CAI conducts pioneering research to develop innovative theories and techniques of AI, and transferring the knowledge and technologies to industry, community and society.
Centre for Artificial Intelligence (CAI) focuses on pioneering research to develop innovative theories and techniques of AI, and transferring the knowledge and technologies to industry, community and society. CAI aims to become an internationally renowned group that excels at AI-related research and applications and engages with diverse stakeholders to address their business challenges. CAI offers expertise in
and their applications to a wide range of real-world problems.
Associate Professor Markus Hagenbuchner
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2022-2024 |
Making Meta-learning generalised Lei Wang, Peng Wang and Lingquiao. Liu; ARC DP |
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2022 |
Developing and evaluating a proof-of-concept image-based dietary tracking tool using machine learning for use by people with multiple sclerosis Peng Wang and Lei Wang; Multiple Sclerosis Research Australia Incubator Grant |
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2021-2013 |
Fine-grained human action recognition with deep graph neural networks Zhiyong Wang, Mohammed Bennamoun, Markus Hagenbuchner, Ah Chung Tsoi and Simon Lewis; ARC DP |
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2021-2024 |
Building on rural knowledges to unlock the potential of rural students Karl Maton, Sarah Howard, Philip Roberts, Christian Ritz, Jie Yang and Yaegan Doran; ARC DP |
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2020-2022 |
Learning kernel-based high-order visual representation for image retrieval L. Wang; ARC DP |
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2020-2023 |
The algorithm will see you now: ethical, legal and social implications of adopting machine learning systems for diagnosis and screening Stacy Carter, Nehmat Houssami, Christopher Degeling, Bernadette Richards, Annette Braunack-Mayer, Wendy Rogers, Lei Wang, Khin Win and Maame Esi Woode; NHMRC Idea |
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2020-2022 |
Efficient Multi-key Homomorphic Encryption and Its Applications Willy Susilo,…,Jie Yang,…; ARC LP |
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2020 |
Automatic and Modularised toolbox for Knowledge Discovery Shen Jun and Jie (Jack) Yang; Defence Innovation Network (DIN): Artificial Intelligence for Decision Making |
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2020 |
VR-CFD Based Simulation and Training Tool for Dust Control in Gateroad Development Panels Ting Ren, Faisal Hai, Jon Roberts, Jennifer Hines, Willy Susilo, Casey Chow and Guoxin Su; ACARP |
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2020 |
Developing Extremely Efficient Deep Neural Networks for Long-tail Image Recognition Lei Wang and Peng Wang; UOW-BUAA Collaborative Grant |
Engage with us as we bridge research and industry, fostering innovation and shaping the AI landscape globally.
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