Intelligent Data Analytics Lab
In the era of big data, data analytics underpins the data-intensive research that responds to industrial challenges. Artificial intelligence (AI) has developed into the most powerful data analytics technology in recent years, leading to remarkable breakthroughs in many fields. These breakthroughs are benefiting industrial sectors as varied as information and communications technology (ICT), finance, health, transportation and manufacturing.
Swinburne’s Intelligent Data Analytics Lab focuses on the research and applications of artificial intelligence techniques for tackling versatile real-world data analysis tasks across various fields with the aim of facilitating the data-to-discovery or data-to-decision process, especially in the context of big data.
We leverage the combined power of artificial intelligence, big data and high-performance computing to deliver accurate, fast and robust data analysis solutions needed by industrial and governmental sectors as well as academia.
Swinburne’s intelligent data analytics experts are investigating innovative ways to identify, extract and integrate intelligence from data to enable organisations to act on opportunities to improve their productivity, economic growth and sustainability.
The Intelligent Data Analytics Lab’s major research capabilities include:
- Machine learning (particularly deep learning, ensemble learning and transfer learning)
- Optimisation (particularly data-driven evolutionary optimisation)
- Collaborative learning and optimisation (i.e., the synergy of machine learning and optimisation)
- Distributed machine learning and distributed optimisation based on high-performance computing facilities with hybrid GPU-CPU architectures
- Big data analytics (particularly predictive and prescriptive analytics)
- Computer vision, image processing and video analytics
- Text mining and analytics
- Time-series analytics
The Intelligent Data Analytics Lab has access to Swinburne supercomputer OzSTAR, which is one of the most powerful high-performance computing facilities in Australia. OzSTAR features:
- 4140 SkyLake cores at 2.3Ghz across 107 standard compute and 8 data crunching nodes
- 230 NVIDIA Tesla P100 12 GB GPUs
- 272 Intel Xeon Phi cores at 1.6Ghz across 4 C6320pKNL nodes
- High-speed and low-latency network fabric able to move data across each building block at over 100Gbps with various features to ensure reliability and traffic flow
- 5 petabyte of usable storage via the Lustre ZFS file system at 30GB/s throughput
It can provide sufficient computational horsepower to enable computationally intensive data analytics, especially for handling big data.