Keynote Speech

Keynote Speakers of 2024

Prof. Dongrui Wu

School of Artificial Intelligence and Automation,
Huazhong University of Science and Technology, China
Fellow of IEEE, Editor-in-Chief of IEEE Transactions on Fuzzy Systems

Prof. Wu's research interests include brain-computer interface, machine learning, computational intelligence, and affective computing. He has more than 200 publications (12000+ Google Scholar citations; h=57). He received the IEEE Computational Intelligence Society Outstanding PhD Dissertation Award in 2012, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, the IEEE Systems, Man and Cybernetics Society Early Career Award in 2017, the USERN Prize in Formal Sciences in 2020, the IEEE Transactions on Neural Systems and Rehabilitation Engineering Best Paper Award in 2021, the Chinese Association of Automation (CAA) Early Career Award in 2021, the Ministry of Education Young Scientist Award in 2022, and First Prize of the CAA Natural Science Award. His team won National Champion of the China Brain-Computer Interface Competition in two successive years (2021-2022). Prof. Wu is the Editor-in-Chief of IEEE Transactions on Fuzzy Systems.


Speech Title: Efficient Optimization of Fuzzy Systems

Abstract: Fuzzy systems have been widely used in classification and regression. However, for big data, traditional evolutionary algorithm based and full-batch gradient descent based optimization strategies become too costly. This talk first introduces functional similarity/equivalence between fuzzy systems and classical machine learning models such as radial basis function network, mixture of experts. Then, it extends their optimization techniques, such as mini-batch gradient descent, DropOut, Batch normalization and Adam, to the optimization of fuzzy systems.


Prof. Hussein A. Abbass

University of New South Wales, Australia
Fellow of IEEE, Fellow of Australian Computer Society, Founding Editor-in-Chief of the IEEE Transactions on Artificial Intelligence (IEEE-TAI), IEEE Computational Intelligence Distinguished Lecturer


Prof. Erik Cambria

Nanyang Technological University, Singapore
Fellow of IEEE, IEEE Outstanding Early Career Award, One of the 5 People Building Our AI Future in Forbes, Provost Chair in Computer Science and Engineering

Erik Cambria is the Founder of SenticNet (, a Singapore-based company offering B2B sentiment analysis services, and a Professor at Nanyang Technological University, where he also holds the appointment of Provost Chair in Computer Science and Engineering. Prior to joining NTU, he worked at Microsoft Research Asia (Beijing) and HP Labs India (Bangalore) and earned his PhD through a joint programme between the University of Stirling and MIT Media Lab. His research focuses on neurosymbolic AI for explainable sentiment analysis in domains like social media monitoring, financial forecasting, and AI for social good. He is recipient of several awards, e.g., IEEE Outstanding Early Career Award, was listed among the AI's 10 to Watch, and was featured in Forbes as one of the 5 People Building Our AI Future. He is an IEEE Fellow, Associate Editor of many top-tier AI journals, e.g., Information Fusion and IEEE Transactions on Affective Computing, and is involved in various international conferences as keynote speaker, program chair and senior program committee member.


Speech Title: Seven Pillars for the Future of AI

Abstract: In recent years, AI research has showcased tremendous potential to impact positively humanity and society. Although AI frequently outperforms humans in tasks related to classification and pattern recognition, it continues to face challenges when dealing with complex tasks such as intuitive decision-making, sense disambiguation, sarcasm detection, and narrative understanding, as these require advanced kinds of reasoning, e.g., commonsense reasoning and causal reasoning, which have not been emulated satisfactorily yet. To address these shortcomings, we propose seven pillars ( that we believe represent the key hallmark features for the future of AI, namely: Multidisciplinarity, Task Decomposition, Parallel Analogy, Symbol Grounding, Similarity Measure, Intention Awareness, and Trustworthiness.

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