Machine Learning

Machine Learning

Machine learning is a branch of computer science that develops algorithms to help us make sense of data. Increasingly, these algorithms are finding applications in systems that need to make predictions based on uncertain or incomplete information.

Our research is both theoretical and applied. We aim to understand the theoretical foundations of how machines learn, their guarantees and limitations, and the relationship between different approaches to learning. Our work is applied to data-rich areas such as social networking, understanding and interpreting images and natural language, biomedical applications, and economic modelling.

We conduct independent research as well as collaborative investigations with other academic and industry groups from Australia and overseas. Highlights of our work include: understanding images and analysing large amounts of social media content to reveal laws of community behaviour; developing theory of learning objectives and their relationship to optimisation algorithms; and discovering new methods to understand complex patterns in static and time series data.

We are advancing machine learning technologies to help people and organisations make better decisions. 

Explore our available student research projects below and if you’d like to discuss opportunities for collaboration or funding, please email us.

Academic staff

Ami Dory

Dr Ami Drory »

Research Officer

Dr Stephen Gould »

Associate Professor

Dr Timothy Graham »

Research Fellow

Dr Dongwoo Kim

Dr Dongwoo Kim »

Research Fellow

Dr Mark Reid »

Research Fellow

Dr Marian-Andrei Rizoiu »

Research Fellow

Professor Hanna Suominen

Dr Hanna Suominen »

Senior Lecturer, Team Leader of Natural Language Processing in Data61, CSIRO

Dr Kerry Taylor »

Associate Professor (Data Science), Convenor, Postgraduate Programs in Applied Data Analytics

Professor Bob Williamson »

Professor

Dr Lexing Xie »

Associate Professor

Dr Shaodi You »

Honorary Lecturer, Research Scientist

Student

Peter Anderson

Mr Peter Anderson »

PhD student

Anila Butt

Miss Anila Sahar Butt »

PhD Student

Hing Lun (Joseph) Chan

Mr Hing Lun (Joseph) Chan »

PhD Student

Mr James Charters »

PhD Student

Dawei Chen

Mr Dawei Chen »

PhD student

Mr Zac Cranko »

PhD Student

Jian Guo

Mr Edison Guo »

PhD student

Parameswaran Kamalaruban

Mr Parameswaran Kamalaruban »

PhD student

Shamin Kinathil

Mr Shamin Kinathil »

PhD student

Finnian Lattimore

Ms Finnian Lattimore »

PhD student

Roslyn Lau

Ms Roslyn Lau »

PhD Student

Alexander Mathews

Mr Alex Mathews »

PhD Student

Daniel McNamara

Mr Daniel McNamara »

PhD candidate

Swapnil Mishra

Mr Swapnil Mishra »

PhD student

Mr Jakub Nabaglo »

Honours Student

Giorgio Patrini

Mr Giorgio Patrini »

PhD student (external)

David Ratcliffe

David Ratcliffe »

PhD Student

Rodrigo Fonesca Santa Cruz

Mr Rodrigo Santa Cruz »

PhD Student

Sam Toyer

Mr Sam Toyer »

CodeBench Software Developer

Affiliates

Assoc Professor Tiberio Caetano »

Honorary Visiting Fellow

Dr Gabriela Ferraro »

Honorary Lecturer

Dr Aditya Menon »

Honorary Lecturer

Professor Richard Nock

Professor Richard Nock »

Honorary Staff Member Level E

Dr Cheng Soon Ong »

Honorary Visiting Fellow

Dr Lizhen Qu »

Honorary Lecturer, Research Scientist at Data61

Dr Felipe Trevizan »

Honorary Lecturer

Dr Christfried Webers »

Honorary Staff Member

Dr Xiangmin Zhou

Dr Emily Zhou »

Adjunct Research Fellow

Visitors

Mrs Noha Khattab »

Occupational Trainee

Mr Libo Zhang »

Occupational Trainee

Investigator

Mr Yiran Zhong »

Research Assistant

2010

Book Chapters

  1. Buntine, W., (2010). Bayesian Methods. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.
  2. McAuley, J., Caetano, T., Buntine, W., (2010). Graphical Models. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.
  3. Quadrianto, N., Buntine, W., (2010). Linear Discriminant. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.
  4. Quadrianto, N., Buntine, W., (2010). Linear Regression. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.
  5. Quadrianto, N., Buntine, W., (2010). Regression. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.
  6. Reid, M., (2010). Generalization Bounds. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.

Journal Articles

  1. Bonilla, E., Guo, S., Sanner, S., (2010). Gaussian Process Preference Elicitation. Advances in Neural Information Processing Systems, 23:262–270.
  2. Du, L., Buntine, W., Jin, H., (2010). A segmented topic model based on the two-parameter Poisson-Dirichlet process. Machine Learning, 81:5–19.
  3. lein, G., Andronick, J., Elphinstone, K., Heiser, G., Cock, D., Derrin, P., Elkaduwe, D., Engelhardt, K., Kolanski, R., Norrish, M., Sewell, T., Tuch, H., Winwood, S., (2010). SeL4: Formal verification of an operating-system kernel. Communications of the Association for Computing Machinery53(6):107–115.
  4. Reid, M., Williamson, R., (2010). Composite binary losses. Journal of Machine Learning Research, 11:2387–2422.
  5. Reid, M., Williamson, R., (2010). Convexity of proper composite binary losses. , pp. 637–644.

