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. Yaochu Jin, Westlake University, China
Bio: Prof Jin is presently the President of the IEEE Computational Intelligence Society and the Editor-in-Chief of Complex & Intelligent Systems. He was the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems, an IEEE Distinguished Lecturer in 2013-2015 and 2017-2019, the Vice President for Technical Activities of the IEEE Computational Intelligence Society (2015-2016). He is the recipient of the 2018, 2021 and 2023 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award. He was named by Clarivate as a “Highly Cited Researcher” consecutively from 2019. He is a Member of Academia Europaea and Fellow of IEEE. He received the 2025 IEEE Frank Rosenblatt Award. Prof Jin’s research interests include evolutionary optimization of complex systems, trustworthy AI and brain-like embodied AI. He has been working in the cross-disciplinary areas of computational intelligence, computational neuroscience and robotics, including evolutionary optimization and learning, secure and privacy-preserving machine learning and optimization, graph neural network and diffusion model based combinatorial optimization, large-scale spiking neural networks and neural plasticity, computational modeling of neural and morphological development, morphogenetic self-organizing swarm robots, reconfigurable modular robots, humanoid robots, and evolutionary developmental systems. He has published over 600 papers in IEEE Transactions and major conferences such as CVPR, ICCV, NeurIPS, ICLR, ICML, AAAI and ACM MM. His research output has been applied to design optimization of many industrial systems, such as turbine engines, high-lift airfoils, vehicles, and aircraft fuselage, electric power networks, reverse engineering of biological gene regulatory networks, vaccine selection, protein-nanomaterial interation, healthcare, fintech and robotics. His research has been funded by EU FP7, UK EPSRC, UK Royal Society, German BMBF, NSF China, and several companies including Honda, Bosch, Airbus, Huawei and Nvidia.
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 of the Department of Data Science and Artificial Intelligence at The Hong Kong Polytechnic University. He is internationally recognised for his pioneering work in Computational Intelligence and Evolutionary Computation. Professor Tan’s contributions to adaptive evolutionary search, noise handling, and decision-making under uncertainty have become foundational references in the field. His research on evolutionary operational research has bridged optimisation theory with real-world decision-making systems, while his studies in neuromorphic computing have helped lay the groundwork for low-power, intelligent autonomous systems, contributing to the long-term pursuit of Artificial General Intelligence. Professor Tan’s scholarly influence is reflected in his prolific academic output and the global recognition it has garnered. He has co-authored eight books and published over 300 peer-reviewed journal articles, many of which appear in the prestigious IEEE Transactions series. His works have been collectively cited over 36,000 times, with an h-index of 94. His contributions to the scientific community have garnered him numerous accolades, including being named a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Senior Research Fellow of the Hong Kong Research Grants Council (RGC). Additionally, he has consistently ranked among the World’s Top 2% Most-Cited Scientists by Stanford University and has been recognised as a Highly Cited Researcher by Clarivate in 2024 and 2025. His research has also attracted substantial funding, having received over HK$20 million in major research grants over the past five years. These include the Collaborative Research Fund from the RGC and the Joint Fund Scheme from the National Natural Science Foundation of China. As a distinguished international leader in Computational Intelligence, Professor Tan has held numerous key editorial and leadership roles, including Chair of the IEEE Computational Intelligence Society (CIS) Fellow Evaluating Committee (2027) and CIS Vice-President for Publications (2021–2024). 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. His contributions have been recognised with many prestigious accolades, including the IEEE CIS Evolutionary Computation Pioneer Award (2026), IEEE Congress on Evolutionary Computation Best Paper Award (2025), IEEE Conference on Artificial Intelligence Best Paper Award (2024), IEEE Computational Intelligence Magazine Outstanding Paper Award (2024, 2019), IEEE Andrew P. Sage Best Transactions Paper Award (2020), 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-organised more than 60 international conferences, including in the roles as General Co-Chair for the 2019 IEEE Congress on Evolutionary Computation and the 2016 IEEE World Congress on Computational Intelligence, underscoring his global impact and leadership.
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.