Research projects in Information Technology

This project aims to identify novel methods for inferring where and when photographs and videos were recorded from features of the material itself. A key requirement of image processing in a Law Enforcement (LE) context is to augment classification of material by identifying its spatio-temporal context.

Adversarial Machine Learning (AML) is a technique to fool a machine learning model through malicious input. Due to its significance in many scenarios, including security, privacy, and health application, AML has attracted a large amount of attention in recent years. However, the underlying theoretical foundation for AML still remains unclear and how to design effective and efficient attack and defence algorithms are remain a challenge in the research community. Furthermore, most existing  AML algorithms can only apply to Euclidean space.

The brain is a complex system and monitoring and imaging methods to observe critical neurophysiological variables underlying brain function are limited. This project works at the intersection of statistical signal processing, inference, machine learning and dynamical systems theory to develop new semi-analtyical filtering approaches for state and parameter estimation to infer neurophysiological variables such as network connection strengths between neural population networks underlying brain activity.

I am seeking PhD candidates interested in working on designing Learning Analytics innovations to study classroom proxemics by analysing and visualising indoor positioning data (along with other sources of evidence such as audio, physiological activity and characteristics of the students).

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.

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.

The most challenging problems of our time are social dilemmas. Thes are situations where individuals are incentivised to free ride on others, but successful group outcomes depend on everyone’s contributions. Examples include, climate change action or compliance with non-pharmaceutical interventions in a large-scale pandemic. In both cases, individuals can rely on others doing their share, but when everyone adopts such a free-riding strategy the public good collapses [1].