Research projects in Information Technology

The Australian Research Council (ARC) Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA) is seeking applications for ten ARC fully-funded PhD projects with generous top-up scholarships.

We're looking for talented students with a background in mathematics, computer science, statistics, economics, engineering or other related fields. These positions are offered across OPTIMA's nodes located at Monash University or The University of Melbourne. Projects will be available from June 2021 onwards.

The proposed PhD project aims to build a machine learning/deep learning-based decision support system that provides recommendations on precision medicine for paediatric brain cancer patients based on clinical, genomics and functional dependency data (CRISPR, drug screens).

Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need to devise adaptive models that take into account the dynamically changing characteristics of environments and detect anomalies in ‘evolving’ data.

The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and Dowe, 1999a) ensures that - at least in principle, given enough search time - MML can infer any underlying computable model in a data-set.

A consequence of this is that we can (e.g.)

Methods of balancing model complexity with goodness of fit include Akaike's information criterion (AIC), Schwarz's Bayesian information criterion (BIC), minimum description length (MDL) and minimum message length (MML) (Wallace and Boulton, 1968; Wallace and Freeman, 1987; Wallace and Dowe, 1999a; Wallace, 2005).

There are many approaches to hypothesis testing.  The well-known approach of p-values has been drawn into question and even controversy in more recent years, even though criticisms reportedly date back at least as far as 1954 (Dowe, 2008a, sec. 1, pp549-550).

Discussion of how to do this using the Bayesian information-theoretic minimum message length (MML) approach (Wallace and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) are given in Dowe (2008a, section 0.2.5, page 539, and section 0.2.2, page 528), and Dowe (2011, pages 919 and 964).

The recent discovery in 2015 of gravitational waves from colliding black holes and neutron stars has opened a new window on the Universe. Astrophysicists can now “see the unseeable” -- black holes that emit no light are regularly being observed through their gravitational-wave signatures. Since the first discovery in 2015, more than 50 black hole mergers, two neutron star mergers, and two neutron star-black hole collisions have been observed.

Graphs or networks are effective tools to representing a variety of data in different domains. In the biological domain, chemical compounds can be represented as networks, with atoms as nodes and chemical bonds as edges. Analysis these networks are important as they may provide AI-based approaches for drug discovery. This project will focus on representing and inferring chemical or biological networks as a form of relational and structural learning.

 Automation has affected employment at least as far back as Gutenberg, the introduction of the printing press and the effect on scribes and others. Such changes have occurred in the centuries since. In more recent times, we see electronic intelligence showing increasingly rapid advances, with examples including (e.g.) easily accessible, free, rapid and often somewhat reliable language translation. More recent advances include the increasing emergence of driverless cars.

    DNA or RNA motif discovery is a popular biological method to identify over-represented DNA or RNA sequences in next generation sequencing experiments. These motifs represent the binding site of transcription factors or RNA-binding proteins. DNA or RNA binding sites are often variable. However, all motif discovery tools report redundant motifs that poorly represent the biological variability of the same motif, hence renders the identification of the binding protein difficult.