Applied Informatics Research group
 

Future Projects


 
  This page contains a list of projects that members of the AIR group would be interested in running either as projects for Research Higher Degree students, or where we are looking for collaborative partners - academic or industry. If you see a project that you would be interested in supporting, please contact us.

 
 
A game theoretic model of renewable energy generation
This project looks at a possible future for Australia when many small renewable energy generators are installed on the electricity grid and partake in a self-interested market. In this scenario each generator tries to optimize its cost recovery in the face of real time changes in electricity price and a regulatory environment designed to encourage renewable energy generators to support the grid during times of high demand or faults. The project will draw on multi agent theory, game theory and optimisation techniques.

 
 
Asynchronous boolean networks as models of gene networks - what can they tell us?
This project looks at boolean models of gene networks, where each gene is modeled as either being on or off. These models have already provided new insights into the stability of critical functions such as cell division. Once the assumption of synchrony is relaxed the models have the potential to provide more information about stability. This project will build on network theory and make use of multi agents models.

 
 
New measures of heart rate variability for disease detection
A person's heart rate can be measured using simple and cheap sensors, but this only provides the time between each heart beat, not a full electrocardiograph. However, it is becoming increasing apparent that measures calculated from the heart rate can be very useful for the diagnosis of a number of diseases, so could be used as simple health screening system. This project will investigate new measures derived from heart rate recordings and will use some ideas from artificial intelligence.

 
 
Automated feature selection for data analytics in large databases
In large databases, there may be many fields that could be used in analysis such as cluster and outlier detection and association analysis. Including all the fields assumed to be relevant has been shown to be sub-optimal and can actually result in poor performance. However, it is difficult to know which fields to select. This project uses search and optimisation methods to determine the optimum set for a database, given the query type.