DP 2025 Success: AAII Leads Three ARC Discovery Projects

In the recent ARC Discovery Projects announcement for 2025, AAII academics have secured research grants for three innovative projects as part of 18 projects awarded across UTS. Discovery Projects funded by the ARC aim to expand Australia's knowledge base and research capacity while delivering economic, commercial, environmental, social and cultural benefits. Congratulations to the lead investigators Associate Professor Guodong Long, Professor CT Lin, and Associate Professor Bo Liu on their successful research projects in Machine Learning, Artificial Intelligence, and Cybersecurity & Privacy. 

Federated Foundation Models for Recommendations - Associate Professor Guodong Long 

Foundation model (FM) is a machine learning term to describe the technology of developing large language models. This project aims to develop an FM-empowered recommendation framework with powerful modeling capacity, privacy preservation, and fine-grained personalisation. The project's outcomes can enhance existing recommendation models by leveraging the changing preferences of users and evolving tendencies with privacy preservation. The project can benefit Australian users by improving recommendation services with greater privacy protection and better user experience. Anticipated outcomes include new knowledge, algorithms, and toolkits for use in developing new service architecture in real applications, such as video and commercial goods.

See more: DP250101576

Mind-reading AI to Translate Silent Speech into Words – Professor Chin-Teng Lin 

The project aims to develop a system that can translate words that are not spoken aloud into speech for people to communicate and interact through their thoughts. It proposes an unprecedented model to process words in sentences to produce natural language. The system will adapt to individuals. Expected outcomes include new understanding of how the brain processes language, artificial intelligence (AI) models for interpreting data from brains and recognising speech elements, and a novel online feedback system to improve how humans and AI interact. The system could transform care sectors, assistive technologies, defence and entertainment as well as advancing AI, human computer interface, robotics, linguistics and computational neuroscience.

See more: DP250103612

The Paradox of Generative Data: Ensuring Security and Privacy - Associate Professor Bo Liu

The project aims to address the security and privacy challenges associated with generative data. The project will examine the current approaches and techniques for ensuring the safety and privacy of generative data and use this knowledge to develop controllable and traceable data generation methods, new privacy protection methods, and forensic techniques. The result will be a comprehensive suite of tools and techniques for generating secure and private synthetic data, preserving individual privacy, and detecting fake data and manipulation across multiple modalities. This solution will help to ensure the security and privacy of artificial data in critical applications such as machine learning and artificial intelligence.

See more: DP250100463

See the full list of UTS DP25 projects