Renjie Feng is a Research Fellow in Mathematics and AI at the School of Mathematics and Statistics and the Sydney Mathematical Research Institute , University of Sydney. His work bridges probability theory, statistics, and applications in machine learning, deep learning, and artificial intelligence. His research interests focus on probability theory and its applications to machine learning , random matrix theory , and statistical physics . He investigates extreme value problems, spectral properties of random matrices, and topological features of random fields over Riemannian manifolds. Recent publications highlight trends in random matrix theory (GUE, GOE, GSE), extreme gap problems , determinantal point processes , and Wiener chaos . Collaborative works with F. Götze, D. Yao, and R. Adler emphasize U-statistics , multivariate linear statistics , and random topology inspired by Poisson point process studies.
Halil Ali is a Lecturer in Data Science (Education Focused) at the School of Computing Technologies, RMIT University. His research spans privacy-preserving machine learning, blockchain technologies, and cybersecurity. Key research areas include federated learning , quantum-enhanced AI , secure biometrics , edge unlearning , and privacy in healthcare data . His recent publications focus on resilient AI systems , blockchain applications , and ethical data handling in emerging technologies. His work demonstrates expertise in integrating machine learning with blockchain security across domains like IoT, smart grids, and metaverse healthcare. He contributes to practical frameworks for zero-trust architectures , lightweight consensus protocols , and quantum-classical hybrid models .
Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.
Professor Ernest Foo is a distinguished academic at Griffith University's School of Information and Communication Technology, specializing in cybersecurity with a focus on industrial control systems and cryptographic protocols. With over 15 years of experience in computer networking, he has established himself as a leading expert in SCADA security and smart grid cybersecurity. His research has significant practical applications in critical infrastructure protection, and he has developed hands-on security training programs that have trained professionals from major Australian utilities and government agencies. Professor Foo's educational background includes: Bachelor of Engineering with Honours in Electronic and Computer Engineering from University of Queensland Doctor of Philosophy from Queensland University of Technology Professor Foo's research interests center around secure cryptographic protocols with specific applications in industrial control system security and cyber physical systems. His work spans SCADA security, smart grid protection, wireless sensor network security, and post-quantum cryptography. He has made significant contributions to understanding vulnerabilities in industrial protocols like Modbus and DNP3, and has pioneered the application of process mining and data mining techniques for attack detection in critical infrastructure systems. His research bridges theoretical security concepts with practical implementations in real-world industrial environments. Professor Foo's recent publications demonstrate a clear trajectory toward increasingly sophisticated security frameworks for critical infrastructure. His work shows a progression from foundational SCADA security research to advanced applications of artificial intelligence, machine learning, and formal methods in cybersecurity. A notable trend is the integration of zero trust principles with industrial control systems, alongside growing emphasis on post-quantum cryptographic solutions. His publications span high-impact journals and conferences in cybersecurity, with increasing focus on anomaly detection in cyber-physical systems and the application of graph-based machine learning techniques to network security challenges. Professor Foo's notable scientific achievements include: Best paper award at the 2nd International Cyber Resilience Conference for "Gap analysis of Intrusion Detection in Smart Grids" Professor Foo has secured significant research funding including an ARC Linkage grant with Powerlink Queensland focused on cyber security for electricity sub-stations. He currently leads multiple research projects including Westpac Micro-Credentials in Financial Crime Investigation, Digital Banking Micro-credentials with ANZ, and research on quantum-safe cryptography. As a dedicated educator, he serves as Program Director for multiple cybersecurity programs including the Master of Cyber Security, and has supervised numerous doctoral and masters students. His Cyber Security: Industrial Control System course, conducted annually from 2013-2018, featured innovative hands-on training with real-world participants from major Australian utilities. Professor Foo has been instrumental in establishing the SCADA security research laboratory with multiple vendor system miniatures running industrial PLCs. His work bridges theoretical security concepts with practical implementations, making significant contributions to the security of industrial control systems and critical infrastructure worldwide.
Dr. Owen Dillon is a Research Fellow in the Discipline of Medical Imaging Sciences at the University of Sydney's Faculty of Medicine and Health. He holds affiliations with the ACRF Image X Institute and the Dodd-Walls Centre for Photonic and Quantum Technologies. His work focuses on advanced imaging techniques for medical applications, particularly computed tomography (CT) and motion compensation in radiation therapy. He completed his PhD in Mathematics at the University of Auckland, specializing in probabilistic compression algorithms for inverse problems. Education: B.Sc. Physics & Applied Mathematics (2013, University of Auckland), First Class Honours in Mathematics (2015), PhD Mathematics (2018). Research interests include inverse problems, Bayesian statistics, CT image reconstruction, and real-time imaging systems. Current projects involve optimizing CT acquisition geometries, motion-compensated 4D imaging, and anatomical motion estimation. His contributions have led to clinical trials reducing radiation dose and scan times. He advises two PhD students and collaborates on grants like the Quantum CT project. Grants: 'Quantum CT for Cancer Diagnosis' (2024), 'Functional Imaging in Lung Cancer' (2024). His work bridges mathematical theory with clinical applications in oncology and interventional radiology.
Professor Hala Zreiqat AM is a leading biomedical engineer at The University of Sydney , serving as the Director of the ARC Training Centre for Innovative BioEngineering . A Fellow of all major Australian academies (AAS, ATSE, FAHMS, FRSN), she develops 3D printed bioceramics for bone regeneration while championing diversity through initiatives like the IDEAL Society and BIOTech Futures mentorship program. Her work bridges academia, clinical practice, and industry in musculoskeletal research . Research Focus: Her lab creates synthetic bone scaffolds that mimic natural bone architecture, strength, and porosity, enabling non-rejected bone regeneration via patient-matched implants. Key applications include orthopaedic, dental, and maxillofacial repair , with over $18M in competitive funding and multiple patents. Current projects explore AI-driven scaffold performance prediction and anti-senescence strategies for aging-related bone loss. Scientific Trends: Recent publications highlight 3D printed nanovoxelated ceramics , antisenescence biomaterials , and multifunctional theranostic platforms . Her team integrates machine learning for scaffold design, atom probe tomography for interface analysis, and two-photon imaging for cellular monitoring in 3D environments. 2021-2022 Fulbright Senior Scholar 2018 NSW Premier's Woman of the Year 2019 Eureka Prize for Innovative Use of Technology Fellow of Australian Academy of Science (2021) Over $18M in research funding Teaching & Leadership: She designed core courses like Tissue Engineering and Nanomaterials in Medicine , mentoring 158 students in 2020 alone. As Chair of CAAR (2020-2023), she strengthens Australia-Arab collaborations. Her lab trains early-career researchers , with alumni now in academia and industry.
Dist. Professor Rachel Caruso is a Professor at RMIT University's School of Science, specializing in nanomaterials, energy storage systems, and photocatalysis. Her research focuses on advancing materials science through innovations in titanium-based nanomaterials, perovskite solar cells, and sustainable hydrogen production. She is actively involved in research supervision, offering guidance on topics like carbon capture, electrochemical CO₂ reduction, and antimicrobial surface engineering. Her work integrates machine learning for material discovery and emphasizes interdisciplinary applications in environmental and biomedical fields. Research Interests: Macromolecular Chemistry, Nanotechnology, Energy Materials, Photocatalysis, and Biomedical Applications. Supervision Projects: Includes pioneering studies on direct air capture of CO₂, graphene-based photocatalytic films, and perovskite materials for biomedical uses. Professor Caruso collaborates extensively on projects addressing global challenges such as renewable energy and antimicrobial resistance. Her contributions span over 200 publications, with notable advancements in titanium suboxide synthesis and electrocatalyst design.
