Dr. Stewart Worrall is a Senior Research Fellow at the Australian Centre for Field Robotics (ACFR) within the University of Sydney. His research focuses on autonomous systems, robotics, and intelligent transportation systems, particularly in the areas of autonomous vehicle perception, human-robot interaction, and sensor fusion. He has contributed to numerous high-impact publications on topics such as edge case testing for autonomous vehicles, collaborative perception frameworks, and context-aware human-robot interaction design. His work integrates robotics hardware, computer vision, and machine learning to address challenges in autonomous driving, crowd dynamics, and urban mobility scenarios. Current research students under his supervision explore topics ranging from light field imaging for autonomous driving to human-machine interfaces for vehicles. Worrall has pioneered datasets like the University of Sydney Campus Dataset and the ACFR Five Roundabouts Dataset, which are critical for evaluating autonomous systems. His contributions span academic conferences (e.g., IEEE IV, ICRA) and journals, emphasizing both technical innovation and societal impacts of autonomous technologies. Key labs/teams: Core member of the ACFR, collaborating across disciplines including robotics, computer science, and urban design.
Professor George Siemens is a leading academic in the field of learning analytics and AI-driven education, serving as Professor and Director of the Centre for Change and Complexity in Learning at UniSA Education Futures, University of South Australia. His work focuses on advancing educational practices through data analytics, artificial intelligence, and understanding online learning dynamics. His research spans MOOCs, social and emotional learning analytics, and the ethical integration of AI in education. Notable contributions include the development of frameworks like the MOOC Replication Framework (MORF) and the DAIR infrastructure for educational AI research. Key publications include studies on student agency in AI environments, practicum effectiveness in teacher education, and synthetic data fairness in learning analytics. He collaborates internationally, with affiliations previously including the University of Texas Arlington. As a Research Degree Supervisor, he guides students in transformative educational technology research. His work emphasizes actionable intelligence for educators and scalable solutions for lifelong learning in the digital age.
Rajendra Acharya is a Professor (Artificial Intelligence in Health) at the University of Southern Queensland's School of Mathematics, Physics and Computing. He holds qualifications including BEng, MTech, two PhDs, and a DSc. His research focuses on AI applications in healthcare, pattern recognition, and medical diagnostics, with notable contributions to EEG analysis, deep learning, and disease detection. Awards include multiple Research.com Leader Awards in Computer Science for Australia and Singapore (2022–2025). His work spans over 650 publications, with high-impact studies on automated disease diagnosis via AI, including COVID-19 detection using X-rays and EEG-based seizure detection. His research interests integrate machine learning, signal processing, and healthcare technologies. He collaborates internationally and advises on AI-driven health solutions. No student list provided; however, his extensive supervision is implied through his research output.
Professor Sara Dolnicar is an ARC Australian Laureate Fellow at the School of Business, Faculty of Business, Economics and Law, University of Queensland. With degrees in psychology and business administration from Wirtschaftsuniversität Wien, she specializes in market segmentation, sustainable tourism, social marketing, and environmental behavior. Born in Slovenia, raised in Austria, now based in Australia Over 300 refereed publications and 16 ARC grants Recipient of TTRA Distinguished Researcher Award (2017) and Slovenian Ambassador of Science (2016) Her research improves market segmentation methodology, addresses environmental sustainability in tourism, and develops behavioral interventions for eco-friendly tourist practices. She pioneered perceptions-based market segmentation and introduced bi-clustering techniques for improved data analysis. Recent publications focus on towel reuse interventions, buffet waste reduction, and smart sensor systems for hotel sustainability. She supervises PhD students in environmental behavior, tourism carbon emissions, and IoT applications for sustainability. Current grants include 'Mechanisms of Behaviour Change Theory' (2025-2028) and 'Reducing plate waste in hotels' (2021-2025). Key research areas: Sustainable tourism, market segmentation, social marketing Supervised over 15 PhD students in environmental behavior and disability employment Collaborates with industry partners like Energy Queensland and disability organizations
