We’re committed to exploring new horizons in artificial intelligence and endowing machines with the capabilities of perceiving, learning, reasoning and behaviour.
Our Sydney Artificial Intelligence Centre is committed to advancing artificial intelligence (AI) to endow 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, 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 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 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 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.
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: Dr Tongliang Liu
This project aims to equip machines with the ability to robustly harness feature-dependent label noise from big data. It expects to produce the potential to explore and exploit the weakly supervised information to better understand, interpret, and infer big data.
Expected outcomes will include theoretical foundations for learning with label noise in the real-world scenarios and the next generation of intelligent systems to accommodate noisily annotated big data.
This project should benefit science, society, and the economy nationally and internationally through the applications in the areas of artificial intelligence, cybersecurity, and big data analytics.
Funding agency: Australian Research Council (ARC)/Discovery Early Career Researcher Award (DECRA) 2019-2022
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 Chang Xu
Multi-view synergistic learning for human behaviour analysis. This project aims to equip machines with a human-likeability to synergistically harness multiple information sources for the purpose of optimal decision-making.
This project will produce the next great step for machine intelligence – laying the theoretical foundation for the learning of multiple views and building the next generation of intelligent systems which can accommodate multiple information sources.
This research is fundamental to the creation of intelligent systems that elegantly tackle varieties of big data and will benefit science, society, and the economy nationally through applications including autonomous vehicle development, sensor technologies, and human behaviour analysis.
Funding agency: Australian Research Council (ARC)/Discovery Early Career Researcher Award (DECRA) 2018-2020
Our partner: Professor Gabor Lugosi (Universitat Pompeu Fabra)
This machine intelligence project aims to explore the potential to use and incorporate past knowledge and training to better understand, interpret and develop new concepts.
The expected outcomes will provide major technological breakthroughs to benefit science, society, and the economy nationally by laying theoretical foundations for learning labels in a streaming fashion, and building the next generation of intelligent systems to accommodate environment change in applications about cybercrime, terrorism, and emergence.
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.