Relational and Structured Learning research projects
Nonparametric Bayesian Machine Learning for Modern Data Analytics
(ARC DP, 2016-2019)
Project lead: Prof. Dinh Phung
We are developing next generation machine learning methods to cope with the data deluge. Our intended outcomes include a new Bayesian nonparametric method that can express arbitrary dependency amongst multiple, heterogeneous data sour sources with infinite model complexity, together with algorithms to perform inference and deduce knowledge from them. An additional outcome is the new Bayesian statistical inference for set-valued random variables that moves beyond vectors and matrices to enrich our analytics toolbox to deal with sets, and a new deterministic fast inference to meet with real-world demand.
Knowledge Graph for Water Sensitive Urban Development
Project lead: Dr Teresa Wang and Dr. Yuanfang Li
CRC for Water Sensitive Cities is in possession of a wealth of information on best practices for water-sensitive urban development that are based on scientific research. However, this information is stored with each case in a variety of files, making it hard to integrate and reuse. The CRC would like to develop a knowledge graph-based system to demonstrate its capabilities and expertise in urban development.