Artificial intelligence programs

Artificial intelligence programs

• AI-enabled Medical Technologies: From Diagnostics to Therapeutics (Awarded November 2022)
• AI for Clean Energy and Sustainability (Awarded November 2022)
• AI for Next Generation Food & Waste Systems (Awarded November 2022)
• AI Techniques for Emergency Management and Critical Infrastructure (Awarded November 2022)
• Human-AI Interaction in the Metaverse (Awarded November 2022)
• RAISE: Creating Responsible AI Software Engineering Capability (Awarded November 2022)
• Sports Data Science & AI Consortium: Growing the Skillsets, Toolsets & Mindsets for Transformation, Innovation &/or Trajectory 2032 (Awarded November 2022)
• AI Enabled Advanced Materials Technology (Awarded April 2022)
• AI in mental health (Awarded April 2022)
• All System analysis–analytics and intelligent automation (Awarded April 2022)
• The Offramps Project: Finishing School Well (Awarded April 2022)
• Towards AI on the edge: Developing data-efficient machine learning models for multimodal sensing devices and IoT (Awarded April 2022)

AI-enabled Medical Technologies: From Diagnostics to Therapeutics

The digitalisation of medicine holds promise in revolutionising healthcare and well-being. At the core of this revolution is the development of data-driven technologies, in particular artificial intelligence (AI) techniques, to interpret and integrate the vast amount of data generated at individual and population levels to address diverse challenges confronting patients, clinicians, and healthcare systems. Low-cost gene sequencing, medical imaging, clinical records, and sensor data provide an ever-increasing flow of digital health data. These data are high-dimensional, multi-modal, noisy, and biased, posing significant challenges to the development of AI models that are accurate, reproducible, trustable, and equitable. This program is aimed at developing cutting-edge AI solutions that contribute to addressing these fundamental challenges across diverse applications from disease diagnosis and prognosis and the prediction of therapeutic interventions to the development of clinical decision support systems and automated clinical reporting.

The program brings a multidisciplinary team of experts in AI, computer vision, genomics, proteomics, therapeutics, bioinformatics, information systems, health informatics, and evidence-based medicine at UNSW, UTS, and Macquarie University, with strong support from 6 industry partners to train a cohort of 15 HDR students in developing innovative AI-driven medical technologies aligned with industry priorities.

AI for Clean Energy and Sustainability

Delivering clean and sustainable energy for mitigating climate change and enabling energy transition is one of the biggest challenges facing the world. Australia's climate change strategies are committed to supporting industries, businesses, and consumers to innovate and adopt smarter practices and technologies to reduce emissions and balance supply and demand from renewable sources.

Digital transformation is reshaping the energy sector, making energy production and consumption more efficient and reliable. The wide adoption of digital devices enables greater connectivity between operations, businesses, and customers and collects massive energy data, opening up the opportunity of adopting AI technologies.

AI is expected to play a significant role in energy transition, leveraging the massive energy data to produce effective models and tools and provide insights, such as more accurate prediction for reliable supply, optimised maintenance and operations, smarter decision-making hedging risks.

This program aims to nurture Australia's next-generation workforce by focusing on the Recycling and Clean Energy National Manufacturing Priority. We are proposing a multi-disciplinary cohort of 7 PhD, 3 MPhil, and 3 master/honour students at Monash and RMIT across AI and energy to explore innovative technologies driven by real-world industry priorities and grounded in consumer perspectives for strengthening Australia's digital economy.

AI for Next Generation Food & Waste Systems

This program addresses the skills shortage in adopting advanced Artificial Intelligence technologies in the areas of Food & Waste, a critical national manufacturing priority. This will boost food productivity, improve food quality control and logistics, reduce, and better manage waste generated during the life cycle of food production and consumption.

This program is of great significance as the global food crisis deepens day by day. Through a range of industry driven research activities, this program will produce a cohort of graduates that are not only equipped with practical AI skills but also ready to integrate into food and waste related industry sectors to generate real impact onto our society.

This cohort will gain deep knowledge on food value chain from industry experts, develop tailored AI solutions for certain segments of the value chain, and deploy their AI techniques to create tangible benefits.

The activities include improving yield and control for farms, such as cattle farms, mushroom farms and vineyards, by developing machine vision techniques to achieve real-time monitoring; improving food processing procedures, production practices and fresh food delivery, by using heuristic based optimisation; improving efficiency in waste collection and management, by establishing data driven scheduling tools.

AI Techniques for Emergency Management and Critical Infrastructure

This program will produce a cohort of graduates with much-needed skills in Artificial Intelligence to support critical infrastructure and community safety. This cohort will develop innovative AI solutions to industry-led problems ensuring the graduates are work-ready. The cohort-based approach will provide an intellectual climate around common themes that will support and stimulate the candidates to produce greater outcomes than a silo-based approach.

The industry projects have been carefully selected to be complementary but independent allowing each student to produce unique outcomes. Some of the common AI techniques across the selected projects are computer vision, agent-based modelling and simulation (ABMS) and digital twin technology. Computer vision is used in creating 3D reconstructions from 2D images of interior designs and to detect potential hazards and threats via surveillance videos. ABMS is becoming increasingly popular to model and simulate the management of disaster events such as floods and bushfires. Digital twins involves complementary approaches of digitising models of infrastructure, the people, and the business processes and one of the projects investigates the integration of all three aspects. The projects in this program will advance the state of the art for both private and government agencies.

Human-AI Interaction in the Metaverse

The future of the metaverse and related technologies will bring both opportunities and challenges that will impact and revolutionise human societies in a variety of ways. Parallels can be drawn to the changes, challenges, and opportunities that have been disrupting our lives related to advances in artificial intelligence (AI) over recent years. In combination, the metaverse and AI present a plethora of significant changes and challenges that urgently need to be envisioned and investigated, so that we can proactively consider how we can best design, develop, manage, and regulate these technologies, with full consideration of their potential implications and ramifications.

This program brings together a multidisciplinary cohort of students, researchers, industry, and projects to investigate human-AI interaction in the metaverse from diverse perspectives. The projects will encompass issues of ethics, experience, interfaces, and technology related to human-AI interaction in the metaverse and will consider specific application areas, such as virtual courtrooms and justice, health and medicine, defence, mining, architecture, training and simulation, and manufacturing. The interdisciplinary cohort will draw on expertise and methods from computer science, psychology, health, criminology, philosophy, and law.

RAISE: Creating Responsible AI Software Engineering Capability

The Responsible AI Software Engineering (RAISE) program aims to create the first national cohort-based world-leading training centre addressing the urgent need for deep expertise and excellent skills in Responsible AI Software Engineering for digital health, transportation, and defence sectors.

Artificial Intelligence (AI) techniques are increasingly employed by industry and government alike to make or inform high-stakes decisions for people. Although AI techniques are solving real-world challenges and transforming industries, there are serious concerns about their ability to behave and make decisions in ways that are considered responsible. RAISE will urgently address these national challenges by training the next generation of graduates to better design, develop, manage, maintain, test, and deploy AI software in a cost-effective way such that their AI algorithmic decisions are responsible to society.

RAISE will produce 11 graduates (7 PhDs, 1 Masters, and 3 Honours) who can innovate, operate and lead the design, development, management, maintenance, testing, and deployment of AI solutions while adhering to the Responsible AI principles. To achieve this, RAISE's graduates will aim to investigate and develop actionable theories, frameworks, and practical techniques and tools to help companies cost-effectively deliver Responsible AI-based software systems, providing significant benefits to students, academia, industry, governments, and society.

Sports Data Science & AI Consortium: Growing the Skillsets, Toolsets & Mindsets for Transformation, Innovation &/or Trajectory 2032

Australia has a deadline: 2032. The impending Olympics has highlighted the crucial importance of data in sport, and the critical lack of experts to manage and analyse these data. Data underpin prediction of future Olympians, optimisation of athlete performance, prevention of injury and protection of mental health.

Sports data analytics is big business: a global worth of USD 2.20B in 2020 and expected CAGR of 21.8% by 2028. It represents a huge job market: in Australia, over 800 sports data analyst vacancies in June 2022 alone. It is also at the forefront of global issues of data integrity, sovereignty and trust.

This Program aims to train a cohort of sports data scientists in high performance sports and para-sports. It will meet the urgent demand by sports organisations for experts in ethical data governance and analysis to improve sports integrity, diversity, recruitment, performance, injury prevention.

By training new multidisciplinary students and up-skilling existing professionals in sports organisations, the Program will step-change Australia's trajectory for 2032. This new generation of graduates with broad skillsets, world-class toolsets and ethical, entrepreneurial mindsets will be AI-work-ready not only for sport but also Defence, Business, Health, Social Sciences and other data-focused fields.

AI Enabled Advanced Materials Technology

This project will contribute to a critical and timely upskill in Australia's capability in applied artificial intelligence (AI) – firmly impacting the Science and Research Priority area of Advanced Manufacturing.

The proposal will train a PhD cohort, with world class supervision and industry partnership in two core areas; namely Machine Learning (ML) and Computer Vision (CV). Research students will participate in industry-led research projects and placements to build job-ready skills.

