Our researchers
Academic profiles and publications
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Sydney Artificial Intelligence Centre
Cutting-edge research on fundamental AI and its applications
We’re committed to exploring new horizons in artificial intelligence and endowing machines with the capabilities of perceiving, learning, reasoning and behaviour.
Our researchers design effective and efficient models to extract, represent and understand information encoded in data and build algorithms and theories
We aim to establish, analyse and evaluate models that can: learn and make predictions on data; create prototypes or applications to investigate autonomous agent actions; and identify patterns and apply logic.
Ultimately, our vision is to lead AI research in Australia and become one of the most prestigious AI research hubs in the world.
AI is a transformative technology that promises tremendous societal and economic benefit. It has the potential to revolutionise how we live, work, learn, discover and communicate.
Research into AI can advance Australia's national priorities, including greater economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security.
Our expertise spans all fundamental aspects of AI research, such as algorithms, knowledge representation and reasoning, learning theory, systems, and software-hardware co-designs; as well as applications in diverse fields, including multimedia information retrieval, object movement analysis, and future planet-scale Extended-Reality (XR) systems.
We strive to address complex challenges and create high impact outcomes in the emerging areas of AI.
Our Research
Our expert: Dr Shuaiwen Song
Our partner: Google Brain, Microsoft, Alibaba Research, Facebook Reality Lab, University of Washington.
We are tackling the essential performance problems for both extreme large-scale and small-scale models on a diverse range of hardware platforms.
Along with our international collaborators, we aim to explore principles and key technologies of multi-scale multi-dimensional machine learning inference system optimisation through cross-stack co-design (compiler, runtime and hardware accelerators).
The scope of our MLSys research includes but not limited to ML compiler design and optimisations, software-hardware co-design, runtime optimisation techniques, and customised acceleration for novel deep learning models.
Funding agency: Google Brain, Alibaba Global Faculty Award (AIR), Facebook Fair Faculty Award, USYD SOAR fellowship.
Our experts: Professor Dong Xu, Dr Tongliang Liu
Our partners: Associate Professor Kun Zhang (CMU)
We aim to equip machines with the ability to harness complex causal structures for transfer learning. We expect to produce the next great step for artificial intelligence – the potential to explore and exploit complex causal information to better understand, reason, and trust transfer learning.
Expected outcomes include theoretical foundations for transfer learning utilising causality and the next generation of intelligent systems to accommodate data with complex causal structures. This should benefit science, society, and the economy nationally and internationally through the applications to analysing their corresponding complex data.
Our expert: Dr Clément Canonne
Our partners: Assistant Professor Jayadev Acharya (Cornell University), Associate Professor Himanshu Tyagi (Indian Institute of Science).
We aim to characterise the fundamental limits and trade-offs of statistical inference and optimisation in distributed or "information-constrained" settings, an umbrella term which encompasses bandwidth constraints, restricted or noisy measurements, and privacy-preserving algorithms.
Our goal is to develop a general and rigorous framework to design and analyse algorithms in such settings, optimally balancing data requirements, computational efficiency, and the information constraints at play.
Our expert: Professor Simon Lewis, Associate Professor Zhiyong Wang
Our partners: Professor Dr Mohammed Bennamoun (UWA), Associate Professor Markus Hagenbuchner (UOW), Professor Ah Chung Tsoi (UOW)
We aim to develop novel graph neural network based deep learning algorithms for fine-grained human action recognition. We expect to bring human action analysis to the next level and to significantly advance the analysis of subtle yet complex human actions.
Expected outcomes of this project include theoretical advances on graph representation based deep learning algorithms for spatial-temporal data, and enabling techniques for more objective human action analysis in many domains such as sports and health.
This should provide significant benefits to any application domain involving big and complex spatial-temporal data for finer analytics and better knowledge discovery.
Our expert: Dr Chang Xu
Our partners: Dr Surya Nepal (CSIRO), Dr Siqi Ma (UNSW)
We aim to enhance the security of networks and information systems by empowering them with intelligent deception techniques to achieve proactive attack detection and defence.
