About the Project
Applications have now closed.
This project aims to develop a novel system for making efficient and effective queries and recommendations based on multi-source data from partner, the Queensland Police Service [QPS], by constructing an enterprise knowledge graph model for analysing complex objects from multiple data sources. The project will develop ranking algorithms and deep models for yielding efficient and effective data queries recommendations, which also consider the evolutionary, uncertain data, and human experts in the loop. The proposed techniques will be validated by conducting interactive experiments on the QPS data lake. The applicability of graph-based methods for generating complex network, ranking methods, and deep models for making queries and recommendations will be explored to discover relationships, rules and patterns previously unknown, and potentially useful for on-duty police officers.
About the Candidate
This is an opportunity for a highly motivated student to join the CIRES project team, collaborating closely with leading experts in the Queensland Police Service (QPS) to develop a prototype system to showcase the scalability, reliability, and usability of an AI-based data discovery system. This scholarship is one of three CIRES projects with QPS related to the responsible use of sensitive data assets.
For this project, CIRES is seeking a candidate with interdisciplinary interests and capabilities. The candidate will have qualifications relevant to this project, e.g., Master of Data Science, Computer Science, IT, and/or Bachelor of Computer Science, IT, Mathematics.
The candidate will have a good background in data science, data analytics, or machine learning, preferably with expertise in predictive analytics, graph mining, and causal inference. Experience working with and mining structured and unstructured data from multiple sources is also desirable.
Proficiency in Python programming language and machine learning software packages such as Tensorflow and Pytorch is required.
Dr Wen Hua (Principal Advisor)
Prof Shazia Sadiq
A/Prof Hongzhi Yin