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

Hussein Abbass is a full professor with the School of Systems and Computing, University of New South Wales, Canberra. He is a Fellow of the Institute of Electrical and Electronics Engineering (IEEE) USA, a Fellow of the Australian Computer Society, a Fellow of the UK Operational Research Society, a Fellow of the Australian Institute of Managers and Leaders, and a Graduate Member of the Australian Institute of Company Directors. Hussein was the National President (2016-2019) for the Australian Society for Operations Research, the Vice-President for Technical Activities (2016-2019) for the IEEE Computational Intelligence Society, and an ExCom and AdCom member (2016-2019) of the IEEE Computational Intelligence Society. Hussein is a Distinguished Lecturer for the IEEE Computational Intelligence Society and the Founding Editor-in-Chief of the IEEE Transactions on Artificial Intelligence. Hussein is the chair of the IEEE Conference on AI Steering Committee, the incoming chair of the IEEE Frank Rosenblatt Award committee (equivalent to the technical medal in computational intelligence) and is the vice-chair for the Working Group on the IEEE P7018 Standard for Security and Trustworthiness Requirements in Generative Pretrained Artificial Intelligence (AI) Models. Hussein is a UAV pilot and a mental health first-aid officer and has completed various executive professional development training. Following ten years in industry and academia, in 2000, he joined the University of New South Wales campus in Canberra (UNSW-Canberra) at the Australian Defence Force Academy. He has been a full professor since 2007 and has served in various university leadership roles. His current research focuses on trusted quantum-enabled human-AI-swarm teaming systems and distributed and trusted machine learning and machine education systems and algorithms.
Speech Title: Smart Flying Sheepdogs: How Can Nature Inspire Distributed Artificial Intelligence?

Abstract: Many researchers in Artificial Intelligence (AI) aspire to reproduce human intelligence; my group does not. Our highly interdisciplinary research program aims to support humans, augment human abilities, extend humans with powerful AI-enabled tools, and enhance human performance. Possibly, albeit arguably, AI will develop a relationship with humans like those we have with animals. Sheepdogs are perhaps the best example of what my program aims to achieve. A sheepdog works with the human farmer as a friend and as an intelligent agent with complementary abilities: sensors such as hearing range exceeding a human’s capacity, actuators such as body motors capable of running faster than humans with sufficient speed to control sheep, an intelligent mind with an ability to autonomously make decisions like deciding on the appropriate path to approach sheep, and more.
Our research program's central question is: Can we develop distributed AI capable of operating next to humans with smartness and abilities similar to sheepdogs? Other questions that follow include: What if these AIs sit within uncrewed systems such as uncrewed ground vehicles, aerial vehicles, or even just on our computers in cyberspace? How can we design these AIs to be contextually aware, effective, efficient, safe, secure, ethical, and responsible? How can we design the interaction space, analytics, interfaces, and interaction modalities to effectively and efficiently operate the eco-system formed by humans, AI, and possibly biological sheepdogs and sheep?
The above questions have developed into an exciting and highly interdisciplinary research program. A variety of collaborators with diverse skills are needed to solve these questions. For example, we have interacted with farmers, sheepdog handlers, behavioural biologists, psychologists, aerospace engineers, UAV operators, AI and computer science experts, and others.
As a public lecture, this talk is designed for a general intelligent audience. The presentation will immerse the audience into the diverse worlds of sheepdogs, AI, autonomous systems, mathematics, and beyond. It will introduce some of the challenges and some of our solutions. I will also attempt to make time for AI discussions in the “Ask me anything on AI” session.


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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