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I am honoured to have been recognised in The Australian's 2023 Research Magazine as Australia's top Bioinformatics and Computational Biology researcher. This is the third year in a row that The Australian has recognised my research in this way. This is a great recognition of the fantastic research of my bioinformatics collaborators, most especially Jiangning Song.

We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree–-"Extremely Fast Decision Tree", a minor modification to the MOA implementation of Hoeffding Tree–-obtains significantly superior prequential accuracy on most of the largest classification datasets from the UCI repository. Hoeffding Anytime Tree produces the asymptotic batch tree in the limit, is naturally resilient to concept drift, and can be used as a higher accuracy replacement for Hoeffding Tree in most scenarios, at a small additional computational cost.

Download the paper here.

Download the software here.


The Living with AI discussion series will feature conversations about social and ethical implications of artificial intelligence. Join us for the first of these conversations, featuring AI expert Terry Cailli addressing the question "Can Computers Behave Ethically?"

Date and Time

Wed. 8 August 2018
5:30 pm – 7:00 pm AEST


Deakin Downtown
Collins Square, Tower 2
Level 12, 727 Collins Street
Docklands, VIC 3008


Click here.

We are very honoured to have received the 2018 SIAM International Conference on Data Mining (SDM-18) Best Research Paper Award for

Efficient search of the best warping window for Dynamic Time Warping.
Tan, C. W., Herrmann, M., Forestier, G., Webb, G. I., & Petitjean, F.
Proceedings of the 2018 SIAM International Conference on Data Mining, 2018.

This paper presents algorithms that allow a critical hyper-parameter for time series classification, the warping window, to be efficiently tuned.

The paper can be downladed here:

The code can be downloaded here: