[Cite as http://purl.org/au-research/grants/arc/DP160100703]
Researchers Dr Qinfeng Shi; Asst Prof Julian McAuley; A/Prof Pawan Mudigonda
Brief description The project intends to develop methods to suggest how to optimally intervene so that the future state of the system will best suit our interests. The power of probabilistic graphical models to model complex relationships and interactions among a large number of variables facilitates many applications. However, such models only aim to understand the underlying environment. What is ultimately needed in many real-world applications is to suggest how we ought to intervene or act, so as to alter the environment to best suit our interests. The proposed project aims to achieve this using probabilistic graphical models on massive real-world data sets, thus facilitating a variety of applications from health care to commerce and the environment.
Funding Amount $318,000
Funding Scheme Discovery Projects