In the domain of ML, this project aims to develop and demonstrate new, reliable methods for materials design, combining machine learning with design thinking. The development of advanced materials has a long history in Australia, and unlike the design of other engineered products, there is no clear design methodology for materials to date. Exploiting ML will enable rapid design and development of alloys for specific applications – with sustainability in the design process.

In the domain of CV, advanced materials technology that includes (i) durability assessment of infrastructure (using robotic vision) and, (ii) integration of CV into the manufacturing process – are critical for enhanced operational efficiency and building emerging technology capability.

The development of technologies in the domain of materials is critical in meeting rapidly emerging requirements and sovereign societal needs.

AI in mental health

Almost half of Australians will experience mental ill health at some point in their lives, however nearly 70% of those with a known mental health condition do not seek professional help due to factors such as stigma, cost, limited access to care and fear of hospitalisation. Most mental health problems are present and established between the ages of 14-24, highlighting the importance of early detection and intervention.

Aside from the human cost of mental illness, the economic burden is considerable, with a reported $10.6 billion spent on Australian mental health services and reported productivity losses in industry due to mental ill health being as high as $39 billion per year. As such, improving the mental health of the Australian population is not only a significant societal need, but also a government priority that requires attention.

There are significant opportunities for AI to help in this domain, from facilitating data supported decision making in hospitals through to enabling self-directed intervention via everyday technologies.

We are proposing a cross-disciplinary cohort of 20 PhD and Masters students across Melbourne, Monash and Monash Malaysia (independently funded) Universities to explore innovative approaches towards AI driven methods and solutions, driven by real-world industry priorities.

All System analysis–analytics and intelligent automation

The aim of this program is to develop an ‘all system analysis’ program that provides post-graduate students with an authentic learning experience by adopting the cohort-based approach to solving real-world engineering problems, thereby enabling them to develop key transferrable skills necessary for future employment. The cohort is carefully designed to comprise 11 distinct but inter-related projects, covering virtually all use cases of data analytics, including to personalise user experience, inform business decision-making, streamline operations, mitigate risk and handle setbacks, enhance safety and security .

Further, the cohort project aims to link process control and machine performance with visualization and human-machine interaction and tackle the critical industrial objectives of developing hybrid man/machine systems where the machine response can be autonomous but benefit from human input.

Students in the cohort with strong math and data analytics backgrounds will be complemented by their peers with product design and manufacturing knowledge, and vice versa, which allow them to seamlessly transfer their abstract ideas from theoretical analyses to practical implementations. The common themes that the student cohort will be working on are related to the algorithm and prototype development, which will later be tested in individual industry partner’s environment.

The Offramps Project: Finishing School Well

This proposal will grow a world-class cohort of graduates who will combine innovative Artificial Intelligence (AI) methods with individual case-based research to rapidly learn the causal pathways which help young people finish school well providing an off-ramp to social disadvantage.

The proposed research programme builds a broad partnership across the NSW government Department of Education, leading researchers in data science, from the University of Technology Sydney and CSIRO as well as leading researchers in the social sciences from the University of Western Sydney and the Australian National University, to train a multi-disciplinary team of AI savvy graduates equipped to use the latest advances in AI for social good.

This connectivity will enable students to build substantial new cross-disciplinary capacity and knowledge in data-driven discovery for social change. The goal is to draw the community of data science and domain science researchers together with government bodies to address the substantial and challenging problems posed by social disadvantage in the context of education.

Towards AI on the edge: Developing data-efficient machine learning models for multimodal sensing devices and IoT

The project aims to advance novel data-efficient machine learning techniques for modelling sensor data obtained by resource-constrained devices, such as microcontrollers, wearables, mobile devices, and IoT. These data are typically high in dimension, noise and uncertainty, and highly varied in its modality, purpose and tasks, ranging from end-consumer wearable sensors and devices capturing human activities and physiological signals to infrastructure sensors and IoT capturing large-scale urban flows and mobility patterns.

This program seeks to enable modelling of human behaviours more efficiently at the edge, with little or no labelled data. It expects to make major breakthroughs in the modelling of human behaviour through a generalised representation learning techniques for different downstream tasks and domains, including transportation and mobility, building construction and management, agriculture and mining, and health and productivity.

The program involves three Australian leading institutions, UNSW, Curtin, and ANU, with strong support from six industry partners: Aurecon, STSA (a subsidiary of SoftBank Japan), SAP, N2N.AI, and HugHealth. The program seeks to train a cohort of twelve students, including four PhD, one Master by Research, and seven Honours students, with the goal to train them on developing and deploying AI on the edge, and embed them in industry-relevant problems.