In recent times, the fictitious environment – honeypot designed by human experience becomes popular to attract attackers and capture their interactions. However, rules-based construction of honeypots fails in preserving the privacy, boosting the attractiveness and evolving the system.
The project expects to advance deep learning and yield novel DeepHoney technologies with associated publications and open-source software. This should benefit science, society, and the economy by building the next generation of active cyber defence systems.
Our experts: Professor Alan Fekete, Professor Bernhard Scholz, Professor Willy Zwaenepoel, Dr Shuaiwen Song
Safe, lasting storage of data, and efficient access to it, is vital for all aspects of computing, ranging from e-commerce applications, and data-management in governments.
For the storage of data, persistent key-value stores are central in modern computing platforms. However, contemporary key-value stores have not been designed for emerging extreme heterogeneous computational systems with future hardware accelerators and storage capabilities, including graphics processor and flash-based memory.
We’re devising an adaptive key-value store framework for heterogeneous systems. Our new framework will adaptively harvest the performance potential of future hardware such that applications can cope with fast-growing data sets.
Our expert: Professor Joachim Gudmundsson
This project aims to devise practical fundamental algorithms and multi-purpose data structures with performance guarantees for big spatio-temporal data sets.
Systematic analysis of trajectory data has been occurring since the 1950s, but with the recent technological advances the size of the data sets has recently soared. Existing computational tools were developed for small to mid-size data sets. It aims to devise practical fundamental algorithms that will enable the development of domain specific tools for a wide range of applications, including sports, behavioural ecology, transport, and surveillance.
Our expert: Dr Sasha Rubin
Following the stunning performance of Artificial Intelligence (AI) in certain tasks, AI systems are increasingly being used to make decisions in the real world. They are driving cars, suggesting bail decisions in criminal cases, and vetting people for social services. Like humans, they may be asked, by law or social need, to provide explanations for their decisions. The research program that is working on this problem is called Explainable AI (XAI). Dr Rubin advocates for Formal Explainability based on logic since this approach is able to provide guarantees: e.g., that explanations are correct.
Outcomes of this work will include understanding how to define and incorporate background knowledge into explanations, how to define and handle user-preferences amongst multiple competing explanations, how to define and compute explanations of AI systems that produce a probability or confidence in their decision, and how to scale the formal approach to large neural networks.
Recent publications
E. Yang, Z. Wang, L. Shen, N. Yin, T. Liu, G. Guo, X. Wang, and D. Tao. (2024). Continual Learning From a Stream of APIs. IEEE Transactions on Pattern Analysis and Machine Intelligence. [https://ieeexplore.ieee.org/document/10684743]
S. Li, X. Xia, J. Deng, S. Ge, and T. Liu. (2024). Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds. IEEE Transactions on Pattern Analysis and Machine Intelligence. [https://ieeexplore.ieee.org/document/10497879]
Wu, S., Zhou, T., Du, Y., Yu, J., Han, B., Liu, T. (2024). A Time-Consistency Curriculum for Learning from Instance-Dependent Noisy Labels. IEEE Transactions on Pattern Analysis and Machine Intelligence. [More Information]
Zhang, J., Song, B., Wang, H., Han, B., Liu, T., Liu, L., Sugiyama, M. (2024). BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(6), 4398-4409. [More Information]
Z. Huang, M. Li, L. Shen, J. Yu, C. Gong, B. Han, and T. Liu. (2024). Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization. [https://link.springer.com/article/10.1007/s11263-024-02075-x]
Research career opportunities
We offer researchers a world-class education, a great opportunity to work on cutting-edge projects, and a catapult for your career. You will work on projects to solve real-world problems that will help improve people’s lives while being mentored through the PhD program that involves both academic and industry training as well as collaboration with other researchers working on similar problems.
If you are interested in pursuing your research career in the field of AI on the following topics, but not limited to, machine learning, learning theory, deep learning, image processing, computer vision, multimedia content analysis and generation, information retrieval, and data mining, we would encourage you to apply for a postgraduate research position at either Doctor of Philosophy (Engineering) or Master of Philosophy (Engineering) levels.
To be considered for these positions, please attach your CV, academic transcript, outstanding record of published work and relevant research experience (if any) to our researchers listed on the page.