Conference Papers

  1. Du, L., Buntine, W., Jin, H., (2010). A segmented topic model based on the two-parameter Poisson-Dirichlet process. Machine Learning81:5–19.
  2. Barthwal, A., Norrish, M., (2010). A Formalisation of the Normal Forms of Context-Free Grammars in HOL4. In Anuj Dawar (ed.), Workshop on Logic, Language, Information and Computation 2010, pp. 15, Brasília Brazil.
  3. Barthwal, A., Norrish, M., (2010). Mechanisation of PDA and Grammar Equivalence for Context-Free Languages. In Anuj Dawar (ed.), Workshop on Logic, Language, Information and Computation 2010, pp. 10, Brasília Brazil.
  4. Bonilla, E., Dubach, C., Jones, T., O'Boyle, M., (2010). A predictive model for dynamic microarchitectural adaptivity control. In Annual IEEE/ACM International Conference on Microarchitecture 2010, pp. 12, Atlanta USA
  5. Bouguettaya, A., Chen, S., Li, L., Liu, D., Liu, Q., Nepal, S., Sherchan, W., Wu, J., Zhou, X., (2010).Managing Web Services: An Application in Bioinformatics. In International Conference on Service Oriented Computing (ICSOC 2010), pp. 2, San Francisco USA.
  6. Buntine, W., Du, L., Nurmi, P., (2010). Bayesian Networks on Dirichlet Distributed Vectors. In Petri Myllymäki, (eds.), European Workshop on Probabilistic Graphical Models, pp. 8, Helsinki Finland
  7. Caetano, T., McAuley, J., (2010). exploiting Data-Independence for fast Belief-Propargation. In Johannes Fürnkranz (ed.), International Conference on Machine Learning (ICML 2010), Haifa Israel.
  8. Du, L., Buntine, W., Jin, H., (2010). Sequential Latent Dirichlet Allocation: Discover Underlying Topic Structures within a Document. In IEEE International Conference on Data Mining 2010, pp. 10, Sydney Australia.
  9. Guo, S., Sanner, S., (2010). Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries. In Liqing Zhang (ed.), International Symposium on Neural Networks (ISNN 2010), pp. 8, Shanghai China.

  10. Guo, S., Sanner, S., (2010). Probabilistic Latent Maximal Marginal Relevance. In Annual ACM SIGIR Conference 2010, pp. 2, Geneva Switzerland.
  11. Guo, S., Sanner, S., (2010). Real-time multiattribute Bayesian preference elicitation with pairwise comparison queries. In Yee Whye Teh, Mike Titterington (eds.), International Conference on Artificial Intelligence and Statistics (AISTATS 2010), pp. 289–296, Sardinia Italy.

  12. Kumar, R., Norrish, M., (2010). (Nominal) Unification by Recursive Descent with Triangular Substitutions. In International Conference on Interactive Theorem Proving (ITP 2010), pp. 16, Edinburgh Scotland
  13. McAuley, J., Caetano, T., (2010). Exploiting within-Clique factorizations in junction-tree algorithms. In Yee Whye Teh, Mike Titterington (eds.), International Conference on Artificial Intelligence and Statistics (AISTATS 2010), pp. 525–532, Sardinia Italy.
  14. McAuley, J., de Campos, T., Caetano, T., (2010). Unified Graph Matching in Euclidean Spaces. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), San Francisco USA.
  15. Petterson, J., Smola, A., Caetano, T., Buntine, W., Narayanamurthy, S., (2010). Word Features for Latent Dirichlet Allocation. In International Conference on Neural Information Processing (ICONIP 2010), pp. 10, Sydney Australia.
  16. Quadrianto, N., Smola, A., Caetano, T., Vishwanatha, S.V.N., Petterson, J., (2010). Multitask Learning without Label Correspondences. In International Conference on Neural Information Processing (ICONIP 2010), pp. 9, Sydney Australia.
  17. Tran, K.-N., Jin, H., (2010). Detecting Network Anomalies in Mixed-Attribute Data Sets. In Third International Conference on Knowledge Discovery and Data Mining, pp. 383–386, Phuket, Thailand
01
Dec
2017
Despite the rise of “autonomous” cars and drones, a fully autonomous robot that can operate reliably outside a carefully structured factory floor is extremely rare. The main...
Bob Williamson
9 May 2016
Professor Bob Williamson from the Australian National University Research School of Computer Science has recently been named fellow of the Australian Mathematical Society....
Lexing Xie
26 Apr 2016
Associate Professor Lexing Xie has been named on the 2016-2017 Roster of Distinguished Lecturers of the IEEE Circuits and Systems The two year position recognised...
Daniel McNamara
3 Feb 2016
PhD student Daniel McNamara and law graduate Hannah Ryan have been awarded 2016 Fulbright Postgraduate Scholarships to study in the United States. Mr McNamara, a PhD...

Updated:  8 September 2015/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing