Australian Artificial Intelligence Institute (AAII)

Dr. Madhushi Bandara is a Senior Lecturer in the School of Computer Science at University of Technology Sydney, Australia, driving teaching and innovation across artificial intelligence (AI) and software systems sectors. She joined UTS in 2020 as a postdoctoral researcher after completing her PhD at UNSW in AI systems engineering and resumed the continuing academic role in 2022. Madhushi is the Course Coordinator for Master of Technology Management (UTS Online) & Master of Technology Innovation (CinLearn) programs.

Madhushi is the Co-Director of UTS DigiSAS lab, and leads the data management research stream contributing to Research & Innovation in Data-intensive Digital Ecosystems. She is a core member of Biomedical Data Science Laboratory in the UTS Australian Artificial Intelligence Institute and the convenor for Future Generation Enterprise Architecture Community of Practice (FGEA CoP) that provids a platform to EA practitioners, researchers and thought leaders from government, higher education and industry to exchange innovative ideas and best practices for architecting and building human-centric AI, data and digital ecosystems.

Madhushi has established a national reputation as an expert in knowledge representation and complex system modelling using technologies such as machine learning and knowledge graphs. She has made significant theoretical and practical contributions to the field, by bringing the advancements in state-of-art AI/ML technologies to information systems in critical sectors such as healthcare and finance through harmonising contextual domain knowledge, data science and management practices, software engineering best practices and organisation business architecture. The synergy of her research and industry needs today enable her to attract industry partners and build long-term partnerships. She has worked with industry partners including Telstra, Cancer Australia, Capsifi, LIRNEasia, Landcom NSW and Cognitivo Consulting to design and deliver knowledge representation and data analytics solutions.

Michael Blumenstein is currently a Visiting Professor in the School of Computer Science at the University of Technology Sydney (UTS) where he previously served as the Pro Vice Chancellor (Business Creation and Major Facilities). Earlier, Michael served as the Acting Dean and the Deputy Dean (Research and Innovation) in the Faculty of Engineering & IT at UTS, and prior to this, he held the positions of Associate Dean (Research Strategy and Management) and Head of the School of Software (now Computer Science). Previously, Professor Blumenstein worked at Griffith University in Queensland where he accumulated over a decade of experience in leadership roles including portfolio Dean (Research) of the Sciences Group and Head of the School of ICT.

Professor Blumenstein has served in numerous leadership roles with industry and professional associations over two decades, including most recently as a Board Member of the Internet of Things Alliance Australia (IoTAA). Previously he served as Vice President (Academic) of the Australian Computer Society (ACS), and prior to that as Director of Technical Advisory Boards. Professor Blumenstein was also elected onto and served as a member on the NSW Council of the Australian Information Industry Association (AIIA). Prior to moving to NSW, he served as the Chair of the Queensland Branch of the Institute for Electrical and Electronic Engineers (IEEE) Computational Intelligence Society. He was also the Gold Coast Chapter Convener and a Board Member of the ACS Queensland Branch Executive Committee. He is a past Chairman of the IT Forum Gold Coast and a former Board Member of IT Queensland.

Michael has served on the Australian Research Council's (ARC) College of Experts on the Engineering, Mathematics and Informatics (EMI) panel. In addition, he also served on the Executive of the Australian Council of Deans of Information and Communication Technology (ACDICT).

Prof Daniel Catchpoole is an expert with over 20 years of lab-based research experience in the area of childhood cancer research, tissue pathology, genomics and cell biology. He has been working in the School of Computer Science and the Biomedical Data Science Lab in the Australian Artificial lntelligence lnst¡tute for the past 2 years, but prior to this for the last 17 years (our first paper was in 2OO4). These projects bring together within FEIT, skills and knowledge in data analytics, artificial intelligence and software development with Dan's expertise in the molecular basis of childhood cancers and genomics to develop improved mapping of cancer treatment pathways. This is transformational research, and has in recent years received significant funding to support ongoing research and development from sources including the Cancer Institute of NSW and the Sony Foundation.

Since 2001, his scientific achievements and publications have primarily focused on the assessment of childhood tumours, with specific attention given to acute lymphoblastic leukaemia and neuroblastoma. Prof Catchpoole’s research focus has centred on translational research through gene expression profiling of paediatric tumours. Recent publications have dealt with the assessment of gene expression signatures proposed to identify cancer patients at risk of treatment failure. This work has lead to his current research developing a systems biology approach to the assessment of cancer patients which includes the implementation of the data-mining and visualization of complex multidimensional biomedical data derived from various high-throughput applications.

Prof Catchpoole is expert in building the necessary frameworks essential for active translational research activity in cancer. This includes experience of basic research into cell and molecular biology, knowledge of clinical pathways through medical institutions including the pathology departments practices, everyday management of biobanks and linkage to required clinical data, the generation of complex genomic and biomedical dataset by high-throughput technologies (eg microarray and next generation sequencing) and the high-end computational analysis of complex multi-dimensional data to draw out actionable knowledge which has clinical relevance.

Consequently, Prof Catchpoole brings with him extensive real-world experience in the application of governance and regulatory requirements around which translational research must be practiced. He has experience in the micro-management of a team of research professionals as well as the strategic management of valuable research infrastructure within a Public Hospital. He has built a strong record for professional networking bringing groups into collaborative arrangements around singular themes and opportunities from across the nation. This includes being a founding member and first President of The Australasian Biospecimens Network Association. His leadership in these and other areas has been recognized across the translational research disciplines and has contributed to a number of committees at local, state and national levels. Finally, Prof Catchpoole is an innovative and strategic thinker, creating novel but productive projects which enhance our understanding of the translational research in paediatric cancer.

Dr Ling Chen is a Professor in the School of Computer Science at the University of Technology Sydney (UTS), Australia. She received her PhD in Computer Engineering from Nanyang Technological University (NTU), Singapore, and subsequently completed postdoctoral training at the Leibniz University Hannover's L3S Research Centre in Germany.

Dr Chen’s research focuses on trustworthy machine learning, with particular emphasis on anomaly detection in graph-structured data and its applications, including fraud detection in financial transactions and malicious user detection in recommender systems. More recently, her work has expanded to the development of advanced decision-making agents, leveraging reinforcement learning and large language models (LLMs) to enable safe and reliable decision-making in interactive systems. Her current research includes hallucination detection and mitigation, as well as the design and implementation of guardrails for LLM-powered agents.

Dr Chen has successfully secured multiple competitive research grants, including an Australian Research Council (ARC) Future Fellowship (Level 3), underscoring the significance and impact of her contributions to the field. Her research has attracted funding and recognition from leading industry partners, including Facebook, TPG Telecom, and Zurich Insurance, as well as government agencies such as the NSW Department of Environment, Climate Change and Water, and the NSW Police Force.

As Research Director of the Australian Artificial Intelligence Institute (AAII), Australia’s largest AI research institute, Dr Chen leads the strategic development of a research community comprising more than 30 researchers and over 200 higher degree research students. Throughout her career, she has supervised 38 higher degree research (HDR) students and six postdoctoral researchers.

Dr Chen has delivered keynote addresses at six major national and international conferences in machine learning and data science. She currently serves as the IEEE Task Chair for Data Science and Advanced Analytics, contributing to the advancement of research and professional practice in the field.

Dr Tobias Cibis is a Lecturer at the School of Computer Science in the Faculty of Engineering and IT at the University of Technoloy Sydney.

My research focuses on the development of next-generation computational physiology frameworks that mechanistically model, simulate, and predict human physiological responses across multiple biological scales. The overarching aim is to create physics-informed digital twins of human body capable of integrating first-principle physiological modelling, clinical data, artificial intelligence, and advanced software engineering to improve human health, safety, and healthcare delivery.

A central feature of this research is the representation of the human body as a complex adaptive system in which cardiovascular, respiratory, neurological, metabolic, immunological, and tissue-level processes interact through feedback mechanisms governed by established principles of physics, physiology, and biology. Rather than relying solely on data-driven predictions, the research seeks to explain physiological behaviour by modelling the underlying processes that generate observable outcomes.

The program spans four major research domains: computational physiology for extreme environments, digital health and personalised medicine, wound healing and regenerative system, and perioperative physiology under anaesthesia. These domains are unified through the development of mechanistic digital twins and integrated computational modelling platforms that can support scientific discovery, clinical decision making, precision medicine, and public health applications.

Tobias' research interests are: 

• Computational and mathematical modelling of physiological systems and interactions with extreme environments with a focus on 
  • Diving and Hyperbaric Medicine
  • Space Medicine
  • Wound Healing
  • Wildlife and Veterinary Science
• Data Engineering and Information System Platforms 
• Decision-Support Systems 

He has been hosting several workshops and special sessions at IEEE EMBS Conferences and is editor and author of the Springer Nature book "Engineering and Medicine in Extreme Environments".

Prof. Daoyi Dong is a Professor and a Future Fellow of the Australian Research Council at the Australian Artificial Intelligence Institute, UTS. He joined UTS in 2025. Before joining UTS, he was a Professor at School of Engineering, Australian National University (ANU). He is a Fellow of the IEEE and a Fellow of Australian Institute of Physics.

He serves as a Vice President, IEEE Systems, Man and Cybernetics Society (2025-2026), a member of Board of Governors, IEEE Control Systems Society (2025-2027), an Associate Editor of Automatica and IEEE Transactions on Cybernetics. He was a member of Board of Governors, IEEE Systems, Man and Cybernetics Society, an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, a Guest Editor of Annual Reviews in Control.

He is a recipient of a Humboldt Research Fellowship from the Alexander von Humboldt Foundation of Germany, the inaugural ACA Temasek Young Educator Award from the Asian Control Association, an International Collaboration Award, a Discovery International Award and an Australian Postdoctoral Fellowship from Australian Research Council.

Dr. Yining Hu has collaborated with multiple industry partners to develop software solutions leveraging AI, Blockchain, Cloud, and IoT through practice-oriented research collaborations and consulting arrangements.

She also runs the Software Development Studio (SDS) subject, which has helped multiple SMEs modernize their websites, automate workflows, analyse data, and explore AI and Cloud integrations.

Professor Paul Kennedy has been recognised for his decades-long contribution to data analytics teaching, learning and academic leadership.

As Professor and Head of the School of Computer Science in the Faculty of Engineering and Information Technology, Paul is one of the main data analytics teachers at UTS. He teaches at undergraduate and postgraduate levels, both coursework and research students, and in short courses. His teaching was recognised by a 2013 Office for Learning and Teaching (OLT) Citation for Outstanding Contributions to Student Learning, and he won a 2012 UTS Learning and Teaching Award for Strengthening the UTS Model of Learning, a 2020 Team Teaching Award and a 2019 Team Teaching Citation.

Paul also enjoys collaborative and multi-disciplinary research, with a focus on the data analytics of biomedical data. He mainly collaborates with paediatric cancer researchers to better understand and predict treatment outcomes for childhood cancer sufferers.

He is a co-initiator of a research collaboration with the Children’s Hospital at Westmead, the University of Western Sydney and Queen’s University, Canada that, in part, involved developing a point-of-care clinical software tool to better diagnose and treat childhood leukaemia sufferers.

He explores also other areas of data analytics and bioinformatics, including developing bioinformatics pipelines to facilitate animal vaccine discovery, developing text analytics approaches to scientific literature and social media, and developing methods for use with flow cytometry data.

Paul is the Lab Director of the Biomedical Data Science Laboratory in the UTS Australian Artificial Intelligence Institute, which aims to use knowledge as the infrastructure to support decision making in biomedicine. He is also co-director of the UTS Ontario Tech Joint Research Centre for AI in Health and Wellness.

He has also served the research community by working on the Organising Committee for the Australasian Data Mining Conference since 2006, and has been co-editor of AusDM proceedings many times. He was co-chair of the 2015 Australasian Computing Science Week.

He has contributed to many international program committees and reviewed for international journals and books. He is an ARC Expert Assessor and has co-authored more than 100 publications. He has graduated 17 PhD students and regularly reviews HDR theses. He is also a member of the Institute of Analytic Professionals Australia.

Paul has been awarded research funding exceeding $2.4 million, and has considerable industry experience developing software and running DM projects, including with Cancer Australia mapping population-level cancer treatment pathways. He is a committee member for ISO/IEC SC42 producing standards on ethical use of AI.

Distinguished Professor Chin-Teng Lin is a leading researcher in artificial intelligence (AI) and brain computer interfaces (BCI), developing systems of brain information processing and communication with machines.

Lin studies the brain and behaviours, the physiological changes that occur when human cognitive functions are working, and ways to combine human physiological information with artificial intelligence to develop monitoring and feedback systems.

He wants to improve the flow of information from humans to robots, so humans can make better decisions and respond to complex, stressful situations, and so that robots can better understand the status and intention of humans to augment human-machine cooperation. This is an emerging trend identified in the fifth Industrial Revolution to deliver a common good for humanity.

Lin was the inventor of fuzzy neural networks (FNN) in 1992, introducing neural-network learning into fuzzy systems and incorporating human-like reasoning into neural networks. Since then, there have been about 500,000 articles about FNN published online.

Lin has since developed a series of FNN models with various learning capabilities suitable for different learning environments, as well as targets on multi-agent reinforcement learning for multi-drone coordination and cybersecurity.

Lin joined UTS in 2016 as Co-Director of the Australian AI Institute (AAII) to advance AI and BCI.