Sushmita Ruj is an Associate Professor in the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney. She serves as the Faculty of Engineering Lead for the UNSW Institute for Cybersecurity (IfCyber) and as the Taste of Research (ToR) Coordinator within the School of Computer Science and Engineering. Her academic journey includes previous positions as a Senior Research Scientist at CSIRO's Data61 (2019-2022), Associate Professor at the Indian Statistical Institute, Kolkata, and Assistant Professor at the Indian Institute of Technology (IIT), Indore. Dr. Ruj's primary research interests focus on applied cryptography, post-quantum cryptography, cybersecurity, blockchains, and data privacy. She designs practical, efficient, and provably secure protocols for real-life applications, with particular emphasis on critical infrastructure including smart grids, cloud computing, ad hoc networks, and data sharing frameworks. As quantum technology advances, her work increasingly focuses on developing quantum-safe algorithms to ensure a more secure Internet infrastructure. Her research spans multiple domains including cryptographic key management, proofs of storage, verifiable computation, vector commitments, and privacy-enhancing technologies for cloud and IoT environments. Her recent publications demonstrate a strong trend toward post-quantum cryptography solutions, with particular emphasis on blockchain applications, DNS security, and privacy-preserving protocols for industrial IoT. The research shows increasing focus on practical implementations of theoretical cryptographic concepts, with applications across multiple sectors including finance, healthcare, and critical infrastructure. Her work bridges the gap between theoretical cryptography and real-world security challenges, with growing emphasis on the transition from classical to quantum-resistant systems. Best Paper Award at ACISP 2024 JNCA Best Survey Award (2023) NSW Innovation Award (iAward) Merit Winner (2022) Women in Science Award from CSIRO (2020) ACM Senior Member (2016) IEEE Senior Member (2015) Samsung GRO award (2014) Dr. Ruj has successfully mentored numerous PhD and Master's students, with many of her former students now holding academic positions at institutions like IIT Indore, TU Wien, and CISPA Helmholtz Center. She has secured significant competitive funding including multiple Australian Research Council (ARC) grants, Samsung GRO Award, NetApp Faculty Fellowship, Cisco Academic Grant, and IBM Research grant. Her current research portfolio includes projects on blockchain-based quantum-safe digital medical passports, embedding trust in digital IDs, and resilience of supply chain unstructured data. As Faculty of Engineering Lead for IfCyber, Dr. Ruj plays a key role in UNSW's cybersecurity research initiatives. She has served on editorial boards for prestigious journals including IEEE Transactions on Information Forensics and Security and has held leadership positions in major conferences such as ACISP 2021 and Indocrypt 2020. She was also a member of the working group on "Blockchain For Cybersecurity" for the National Blockchain Roadmap of Australia and the first Blockchain Working group set up by the Reserve Bank of India.
Professor Vinayak Dixit serves as the IAG Chair of Risk in Smart Cities and Director of the Research Centre for Integrated Transport Innovation (rCITI) at the University of New South Wales (UNSW), within the School of Civil and Environmental Engineering. With a distinguished career spanning academia and research leadership, Professor Dixit has established himself as a leading expert in transportation risk analysis and smart city infrastructure. Professor Dixit's research focuses on studying risk in transportation infrastructure systems, with particular emphasis on highway safety, travel time uncertainty, and resilience against natural and man-made disasters. His work integrates cutting-edge approaches including quantum computing applications for transportation network optimization, analysis of connected and automated vehicles, and development of models for transportation resilience. His research interests span multiple disciplines, bridging civil engineering, transportation science, risk analysis, and computational methods. Professor Dixit has secured significant research funding from prestigious organizations including the United States National Science Foundation, Federal Highway Administration, and the Strategic Highway Research Program of the Transportation Research Board. His leadership extends to directing the Research Centre for Integrated Transport Innovation (rCITI), where he oversees a comprehensive research program addressing critical transportation challenges in smart cities through multiple focus areas including Connected Mobility Services, Deep Data and Digitization, Engineering Smart Cities & Logistics, Human-Centred And Automated Systems Design, and Integrated Infrastructure Strategic Planning. Professor Dixit previously served as the Associate Director of Research for the Gulf Coast Centre for Evacuation and Transportation Resiliency at Louisiana State University, where he founded the Driving Simulator Laboratory in collaboration with other faculty members. This demonstrates his longstanding commitment to innovative research infrastructure development and interdisciplinary collaboration across engineering, economics, computer science, and urban planning to address complex transportation challenges in the 21st century.