Hsei Di Law is a Research Fellow at the National Centre for Epidemiology and Population Health (NCEPH), part of the Australian National University (ANU). She is concurrently pursuing an MSc in Computational Data Analytics at the Georgia Institute of Technology . Her work focuses on data linkage, machine learning, and epidemiological study design using whole-of-population linked datasets. Research Affiliations National Centre for Epidemiology and Population Health (ANU) Centre of Epidemiology for Policy and Practice Linked Data for Better Health group Health Experience and Health Services Research collaborator Research Interests : Hsei Di Law's work centers on large-scale linked administrative datasets and longitudinal data analysis , with applications in environmental contamination (PFAS, asbestos), healthcare economics (out-of-pocket costs), and epidemiology of diseases. She specializes in integrating machine learning techniques with epidemiological study design to address policy-relevant questions. Publication Trends : Her recent publications (2025–2023) analyze healthcare affordability in Australia, PFAS environmental health impacts , and data linkage methodologies . Over 2022–2015, she studied cardiovascular risk under-treatment , COVID-19 healthcare outcomes , and immune system disorders using mouse models. Projects Led : She is involved in multiple projects, including the ACT Asbestos Health Study II , PFAS Health Study , and Whole-of-population linked data project . These projects examine the health effects of asbestos and PFAS contamination , healthcare utilization, and epidemiological modeling . Contact : Email: hsei-di.law@anu.edu.au Phone: +61 2 6125 0547 Location: Building 62A, Room 2.56, ANU
Dr. Wade Smith is a Senior Lecturer within the School of Mechanical and Manufacturing Engineering at the University of New South Wales. He is an active member of the WAVES research group (Wear, Aeroacoustics and Vibration in Engineering Systems) and conducts his research in the Tribology and Machine Condition Monitoring laboratory. His primary research interests include vibration-based diagnostics of rotating machinery, prognostics of rotating machinery, gear wear monitoring and prediction, simulation and modeling of rotating machines for diagnostic applications, and signal processing of machine vibration signatures using cyclostationarity. His work has significant applications in industrial machinery health monitoring and predictive maintenance systems. Dr. Smith's recent publications demonstrate a consistent focus on advanced diagnostic techniques for rotating machinery, with particular emphasis on gear systems and bearings. His research integrates traditional mechanical engineering principles with modern signal processing and machine learning approaches to develop more effective condition monitoring solutions. He actively supervises PhD and Masters students on projects related to gear diagnostics, wear monitoring, and vibration analysis. His current research projects include gear diagnostics in planetary gearboxes using internal sensors, gear wear monitoring and prediction, sliding contact-induced vibration studies, and transmission-error-based gear diagnostics. Dr. Smith's laboratory is equipped with specialized facilities including gearbox test rigs (both planetary and parallel configurations), a rolling element bearing test rig, an engine test rig, friction rig, tribometer, high-quality microscope, and extensive instrumentation for vibration analysis. His research has attracted collaborations with institutions including Queensland University of Technology, SpectraQuest (USA), Weir Minerals, University of Technology Sydney, RWTH Aachen University (Germany), and Safran.
Steven Meikle is a Professor of Medical Imaging Physics and Head of the Imaging Physics Laboratory at the Brain and Mind Centre, University of Sydney. He also serves as Deputy Director (Preclinical) of Sydney Imaging and Deputy Director of the National Imaging Facility's Sydney node. His expertise spans advanced imaging technologies, with a focus on PET/SPECT instrumentation and molecular imaging. He holds a B.App.Sc.(Hons) from the University of Technology Sydney and a PhD from the University of New South Wales. Research focuses include developing novel PET systems like Open-field PET (for freely moving rodents) and Total Body PET, which enhance imaging sensitivity and enable real-time behavioral studies alongside brain function analysis. Collaborations include Tsinghua University (China) and UC Davis (USA). He leads projects on motion correction, quantitative imaging, and AI-driven analysis. Key achievements include over 180 peer-reviewed publications, editorial roles in Physics in Medicine and Biology , and leadership in professional societies. Awards include IEEE Senior Membership and Australian Institute of Physics Fellowship. Current student projects explore Total Body PET applications, motion correction, and radiopharmaceutical evaluation. Teaching roles include medical physics courses in diagnostic radiography and medical physics programs. He advises on imaging ethics, facility implementation, and translational research bridging basic science and clinical applications.
