School of Computing activity clusters
The School of Computing has a strong foundation in the computing and information sciences at the ANU. We are a transformative centre for research in artificial intelligence and machine learning, computer systems and software, and theoretical foundations of computing. We span traditional computer science and modern data and computational science.
Our mission is motivated by the need to design, drive and sustain strategic activities via five broad focus areas: Computing Foundations, Computational Science, Intelligent Systems, Data Science and Analytics, and the Software Innovation Institute.
Computing platforms underpin global commerce, governance, and social wellbeing as critical infrastructure. We focus on the software and hardware foundations of computing, and its theory, to improve the safety, reliability, and performance of software systems, and to make them scalable and secure. We combine teaching and research in the foundations of computing: logic and verification, theory of computation, computer organisation and architecture, operating systems, formal methods and methodologies for software development, and programming languages and tools. We work closely with industry partners on solutions to problems for real systems. Our education programs emphasise hands-on implementation and project-based learning.
Computation increasingly drives discovery in the sciences and engineering. We design, implement and use mathematical models to analyse and solve computationally demanding problems, using advanced computational infrastructure and algorithms to perform large-scale simulations of physical systems and processes, and visualise the outcomes to inform the science. Drawing on advances in machine learning (ML) and artificial intelligence (AI) we enable new approaches to virtual discovery and design, and the effective utilisation of computational assets at scale. Our education programs train computational scientists and provide them with skills in high performance computing relevant to science and engineering. We work with partners in target applications such as environmental science, computational biology, bioinformatics, quantum physical systems, and nanotechnology, to accelerate discovery in these domains.
Machine Intelligence augments human intelligence in analysing and synthesising vast amounts of information. We focus on the computational modelling and design of intelligent agents in complex real-world contexts. Our research integrates areas of artificial intelligence (AI), machine learning (ML) and vision, natural language understanding, and robotics, to build autonomous systems that can perceive, plan, and respond to their environment in pursuit of high-level goals. Our teaching portfolio includes introductory and advanced courses in AI and ML from the foundational science to implementation of large-scale practical intelligent systems, with applications in computer vision, language understanding, and robotics, co-taught and co-developed across the College. We also work across the University to address questions on integrating human and social values in AI systems, touching on aspects of philosophy, cognition, ethics, and safety.
Data Science and Analytics
Data is central to all endeavours today, dealing with its acquisition, storage, curation, retrieval, and processing. By utilising Artificial Intelligence, Machine Learning, and Statistics data becomes the basis for our modelling of and reasoning about the world and society, to also gain understanding. We pursue a rigorous processing of data and its contexts and implications, engaging with domain experts in government, business, and the health and social sciences to build models that turn data into information into knowledge to then support effective and confident economic and social decision making. Our research focus on the design and construction of robust processes and models leads to new algorithms, prototypes, and deployed systems across multiple domains to derive new meaningful insights while being sensitive to bias. Our broad teaching portfolio includes both micro and macro credentialing, balancing theoretical techniques with domain-relevant project-based learning, aimed at researchers, practitioners, and decision makers.
Software Innovation Institute
The Software Innovation Institute (SII) is developing new ways to train the next generation of Data Scientists and Software Engineers. We create, apply and teach state-of-the-art techniques in Data Science and Software Engineering to provide world-leading integrated learning for students, while addressing some of the complex challenges of today. We work with clients on actual projects, managed and supervised but industry experienced staff, to create systems that solve their data problems and drive business decisions, utilising world leading research outcomes. We bring together leading researchers, industry experts, and students to translate research, to design, to engineer, and to build solutions to complex problems, cognisant of cultural context while preserving privacy. Working with colleagues across the University, we are the translational engine for the School and a locus for experiential learning.