Christopher Ferrie is an Associate Professor at the University of Technology Sydney (UTS), where he is affiliated with the Faculty of Engineering and Information Technology and the Centre for Quantum Software and Information (QSI). His academic career spans quantum information science, machine learning, and scientific education, with a strong emphasis on both theoretical research and public engagement through science communication. Full-time faculty member at UTS Active researcher in quantum information science Director of the Centre for Quantum Software and Information Author of numerous scientific publications and popular science books Dr. Ferrie earned his PhD in Applied Mathematics from the Institute for Quantum Computing and University of Waterloo in Canada in 2012. His doctoral work focused on quantum information and laid the foundation for his subsequent research career in quantum computing and related fields. Dr. Ferrie's research interests span several interconnected domains within quantum information science. His primary focus is on quantum estimation and control, with particular emphasis on applying machine learning techniques to solve statistical problems in quantum information science. He investigates how quantum systems can be characterized, controlled, and optimized for practical applications. His work bridges theoretical quantum physics with practical implementations, exploring how quantum phenomena can be harnessed for computational advantage. Recent research directions include quantum machine learning, quantum neural networks, and quantum optimization algorithms, with applications ranging from quantum state tomography to solving combinatorial optimization problems. Analysis of Dr. Ferrie's recent publications reveals a strong focus on practical quantum computing challenges. His work consistently addresses the intersection of quantum information theory and machine learning, with particular emphasis on making quantum algorithms more efficient, interpretable, and robust against noise. A significant portion of his recent research explores variational quantum algorithms and their optimization, reflecting the current priorities in near-term quantum computing. His publications also demonstrate growing interest in quantum machine learning applications and the development of techniques for quantum error mitigation and characterization. Dr. Ferrie has secured multiple research grants supporting his work in quantum computing and related fields. His funded projects span quantum control, quantum probability, quantum machine learning, and statistical decision theory, reflecting the breadth of his research program. While specific major awards aren't detailed in the available information, his sustained funding and publication record indicate significant recognition within the quantum information science community. Dr. Ferrie is actively involved in research supervision and teaching, with current funding supporting multiple PhD students and postdoctoral researchers. His teaching responsibilities include courses on quantum computing, where he introduces students to the fundamentals of quantum information processing. His research group at the Centre for Quantum Software and Information focuses on developing novel quantum algorithms and exploring the practical implementation challenges of quantum computing. The Centre for Quantum Software and Information at UTS serves as the primary research environment for Dr. Ferrie's work. This center brings together researchers working on various aspects of quantum computing, from hardware development to algorithm design and applications. Dr. Ferrie's team within the center focuses specifically on quantum software development, quantum algorithm design, and the application of machine learning techniques to quantum information problems. The collaborative environment enables interdisciplinary research that bridges theoretical quantum physics with practical computing applications.
Kavan Modi is a Professor at the School of Physics and Astronomy, Monash University. His research focuses on quantum information theory applied to dynamics, metrology, computation, thermodynamics, and relativity. He leads the Monash Quantum Information Science (MonQIS) group and serves as Director of the Centre for Quantum Technology at Transport for NSW (2022–2024). Education: B.Sc. Engineering Physics (Embry-Riddle Aeronautical University, 2001), M.A. Physics (University of Texas at Austin, 2004), Ph.D. Physics (University of Texas at Austin, 2008). Postdoctoral positions included the Centre for Quantum Technologies (Singapore, 2008–2011) and Clarendon Lab, Oxford (2011–2013). Joined Monash in 2014. Research interests center on quantum dynamics, non-Markovian processes, and their applications in quantum computing and information science. Projects include developing error correction codes, quantum algorithms for network analysis, and mitigating correlated noise in quantum systems. He has authored over 111 publications, with recent work emphasizing non-Markovian characterization, quantum process tomography, and topology-based quantum algorithms. Awards and grants include leadership in multiple Australian Research Council projects. Advising/Grants: Primary Chief Investigator in projects like 'Quantum Software Platform' (2023–2026) and 'Mitigating Correlated Noise in Quantum Machines' (2020–2021). Supervises graduate students and collaborates globally on quantum information science. Labs/Teams: MonQIS group focuses on foundational and applied quantum research, integrating theory and experimental collaborations.
Commonwealth Scientific and Industrial Research OrganisationAustralia
Didar Zowghi is a Senior Principal Research Scientist and Science Team Leader at CSIRO Data61, Australia's national science agency. His work focuses on advancing ethical AI systems, data quality frameworks, and requirements engineering methodologies. He leads research initiatives addressing challenges in AI governance, diversity/inclusion in technology, and human-centered AI development. Research interests include AI ethics, data completeness in healthcare systems, and the application of machine learning in requirements engineering. His contributions span theoretical frameworks for responsible AI patterns, empirical studies on user perceptions of AI tools like M365 Copilot, and analysis of AI's role in global diplomatic practices. Zowghi has published extensively on topics ranging from blockchain in supply chains to pedagogical innovations in software engineering education. His work often bridges technical systems and societal impacts, emphasizing real-world implementation challenges through collaborative industry-academia projects. Notable outputs include the Responsible AI Pattern Catalogue and studies examining barriers to data quality in IoT platforms. He has pioneered frameworks linking innovation initiatives to occupational skill requirements and developed tools like Elica for dynamic requirements knowledge extraction in agile teams.
