Building AI Without Boundaries
Description
Join Queensland University of Technology (QUT), the Australian Academy of Technological Sciences and Engineering (ATSE) and ARC Industrial Transformation Training Centre—Joint Biomechanics (ITTC) for a high-impact workshop addressing two fundamental barriers to real-world AI deployment: data that cannot be shared, and compute that cannot be scaled affordably.
Across sectors, from finance and defence to resources, manufacturing, and government, organisations face the same constraint: the AI systems they need depend on data they cannot move and infrastructure they cannot always justify. This workshop brings together researchers, industry leaders, and policy experts to examine emerging approaches that are reshaping this paradigm and their implications for Australian competitiveness, security, and sovereign capability.
The workshop will identify practical pathways to deploy secure, scalable, and cost-effective AI across organisations and jurisdictions without surrendering control of sensitive data. The outcome will be a clearer roadmap for how Australia can lead in trustworthy, sovereign AI.
You can attend in person, or online via Zoom.
Register for 'Remote Attendance' and a Zoom link will be emailed to you prior to the event.
If you are unable to attend in person, you can dial in to our Zoom:
Join Zoom Meeting:
https://qut.zoom.us/j/87505670074?pwd=aXj5rRea8gphcyqwageLNb4XV4JbpH.1&from=addon
Meeting ID: 875 0567 0074
Passcode: 661926
Co-Chairs
Distinguished Professor Yuantong Gu: Pro Vice-Chancellor (Research Career Advancement) and Head of School (Mechanical, Medical & Process Engineering) at QUT.
Professor Clinton Fookes: Head of School, Electrical Engineering and Robotics (EER), Professor in Vision & Signal Processing, and Director of the SAIVT Lab (Signal Processing, Artificial Intelligence and Vision Technologies) at QUT.
Dr Laith Alzubaidi: Senior Research Fellow in AI at QUT; and AI Lead Researcher at ATD AI and Aptium.ai.
Adjunct Professor Mel Bridges: Adjunct Professor in the School Mechanical, Medical & Process Engineering at QUT; Director of Tissue Therapies Limited and Chairman of Parma Corporation.
Angela Dahlke: Manager, Industry Engagement (Data Sciences).
Speakers & Panellists
Dr Rae Donovan: Chief Clinical Information Officer, eHealth Queensland.
Damian Green: Board Chair, Australasian Institute of Digital Health (AIDH).
Distinguished Professor Jie Lu AO: University of Technology Sydney, IEEE Fellow, IFSA Fellow, Australian Computer Society Fellow, Australian Laureate Fellow in AI and Australian Industry Laureate Fellow in AI-for-Health.
Distinguished Professor Kerrie Mengersen: Distinguished Research Professor in Statistical Science at QUT.
James Moore: Engineering and Design Manager, NEXTDC.
Craig Scroggie: CEO and Managing Director, NEXTDC.
Professor Cori Stewart (ATSE Fellow): Founder and CEO of ARM Hub.
Professor Karin Verspoor (ATSE Fellow): Dean of the School of Computing Technologies at RMIT University.
Moderators
Professor Clinton Fookes (ATSE Fellow): Head of School, Electrical Engineering and Robotics (EER) at QUT.
Professor Raja Jurdak: Professor of Distributed Systems & Chair in Applied Data Sciences, Director of the QUT Energy Transition Centre, and Director of the Trusted Networks Lab at QUT.
10:00am | Coffee and Networking |
|---|---|
10:30am - 12:00pm | Session 1: Sovereign AI: Training Without Sharing Data MC: Prof Clinton Fookes Speaker presentations facilitated panel discussion |
12:00pm - 1:00pm | Networking lunch |
1:00pm - 2:30pm | Session 2: Efficient AI at Scale: Doing More with Less MC: Prof Raja Jurdak Speaker presentations and facilitated panel discussion |
2:30pm - 3:00pm | Closing Remarks — Distinguished Prof. Yuantong Gu |
3:00pm - 4:00pm | Networking refreshments |
Agenda
- 10:30 am - 12:00 pm
Session 1 - Sovereign AI: Training AI without sharing data
Organisations hold vast reserves of sensitive data that cannot, and should not, be centralised. Commercial confidentiality, national security obligations, and tightening regulatory frameworks make traditional AI approaches not just inefficient, but strategically untenable. At the same time, the demand for more capable and intelligent systems continues to grow. This session explores how AI can learn across distributed organisations without moving data. By enabling learning to occur locally while exchanging only model-level knowledge, new decentralised approaches make true cross-organisation collaboration possible, without central servers or data transfer. Speakers and panellists will examine the technical, governance, and strategic implications of this shift, and what it means for sovereignty, compliance, and Australia’s position in a data-driven global economy. Why it matters now: Regulatory pressure on data sovereignty is accelerating globally. Organisations relying on centralised AI face increasing legal, financial, and reputational risk — and that pressure will only intensify. What we’ll explore: How AI can be trained across distributed institutions with zero data movement, how decentralised approaches compare to existing alternatives, and what sovereign AI means for Australian industry and government.
- 1:00 pm - 2:30 pm
Session 2 — Efficient AI at Scale: Doing more with less
AI is becoming prohibitively expensive. Training modern systems demands significant computing, energy, and time, creating barriers that determine who can participate in the AI economy. The assumption that more capable AI requires more infrastructure is no longer sustainable. This session examines how modular, adaptive learning frameworks are redefining AI construction by decomposing large-scale problems into smaller, coordinated components that can be trained efficiently and integrated without full retraining. These approaches reduce cost, accelerate development, and enable continuous updates as new data, tasks, and operational demands emerge. Speakers and panellists will explore how organisations can build and scale AI systems without centralised infrastructure, and how these methods unlock broader participation across industries — including finance, resources, manufacturing, defence, and research. Why it matters now: Hardware advances are no longer keeping pace with the computational demands of modern AI. Smarter, more efficient approaches are essential for scalability and broad participation. What we’ll explore: How modular and hierarchical training reduces cost and time, how AI systems can scale without centralised retraining, and how models remain current in a dynamic, real-world environment.
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