a Hong Kong RGC Senior Research Fellow. Additionally, he has consistently ranked among the World’s Top 2% Most-Cited Scientists by Stanford University and has been recognized as a Highly Cited Researcher in 2024 and 2025 by Clarivate. Professor Tan has held numerous key editorial and leadership positions, including Chair of the IEEE CIS Fellow Evaluating Committee (2027) and Vice President for Publications (2021–2024) of the IEEE Computational Intelligence Society (CIS). He served as Editor-in-Chief of the IEEE Transactions on Evolutionary Computation (2015–2020) and the IEEE Computational Intelligence Magazine (2010–2013), and currently acts as Chief Co-Editor of the Springer Book Series on Machine Learning: Foundations, Methodologies, and Applications. Professor Tan has been recognized with various awards, such as the IEEE CIS Evolutionary Computation Pioneer Award (2026), the IEEE CEC Best Paper Award (2025), the IEEE CAI Best Paper Award (2024), the IEEE Computational Intelligence Magazine Outstanding Paper Award (2024, 2019), the IEEE Andrew P. Sage Best Transactions Paper Award (2020), the IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award (2016), and the IEEE CIS Outstanding Early Career Award (2012). Beyond research excellence, Professor Tan has delivered over 80 plenary and keynote lectures and co-organized more than 60 international conferences, including his roles as General Co-Chair for the 2019 IEEE Congress on Evolutionary Computation and the 2016 IEEE World Congress on Computational Intelligence.
Speech Title: Toward Self-Evolving Intelligence: Architecting Autonomous AI Ecosystems
Abstract: The future of artificial intelligence lies not in increasingly complex static models, but in systems that can continuously evolve, adapt, and reorganize themselves with minimal human intervention. This presentation advocates a paradigm shift from model-centric design to self-evolving intelligence, where AI systems operate as autonomous ecosystems rather than fixed artifacts. The presentation is organized into three parts. First, the conceptual and mathematical foundations of self-evolving intelligence are established, framing autonomous AI systems as co-evolving populations governed by multi-level evolutionary dynamics. Second, recent advances that enable this paradigm are reviewed, including scalable evolutionary computation, evolutionary model reuse, and generalizable algorithm automation. Finally, key challenges in realizing fully autonomous AI ecosystems are discussed, such as maintaining long-term evolutionary stability, ensuring verifiable safety, and developing adaptive protocols under system and data constraints.
Prof. Dongrui Wu, Huazhong University of Science and Technology, China
Bio: Dongrui Wu (IEEE Fellow) received a B.E in Automatic Control from the University of Science and Technology of China, Hefei, China, in 2003, an M.Eng in Electrical and Computer Engineering from the National University of Singapore in 2006, and a PhD in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 2009. He is now Chair Professor and Vice Dean of School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. His research interests include brain-computer interface and machine learning. He has more than 200 publications (17000+ Google Scholar citations; h=69), with 7 outstanding paper awards. His team won National Champion of the China Brain-Computer Interface Competition in seven successive years (2019-2025). He is the Editor-in-Chief of IEEE Transactions on Fuzzy Systems.
Speech Title: Machine Learning in Brain-Computer Interfaces
Abstract: A brain-computer interface (BCI) enables direct communication between the brain and external devices. Electroencephalograms (EEGs) used in BCIs are weak, easily contaminated by interference and noise, non-stationary for the same subject, and varying across different subjects and sessions. Thus, sophisticated machine learning approaches are needed for accurate and reliable EEG decoding. Additionally, adversarial security and privacy protection are also very important to the broad applications of BCIs. This talk will introduce machine learning algorithms for accurate, secure and privacy-preserving BCIs.