Professor Paul A. Raschky is affiliated with the Department of Economics at Monash University , where he also directs the SoDa Labs and co-founded the KASPR Datahaus PTY LTD and IP Observatory . His academic focus spans Political Economy , Environmental Economics , Insurance Economics , and Development Economics , with a specialization in natural hazards and data science applications. Education: PhD (University of Innsbruck, 2008), with a postdoctoral research visit at the Wharton School . Research: Explores intersections of disaster risk , governance , and economic development , leveraging machine learning and big data . Recent Publications: Focus on generative AI productivity , ethnic favoritism , disaster insurance , and conflict economics . Awards: Recipient of the ABDC Award for Innovation and Excellence in Research (2022) and Dean’s Excellence in Research Award (2016) . Projects: Leads initiatives on internet suppression in Myanmar , news media availability , and AI-driven policy insights in South-East Asia and the Pacific. Future Work: Will be on sabbatical in 2025 , likely expanding on AI applications and disaster resilience .
Dr. Liyi Zhou is a Lecturer in the School of Computer Science at the University of Sydney, specializing in systems security, blockchain, and AI. His research focuses on developing automated and adaptive security tools using machine learning and reinforcement learning. He co-founded D23E.ch, a platform addressing blockchain security and privacy challenges. Research interests include AI-driven vulnerability detection, large security models, real-time intrusion prevention, advanced program analysis (fuzzing/symbolic execution), and privacy-preserving systems. He actively recruits PhD students for projects advancing AI in cybersecurity. Notable achievements include pioneering 'sandwich attacks' discovery in DeFi protocols, contributing to Ethereum Foundation grants, and receiving bug bounties from Flashbots and Ethereum Foundation for vulnerability disclosures. His work has been published in venues like IEEE S&P, USENIX Security, and SIGMETRICS. Teaching includes the course INFO2222. He seeks collaborations and funding to bridge academic research with real-world industry problems, emphasizing practical impact.
Dr. Thanh Nho Do is a Scientia Senior Lecturer at the Graduate School of Biomedical Engineering (GSBmE), UNSW Sydney, and Director of the UNSW Medical Robotics Lab. He holds a PhD in Mechanical Engineering (Surgical Robotics) from Nanyang Technological University (NTU), Singapore, and a B.Eng. in Manufacturing Engineering from Ho Chi Minh City University of Technology, Vietnam. His research focuses on soft robotics, wearable technologies, and biomedical devices, including flexible surgical systems, soft actuators, and haptic interfaces. Education PhD in Mechanical Engineering (Surgical Robotics), NTU Singapore, 2015 B.Eng. in Manufacturing Engineering, Ho Chi Minh City University of Technology, Vietnam Research Interests Soft robotics for medical applications (e.g., NOTES systems, wearable haptics) Functional materials for biomedical devices Cardiovascular engineering and assistive devices Advanced control algorithms for medical robotics Key Contributions His work spans bioprinting, motor-free robotic systems, and soft wearable technologies. Recent studies include self-deploying cardiac compression devices and bioinspired artificial muscles. Awards 2025: CINSW Career Development Fellow 2024: NSW Young Tall Poppy Science Award 2023: Best Poster Awards at EMBC and ICRA Grants & Funding Includes NHMRC Ideas Grant (Lead CI), Cancer Institute NSW Fellowship, and UNSW Scientia Grant. Active projects address cardiovascular interventions and wearable robotics. Labs & Teams Leads the UNSW Medical Robotics Lab, collaborating on devices like soft robotic catheters and textile-driven exosuits.
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.
