AAII researchers named lead CIs on three projects in the ARC DP24 announcement: Profs Ling Chen, Lu Qin and Xiaojun Chang.
The ARC's Discovery Program objective is to grow Australia’s research and innovation capacity, leading to the development of new technologies, products and ideas, the creation of jobs, economic growth and an enhanced quality of life in Australia. Image: AdobeStock.
AAII celebrates successful DP24 ARC Round
AAII researchers have been named CIs on three Discovery Projects awarded to UTS in the latest round of the ARC's Discovery Program. This latest announcement brings the total of number of Discovery Projects awarded to AAII researchers since 2017 up to 24.
Toward Human-guided Safe Reinforcement Learning in the Real World - Prof Ling Chen
This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language models, and human-guided continuous correction for safety improvements. The established theories and developed algorithms will advance frontier technologies in AI and contribute to a wide range of real applications of safe RL, such as robotics and autonomous driving, bringing enormous social and economic benefits.
See more: DP240102349
Next-Generation Distributed Graph Engine for Big Graphs - Prof Lu Qin
This project aims to develop an efficient and scalable distributed graph engine to process big graphs. In particular, we will investigate the foundations for the distributed real-time graph engine, focusing on graph storage and graph operators, and then provide solutions for a set of representative graph mining and query processing tasks. Expected outcomes of this project include theoretical foundations and a scalable real-time graph engine to process big graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and road networks.
See more: DP240101322
Mitigating the Influence of Social Bots in Heterogeneous Social Networks - Prof Xiaojun Chang
This project aims to mitigate the influence of social bots in dynamic and constantly changing social networks. Social bots can spread misinformation, manipulate public opinion, and compromise privacy and security. This project will use advanced algorithms to detect and neutralize the impact of social bots, improving the integrity and accuracy of information on social media. The expected outcomes include the development of a robust system for identifying and mitigating social bot influence, and the reduction of harmful content and misinformation on social media. The benefits of this project include a more trustworthy and secure social media environment, protection of individuals and organizations from malicious activities.
See more: DP240100181