AUT Data Science Research Group
The Data Science Research Group (DSRG) seeks to promote research into all aspects of Data Science. Data Science is poised to become one of the most intensively researched areas within the Computer Science discipline and the research carried out by the group will be at the cutting edge of new developments in this field.
The group has a strong research profile in areas such as Big Data Processing and Data Stream Mining. In this connection we have a strong focus on developing novel techniques for integrating large streams of data in real time to support online query processing. We have also developed novel methods for change detection in data streams and capture of recurrent patterns. Others research areas of interest include Text Mining, Machine Learning, Information Visualisation, and Simulation and Modelling. The application areas where research carried out by the group is being applied are Smart Business, eHealth, Computational Sport Science, Telecommunication, Social Network, Power and Electricity and City Planning.
The Group, although based at the School of Engineering, Computer and Mathematical Sciences has a number of research collaborators, both within New Zealand and overseas. In addition, individual members of the group have strong connections with Industry in New Zealand, some of which has sponsored the group’s research activities. The group seeks to attract Doctoral students who have a strong interest in researching into any area of Data Science and provides financial support to these students whenever funds permit.
Following is the research spectrum diagram with core research areas, application domains and projects with their links.
Recent News and Events
Call for papers –IEEE-IWDS 2017
|3rd Data Science Research Group Workshop on Recent Developments in Data Science|
|Sarah Marshall chaired the Joint NZSA+ORSNZ Conference with Priya Parmar which was held at AUT between 27-30th November. She welcomed over 200 delegates, primarily from NZ universities. There were 115 contributed talks by academics, practitioners and students from New Zealand and abroad. There were two post-conference workshops - Data Visualisation with 18 participants and Health Analytics with 50 delegates including several members of AUT’s Data Science Research Group.|
|A/Professor Russel Pears co-chaired the Workshop at ACML (Asian Conference on Machine Learning) ACML 2016.|
|A/Professor Russel Pears published his paper “A Composite Spatio-Temporal Modeling Approach for Age Invariant Face Recognition Expert Systems with Applications” with Fahad Alvi in Elsevier Expert Systems with Applications, Impact Factor 2.98. http://www.sciencedirect.com/science/article/pii/S0957417416305826|