Professor Anna Marie Munster is a distinguished academic and artist at UNSW Art and Design (College of Fine Arts), where she has held a full-time tenured position since 2001. Her work bridges theoretical scholarship with creative practice, establishing her as a leading figure in digital media art and critical theory. University: University of New South Wales School: College of Fine Arts (UNSW Art and Design) Position: Professor Qualifications: PhD from UNSW (2002), BA (Class 1 Hons with University Medal) from University of Sydney (1984) Professor Munster's research explores the intersections of art, technology, and philosophy, with particular focus on statistical visuality, radical empiricism, the politics and aesthetics of machine learning, and more-than-human perception. Her work integrates process philosophy with contemporary media practices, examining how time, movement, and sonicity shape our experience of digital environments. She approaches these topics through both theoretical scholarship and creative practice, often collaborating with artist Michele Barker on multi-channel audiovisual installations. Her publications reveal a consistent trajectory exploring the relationship between embodiment and digital media, with recent work focusing on AI and machine learning in cultural contexts. Munster's creative output demonstrates sophisticated integration of technical innovation with philosophical inquiry, particularly in how her installations challenge conventional perceptions of time and space. ARC, UNSW Student Council Award for Excellence in Postgraduate Supervision (2017) Dean's Award for Excellence in Postgraduate Supervision (2015) Highly commended for Materializing New Media at Prix Ars Electronica (2008) Winner of National Digital Art Awards 'The Harries' (2006) University Press of New England Publishing Award (2005) Professor Munster has completed over 20 PhD and Masters supervisions at UNSW and been involved in another 10 secondary supervisions. She currently supervises five PhD candidates working on diverse topics including coding materialities, critical bio-textiles, schizophrenia and XR technologies, sound and resonance, and affective drawing. Her ARC-funded research projects demonstrate sustained commitment to interdisciplinary work, particularly at the intersection of art, science, and technology. Her creative practice with Michele Barker has resulted in numerous commissioned installations exploring perception, embodiment, and the relationship between humans and technology.
Professor Stuart James Khan is an Adjunct Professor in the School of Civil & Environmental Engineering at the University of New South Wales (UNSW). He previously served as Director of the Australian Graduate School of Engineering (AGSE). His research focuses on sustainable urban water management, water treatment processes, and chemical contaminant analysis. Khan holds a PhD in Environmental Engineering from UNSW (2003) and a BSc (Hons 1 in Organic Chemistry) from the University of Sydney (1995). His research interests include water recycling, desalination, disinfection byproduct formation, and the safe management of chemical contaminants. He has advised over 15 PhD students on topics ranging from advanced treatment technologies to risk assessment frameworks. Khan has published extensively, with over 200 journal articles and 18 book chapters, emphasizing interdisciplinary approaches to water quality challenges. Key contributions include advancing membrane bioreactor performance, optimizing water recycling systems, and assessing risks associated with emerging contaminants. His work bridges fundamental science and practical engineering solutions, addressing global water security challenges. Current projects explore wastewater-based epidemiology, PFAS remediation, and climate-resilient water infrastructure. Khan collaborates internationally on water reuse strategies and has contributed to policy frameworks for safe water management. His teaching spans courses like Water & Wastewater Treatment and Environmental Risk Analysis, reflecting his commitment to educating future engineers in sustainable practices.
Amin Barari is an Adjunct Professor at the School of Engineering, RMIT University, Australia. His research focuses on geotechnical and offshore engineering, particularly in foundation systems for offshore wind turbines, soil-structure interaction, and seismic liquefaction mitigation. He has extensive experience in experimental and numerical analysis of pile foundations, bucket foundations, and caisson structures. His work integrates advanced computational methods (e.g., machine learning, finite element modeling) to predict foundation behavior under extreme conditions. Research interests include: offshore wind energy foundations, soil liquefaction, cyclic stability diagrams, and probabilistic hazard assessment frameworks. He has supervised multiple PhD/Masters projects on topics like resilient foundations in calcareous deposits and pile foundation dynamics in expansive soils. His publications span over 147 research outputs, emphasizing geotechnical challenges in coastal and offshore environments. Dr. Barari collaborates with international institutions and has expertise in experimental testing (e.g., large-scale load testing, centrifuge modeling) and advanced AI-driven frameworks for geohazard prediction. His work contributes to sustainable infrastructure design and risk mitigation strategies for renewable energy systems.
