Machine learning

Flagship leaders

  • Professor Wray Buntine
  • Dr Reza Haffari

This research programme investigates the mathematical and practical foundations of learning models from data. The research provides the engine for the Data Science Centre and the core expertise behind the Data Science courses at Monash University. The research area has existed within Monash since the 1960's with Prof. Chris Wallace.

Prospective research students should look at our Machine Learning Flagship Projects at the Faculty of Information Technology's Project webpage. Further details about applying for research degrees can be obtained there. Of course, we always encourage initiative so contact individual faculty or researchers below if you have a particular project of your own in mind.

Research Themes

The Research Themes in the programme include:

  • Learning under Non-stationary Distributions
  • Bayesian Non-parametric Methods
  • Information Theoretic Approaches to Data Analysis
  • Bayesian Approaches to Learning and Knowledge Discovery

Application Areas

Complementary to the methodological research, a number of Application Areas for machine learning are explored, including:

  • Computational Biology
  • Information Retrieval (IR) and Web Mining
  • Medical Informatics
  • Monitoring and Assistance Systems
  • Natural Language Processing (NLP) and Text Mining
  • Time-Series Analysis/Classification
  • Behaviour Modelling

Showcase Software

There is also a long tradition in our group of producing quality research software. The first split-merge clustering system able to estimate the number of clusters, called SNOB, was developed by Chris Wallace and his group in 1968 and has been continually extended.

Machine Learning Showcase Software include: HCA, Chordalysis, Minimum Message Length (MML) Software, Weka components, Mass estimation software and CaMML (causal discovery).

Flagship Members


Dr David Albrecht Temporal geostatistics, Bayesian inference, Bayesian networks, Minimum Message Length Inference
Prof Wray Buntine Bayesian methods, non-parametric methods, NLP, text mining
Dr Mark Carman Text and Web Mining, Recommender Systems, Information Retrieval, Rank Learning
A/Prof David Dowe Minimum Message Length Inference
Dr Reza Haffari Structured Prediction Learning, (Non-parametric) Statistical modelling, Natural Language Processing and Text Processing, Computational Genomics
Dr Arun Konagurthu Minimum Message Length Inference, directional statistics, applications to structural bioinformatics
Dr Kevin Korb Causal modeling, causal discovery, Bayesian networks, statistical inference, evaluation methods, Bayesian inference
A/Prof Vincent Cheng-Siong Lee Adaptive signal processing, cognitive data mining, pattern recognition, constrained optimisation, service system
Prof Ann Nicholson Artificial intelligence. Probabilistic reasoning. Bayesian networks. Knowledge Engineering
A/Prof Andrew Paplinski Computational Neuroscience and Intelligence, Neural Networks, Computer Vision, Image and Signal Processing
Dr Sid Ray Pattern Recognition; Image Processing and Analysis; Cluster Analysis of Data
Dr David Squire Image similarity, learning visual texture features, object recognition, content-based image retrieval, text similarity
Dr Peter Tischer& Minimal Message Length inductive inference, image segmentation, image compression, visual analytics
Prof Geoff Webb Classification learning, graphical modeling, pattern discovery, statistical screening
Prof Ingrid Zukerman User modeling, Spoken dialogue systems, NLP


Dr Lloyd Allison Minimum Message Length Inference
Dr Christoph Bergmeir Time series forecasting, Applied Machine Learning
Dr Yong-Bin Kang Multi-label classification, Meta learning, Ontology Learning, Concept extraction from text
Dr Masud Moshtaghi applications of machine learning in network security, environmental monitoring, aged care
Dr Francois Petitjean Statistical inference, classification, time series analysis, image processing, spatial analysis
Dr Nayyar Zaidi Supervised classification tasks, designing scalable implementations, big data learning


Geoff Webb to co-chairs KDD 2015 which will run 10-13 August in Sydney. See more.

Along with colleagues, Geoff Webb is awarded a Movember Australian Mental Health Initiative grant on “Improving men’s access to care: a national ambulance approach to reduce suicide and to improve the mental health of men and boys” (2015 $971,000, 2016 $869,000, 2017 $917,000).

Geoff Webb has elected a Fellow for IEEE in December 2014, see more.

Ingrid Zukerman has been awarded a grant by the US Air Force Research Laboratory Asian Office of Aerospace Research And Development, for 2014-2016, for work on robotic user interaction and natural language processing.

Francois Petijean been awarded the best PhD thesis prize 2014 by the French-speaking world's main machine learning association, the Association Internationale Francophone d'Extraction et de Gestion des Connaissances (EGC, founded 2000). This is for his contributions on time series analysis at the French Space Agency, see more.

Geoff Webb received Australian Research Council Discovery Outstanding Researcher Award, 2014. One of only 17 of these prestigious grants awarded in 2014 across all fields of research.

Kevin Korb is quoted in an article in The Age on Mon 21 July 2014, in an article, 'Artificial intelligence: the next step in evolution?', by Peter Spinks. See more.

The Age ran a piece on Big Data, mentioning the new Master of Data Science degree. It was published 06/10/2014. See more.