Monash Information Technology awarded $2.3 million in 2025 Australian Research Council Discovery Projects

06 December 2024

In the recently announced Australian Research Council (ARC) Discovery Projects (DP) round, the Faculty of Information Technology (IT) is leading four applications totalling more than $2.3 million while co-leading a further two.

"The outstanding success of our researchers in the ARC Discovery Projects scheme reflect not only our expertise in AI, robotics and sustainable design but also our commitment to advancing knowledge that drives innovation and delivers meaningful societal impact. Congratulations to all our researchers for their remarkable achievements.’ said the Faculty’s Deputy Dean (Research) Professor Jesper Kjeldskov.

The six groundbreaking initiatives involving the Faculty of IT reflect Monash’s commitment to key areas such as artificial intelligence (AI), sustainable design, and robotics:

1. Securing Privacy-Preserving Cloud Computation Against Active Attacks (Lead)

Associate Professor Ron Steinfeld, Associate Professor Amin Sakzad, Dr Muhammed Esgin, Professor Damien Stehlé

Developing an innovative security kit of advanced cryptographic tools to significantly enhance the privacy of cloud services, paving the way for the secure deployment of applications such as privacy-preserving AI services – while reducing the risk of cloud data privacy breaches plaguing enterprise and personal data in recent years.

Funding: $464,003

2. Encoding Material Agency: Generative Design for a Sustainable Future (Lead)

Professor Jon McCormack, Professor Roland Snooks

Developing novel design processes that embed material behaviour within agent-based and machine learning computational design strategies. This will open new territories in architectural creativity and deliver a sustainable blueprint to minimise waste, curtail mineral reliance and reduce the carbon footprint of construction while producing new design knowledge via architectural prototypes fusing computational design, robotic craftsmanship, and biomaterials.

Funding: $549,325

3. Can Machines Unlearn? Toward Next-Generation Safe Artificial Intelligence (Lead)

Professor Dinh Phung, Associate Professor Mehrtash Harandi, Dr Trung Le, Dr Jing Zhang, Professor Jianfei Cai

Inventing new principles, theories, algorithms, tools, techniques, methods and IP to enable AI systems to unlearn undesirable artifacts – safeguarding user data, bridging AI skills gaps and contributing to Australia’s position as a global leader in ethical and responsible AI.

Funding: $790,750

4. Formal Explainability for Neuro-Symbolic Artificial Intelligence (Lead)

Professor Peter Stuckey, Dr Alexey Ignatiev

Enhancing trust in neuro-symbolic AI systems by creating formal methods that provide 100% accurate explanations into their decision-making that are easily understood by humans. AI is already widely used in critical decision-making, but weaknesses in the reasoning capability of black-box AI has driven the development of neuro-symbolic AI combining the strength of black-box learning with reasoning.

Funding: $515,966

5. Fabrication of 3D Neural Networks for Next-Generation Biocomputing (Co-led)

Professor John Forsythe, Emeritus Professor Helena Parkington, Associate Professor Levin Kuhlmann (IT), Professor Marie-Isabel Aguilar

Driving the next generation of biocomputing by developing a new 3D bioprinted system that will produce scalable neuronal networks which can be interfaced to communicate with the real-world and perform recognition tasks – leading to faster decision-making, continuous learning and enhanced energy efficiency.

Funding: $701,415

6. Accurate and Fast 3D Stiffness Mapping via Vision-Guided Robotic Probing (Co-led)

Dr Liao Wu, Associate Professor Zongyuan Ge (IT)

Enabling remote haptic evaluation in critical sectors such as healthcare and manufacturing by developing novel methods to generate 3D stiffness maps of deformable surfaces within confined spaces, facilitating remote estimation of the mechanical properties of delicate objects with limited accessibility. This project expects to achieve high accuracy and efficiency by seamlessly integrating computer vision, machine learning and robotics while driving outcomes such as new frameworks and algorithms for precise 3D reconstruction using visual and tactile data, accurate single-point stiffness estimation and efficient sampling strategies for stiffness mapping of large surfaces.