Research Grant
[Cite as https://purl.org/au-research/grants/arc/DP240103070]Researchers: Dr Weitong Chen (Chief Investigator) , Miao Xu (Chief Investigator) , Olaf Maennel (Chief Investigator) , WEI ZHANG (Chief Investigator) , ARC Linkage Grant LP1701000985 (Funded by)
Brief description Towards knowledge discovery from imperfect and evolving data. Information extraction from data is critical, both to analyse and protect consumer data. However, many learning techniques are developed using perfect, static datasets, quite different to messy, ever-changing real-world data. This project aims to develop data analytics techniques that can extract accurate information in complex structures from imperfect/incomplete data that changes over time. Expected outcomes are a prototype tool, tested on real datasets, that combines new techniques in data modelling, algorithm development, and system design. Likely benefits are enhanced Australia's competence in data science through student training and new, robust data tools relevant to critical sectors such as cybersecurity, healthcare, and defence.
Funding Amount $292,330
Funding Scheme Discovery Projects
- PURL : https://purl.org/au-research/grants/arc/DP240103070
- ARC : DP240103070