A highly published researcher in the fields of machine-intelligent systems and brain computer interface, Lin is also the founding director of the Computational Intelligence and Brain Computer Interface Lab at the Australian AI Institute, at UTS. The Lab is developing mobile sensing technology to measure brain activity using non-invasive methods, to assess human cognitive states.

Lin led a large-scale 10-year project ($10 million) on Cognition and Neuroergonomics (2010-20) with the US Army Research Lab. The project explored advanced BCI technologies by studying the effects of vehicle motion and cognitive fatigue, and developing wearable EEG devices.

Since joined UTS in 2016, Prof Lin had been granted 5 ARC Discovery projects, 2 NHRMC projects, 1 CRC-P project, and 2 Defence innovation Hub projects. He has also attracted quite a lot of industry funding.

In 2020, a team of researchers led by Lin, embarked on a two-year, $1.2 million project with the Department of Defence to examine how cutting-edge technologies could use brainwaves to command and control autonomous vehicles, integrating cognition neuroscience and device engineering to develop wearable technology. The success of this project led to the next phase of 2.5 years, $3.8 million project in 2023 to mature the developed technology in the previous phase.

Lin was Editor-in-Chief of IEEE Transactions on Fuzzy Systems (2011-16) and is on the board of governors of several IEEE societies.

He was elevated to be an IEEE Fellow for his contributions to biologically-inspired information systems in 2005, and was elevated to International Fuzzy Systems Association (IFSA) Fellow in 2012. In 2017, he received the IEEE Fuzzy Pioneer Award.

With a BS degree from the NCTU, Taiwan (1986) and Masters (1989) and PhD (1992) in electrical engineering from Purdue University in the US, Lin also holds academic positions at the University of California and University of Nottingham.

Dr Bo Liu is currently a Professor in the School of Computer Science, a core member and director of the AI Security and Privacy (AISP) Research Lab at the Australian Artificial Intelligence Institute (AAII), at the University of Technology Sydney (UTS).

Dr Bo Liu is an expert in cybersecurity and privacy, privacy protection, AI and machine learning, and wireless communications and networks. He has an excellent publication record, including 4 authored books, 2 book chapters, and over 160 refereed journal and conference papers.

Dr Liu has also set up industry collaborations with organisations such as the NSW Department of Planning and the Reserve Bank of Australia. He serves as Associate Editor for IEEE Transactions on Broadcasting and contributes actively to the academic community through roles such as conference Co-Chair, reviewer for top-tier journals and conferences, and assessor for ARC grant schemes.

Dr Long is currently a Full Professor at the School of Computer Science, Faculty of Engineering and IT (FEIT), University of Technology Sydney (UTS). He is a core member of the Australian Artificial Intelligence Institute (AAII) - UTS's flagship research institute. His research focuses on Artificial Intelligence (AI) and Machine Learning (ML), spanning both foundational theory and real-world applications. His current research interests focus on using federated learning to develop a trustworthy AI with privacy-preserving and personalised intelligence, and also developing new on-device intelligence to cooperate with pre-trained foundation models at the server, e,g, large language models. More details can be found on the Tab page of "Research Interests".  

He is currently leading a research group, namely the foundation model and federated learning (https://www.fmfl.group/). He and his PhD students have published papers at top AI conferences, including ICLR, NeurIPS, ICML, ACL, CVPR, AAAI, IJCAI, KDD, and WWW. His publications have attracted over 42,000 citations (3 August 2026, Google Scholar) with an h-index of 66. His author-normalised citation is  6,991. By 2025, he had 20 papers that received more than 200 citations. 

Dr Long has demonstrated research impact on the real industry. His research interests focus on several application domains, such as NLP, Healthcare, IoT, Education, and Social Media. He has received more than $4m in external investment from the industry. In collaboration with the industry, Dr Long and his team can use their expertise to solve real problems, leading to the innovative application of the industry sector, and also can reshape their research vision with inspiration from the industry. Dr Long and his collaborators have created a small research ecosystem to pave the knowledge transition among theoretical research, applied research, and real applications.

Dr Long is actively engaged with the research communities. He is serving as the general co-chair to organise the WebConf to be hosted in Sydney in May 2025. He is a regular assessor for more than ARC (Australian Research Council) proposals, including DP, LP, DECRA and Future Fellow. He has served as First General Co-chair to lead a cross-national organising committee to organise the ACM Web Conference 2025, which welcomed over 1,400 delegates at ICCSydney. He served to organise the Australasian Joint Conference on Artificial Intelligence by acting as general co-chair (2025) and program co-chair (2021). He has also served as an area chair for a few top AI conferences, e.g., NeurIPS, ICLR, ICML, AAAI, and IJCAI. He was the Job Match Chair for KDD 2015 and IJCAI 2017. The Job Match program aims to provide a face-to-face recruitment opportunity for conference attendees and sponsors. He also served as the program co-chair for the 17th International Conference on ADMA 2021.

