Brain-computer interface applications
Research Lead
Distinguished Professor CT Lin
AAII Research Lab
Computational Intelligence and Brain-Computer Interface
Collaborators
US Army Research Lab
US Office of Naval Research
Lockheed Martin
Australian Defence
Commonwealth Bank
Australian Research Council
The future is interconnected and hands-free. Our researchers are leading the world in advancing brain-computer interface (BCI) technologies; allowing people to seamlessly communicate and control external devices using their brain signals. The next generation of BCI will transform the daily life, health and well-being of humanity – and the real-world applications are exhilarating.
We are pushing the boundaries of machine learning algorithm development and paving the way to redefining approaches to everything: from how we manage stroke rehabilitation and autism to elevating cognitive neuroscience research, signal and information processing, system realisation and evaluation, and so much more.
Graph Modelling and Analysis for e-Commerce
Simplified model of farm clicking detection. Image: Ying Zhang
Research Lead
A/Prof Ying Zhang
AAII Research Lab
L; arge-Scale Network Analytics
Collaborators
Alibaba Group
Graph analytics provides powerful insights into how to unlock the value graphs hold. Due to their powerful capabilities, techniques for analysing graphs are becoming an increasingly popular topic of study in both academics and industry. As such, a host of researchers in the fields of e-commerce, cybersecurity, social networks, environmental issues, defence, and many more, are turning to graph modelling to support real-world data analysis.
In one of our recent collaboration projects with Alibaba Group, we provided solutions for large-scale graph analysis of various e-commerce graph data. One example is the real-time farm clicking detection technique, which was launched in the 2017 Double 11 shopping festival, and significantly increase the recall by 40%. By developing efficient and scalable biclique detection algorithms on large scale dynamic bipartite graph with billions of buyers and productions and 10+ billions of transactions, we can identify the potential fake buyers in a real-time manner.