Research projects in Information Technology

In this project, we are interested to translate speech to text. This can be used for translating a speaker who speaks in French to English in order to make it understandable by an English speaking person.

There are various aspects to this project, including (but not limited to): (i) handling multi-modal speech and text data, and (ii) producing simultaneous translations. This project involves innovating interesting models to address different aspects of this problem (eg [1,2]).

The meaning of an utterance depends on the broader context in which it appears. The context may refer to the paragraph, document, conversational history, or the author who has generated the utterance. In this project, we develop effective methods for translating text using the context, e.g. the rest of the sentences in the document or the conversational history.

I am seeking PhD candidates interested in working on designing Learning Analytics or similar reflection interfaces that automatically highlight design elements of data visualisations and generate narrative to communicate insights (instead of just plotting data).

I am seeking PhD candidates interested in designing and connecting Multimodal Learning Analytics solutions according to the pedagogical needs and contextual constraints of teamwork occurring across physical and digital spaces.

Insects are vital components of natural and agricultural ecosystems that interact with plants in complex ways. Computer simulations can help us understand these interactions to improve crop production, and to assist us to sustain our natural ecosystems as we change the Earth's climate. This technology is vital to inform our strategies to protect global food supplies and manage our national parks and forests.

This project builds on research in which geo-tagged social network site images are used to determine insect and flowering plant distributions on a continent-wide scale. This work was awarded an "AI for Earth" grant by Microsoft, one of only 6 projects in Australia to receive this recognition.

Mixed-Integer Programming (MIP) solvers are very powerful tools to solve combinatorial problems that arise in many industries. Modern MIP solvers usually run a sequence of algorithms to solve the input instance: first it preprocesses the instance, then it solve its Linear Programming Relaxation, runs cutting plane algorithms, primal heuristics, then the branch-and-bound. How much time is devoted to each of these types of algorithms is decided online, but once the next stage of solving has started, there is no turning back.

The Multi-Agent Path Finding (MAPF) is a combinatorial problem in which agents must find a path from a start to a goal location without colliding with each other. The optimisation group at Monash is leading research in this area and has designed some of the most efficient methods to solve MAPF. Companies like Amazon have funded the optimisation group at Monash to do research on MAPF as it relies on this technology for its automated warehouses and fulfilment centres.

In recent years, AI techniques such as GANs and associated deep learning neural networks have become popular tools applied to the production and creation of works of art. In 2018, AI Art made headlines around the world when a “work of art created by an algorithm” was sold at auction by Christie’s for $432,500 – nearly 45 times the value estimated before auction.

We normally think of drawing as an (almost) exclusively human activity. The idea behind this research is to explore the concept of post-anthropocentric creativity. We want to understand what art made by an autonomous, non-human intelligence might look like, and if artificial systems can exhibit what we recognise as creative behaviour. This behaviour and the drawings produced might not be the same as what humans would do.