Dr Long is always seeking talented and self-supervised PhD candidates. If you are interested in working with him for several years, please send your CV to his email.

Dr Haiyan (Helen) Lu is an Associate Professor in the School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS).

Dr Lu received her Bachelor and Master Degrees of Engineering from the Harbin Insitute of Technology (HIT), China in 1985 and 1988 respectively.

She was a lecturer in the Yanshan University China before she moved to Sydney Australia in 1994. She completed her Ph.D. degree in engineering at UTS in Australia in 2002.

She is a core member of the Decision Systems and e-Service Intelligence Lab in the Centre for Artificial Intelligence (a part of former Centre for Quantum Computation and Intelligent Systems) at UTS.

Professional Activities:

A senior member of IEEE

A member of ACM

A member of IEEE Computational Intelligence Society
A member of IEEE Power and Energy Society

Vice Chair of IEEE NSW Section Women in Engineering Affinity Group

Secretary and Treasurer of IEEE NSW Computational Intelligence Society Chapter

Academic editor of Engineering pplications of Artificial Intelligence (EAAI) since Oct 2023

Distinguished Professor Jie Lu AO (Officer of the Order of Australia) is an internationally renowned scientist in the area of computational intelligence, who has made fundamental and influential contributions, particularly in fuzzy transfer learning, concept drift, data-driven decision support systems, and recommender systems. Her research has huge positive implications and significant impact on her research community and for society and economics.

An IEEE Fellow, IFSA Fellow, Australian Computer Society Fellow, Australian Laureate Fellow in AI and Australian Industry Laureate Fellow in AI-for-Health, Jie has published six research books and over 500 papers in leading journals and conferences, has won 10 ARC Discovery projects and ARC Laureate Fellow project, ARC industry Laureate Fellow project, and also led 20 industry oriented projects including ARC Linkage, CRC and industry contract research projects. She has supervised 60 PhD students to graduation.

As the Director of the Australian Artificial Intelligence Institute (AAII) at UTS, the largest AI hub in Australia with 35 researchers and 230 PhD students in the field of AI, she has steered AAII to secure 47 ARC grants and over 110 industry projects since 2017.

Jie, who is also Associate Dean (Research Excellence) in the Faculty of Engineering and Information Technology at UTS, has driven the faculty’s culture of research excellence and impact.

Effective prediction and decision making deliver direct benefits to industry and the community by increasing economic activity and helping organisations to manage risk. Jie has applied her expertise and theoretical innovation in this field in industry collaborations across a wide range of sectors, including transportation, telecommunications, health care and education.

Her projects include partnerships with Optus, Sydney Trains, Domain Holdings, Australia Ltd, Workforce Health Assessors Transport NSW, iMove CRC, DH CRC, and 23Strands, developing and implementing machine learning modelling, intelligent recommender systems and AI-driven decision support systems.

She received the UTS Vice-Chancellor's Research Medal for Research and Teaching Integration (2010), the UTS Chancellor's Research Medal for Research Excellence (2019), the Computer Journal Wilkes Award (2018), the IEEE Transactions on Fuzzy Systems Outstanding Paper award (2019 and 2022), the Australian Most Innovative Engineer award (2019), IEEE Distinguished Lecturer, NeurIPS 2022 Paper Award, Australasian AI Distinguished Research Contribution Award (2022) and Australian NSW Premier Prize on Excellence in Engineering or Information & Communication Technology (2023).

She delivers 40 keynote speeches at international conferences. She serves as Editor-In-Chief for leading journal Knowledge-Based Systems (Elsevier) since 2010.

Dr Mohsen Naderpour is an Industry Fellow at the Australian Artificial Intelligence Institute (AAII). Before this, he was a Senior Lecturer at FEIT. Mohsen received his PhD degree from UTS, Master of Science degree in Industrial Engineering from the Iran University of Science and Technology (IUST), Tehran, Iran, and a Bachelor of Science degree in Applied Mathematics from Ferdowsi University of Mashhad, Mashhad, Iran. His research interests lie in the areas of Risk Engineering, Artificial Intelligence, Computational Intelligence, and Decision Support Systems.
In 2020, Mohsen received the University’s top honour for teaching excellence, an Individual Award, for developing risk awareness in engineering education.