Professor Ibrahim Khalil is a faculty member in the School of Computing Technologies at RMIT University, Melbourne, Australia. He holds a PhD in Computer Science from the University of Bern (2003) and has extensive industry experience in Silicon Valley focusing on secure network protocols. His research spans Security, Privacy, Federated Learning, Blockchain, Quantum Computing, and Distributed Systems. He leads high-impact projects funded by ARC grants (DP250100582, DP220100215, etc.) and international initiatives like the EU’s SELFY project. His work addresses challenges in secure AI data analytics, privacy-preserving systems, and critical infrastructure protection. Khalil supervises PhD/Masters students on topics ranging from federated learning security to quantum-enhanced machine learning. Education: PhD in Computer Science (University of Bern, 2003); prior roles at EPFL, Osaka University, and industry tech hubs. Research Interests: Privacy-Preserving Technologies Blockchain Applications in Healthcare and Supply Chains Quantum Computing for Machine Learning Secure Edge Computing and Federated Learning IoT Security and Critical Infrastructure Protection Grants & Collaborations: Over 10 major grants since 2017, including ARC Discovery/Linkage Projects and international partnerships (QNRF, EU). Notable projects include Privacy-Aware Digital Twins for Critical Infrastructure and Federated Learning frameworks for GenAI models. Advising & Labs: Active supervisor of 25+ research projects since 2013, focusing on anomaly detection, secure data analytics, and blockchain-based systems. Collaborates with industry partners on defense and healthcare tech.
Associate Professor Tongliang Liu is affiliated with the School of Computer Science at the University of Sydney, serving as Director of the Sydney Artificial Intelligence Centre and Trustworthy Machine Learning Lab. He holds a BEng and PhD, and is an ARC Future Fellow. His research focuses on trustworthy machine learning, including adversarial defense, causal representation learning, and robust AI systems. He has authored over 200 papers in top venues like NeurIPS and ICML, and serves as co-Editor-in-Chief of Neural Networks. Research Interests: Developing reliable algorithms for machine learning, emphasizing generalizability and safety. Specific areas include learning with noisy labels, causal inference, and foundational model ethics. He aims to bridge theoretical guarantees and practical applications in computer vision and data mining. Awards: 2024 CORE Award, 2023 IEEE AI's 10 to Watch, 2022 ARC Future Fellowship. Notable recognitions include Eureka Prize shortlist and DECRA. Advising & Grants: Supervises 12 PhD/Master’s students on topics like trustworthy AI, causal discovery, and quantum machine learning. Leads grants on robust learning and AI safety. Labs: Sydney AI Centre and Trustworthy Machine Learning Lab.
Professor Brant Gibson is a Deputy Dean of Research and Innovation and holds the rank of Professor in the School of Science at RMIT University. His research focuses on quantum technologies, particularly diamond-based systems including nitrogen-vacancy (NV) centers, fluorescent nanoprobes, and hybrid materials for sensing applications. He leads projects in quantum magnetometry, photonics, and biomedical imaging, with an emphasis on translating lab-based innovations into practical devices for fields like medical diagnostics and environmental monitoring. Brant’s work spans condensed matter physics, nanotechnology, and optical engineering, with notable contributions to diamond-doped optical fibers, quantum sensor development, and the application of nanodiamonds in biophotonics. His research integrates experimental physics with computational modeling to optimize material properties and sensor performance. He is actively involved in student supervision, offering guidance for Masters and PhD candidates in quantum engineering, materials science, and interdisciplinary applications. Current projects include quantum tensor gradiometry for navigation, bioimaging with near-infrared emitters, and silk-diamond composites for wound monitoring. Brant’s academic contributions are further reflected in over 150 peer-reviewed publications and collaborations across academia and industry. His work bridges fundamental research with real-world applications, emphasizing Australia’s role in global quantum technology advancements.
