Dr. Melissa Humphries Senior Lecturer in the School of Computer and Mathematical Sciences at the University of Adelaide. Specializes in statistical methodologies applied to forensic science, health analytics, and AI integration. Advocates for end-user collaboration in research and equity in STEM. Affiliated with the Faculty of Sciences, Engineering and Technology. Research Interests: Development of statistical tools for decision-making transparency, forensic DNA analysis, explainable AI, and interdisciplinary applications in health and criminal justice. Active in bridging statistical models with physical systems and machine learning. Key Contributions: Published work on GAN-based DNA signal simulation, outlier importance in forensics, and methadone toxicity trends. Specializes in integrating advanced statistical techniques with real-world challenges in criminal justice and healthcare. Labs/Teams: Collaborates with forensic science groups and AI research teams at Adelaide. Engaged in multi-disciplinary projects combining statistics with engineering and medical applications.
Dr. Vesna Popovic is a Professor at the School of Design, Queensland University of Technology (QUT), specializing in human-centered design, intuitive interaction, and ergonomics. Her research focuses on applying design principles to improve user experiences in complex systems such as airports, healthcare facilities, and assistive technologies. Her work bridges theoretical frameworks with practical applications, emphasizing empirical studies on expertise development, aging populations, and accessibility. Notable contributions include pioneering studies on airport passenger safety, security screening processes, and the design of inclusive interfaces for diverse user groups. Dr. Popovic has led numerous industry-academia collaborations, including projects with Brisbane International Airport and telehealth initiatives. She has authored over 150 peer-reviewed publications and edited volumes, contributing to both academic and applied design knowledge. Her research methodologies integrate observational studies, verbal protocol analysis, and participatory design approaches. Key areas of focus include intuitive interaction theory, passenger experience modeling, and the application of ergonomics principles in product design.
Sander J.J. Leemans is an Associate Professor at Queensland University of Technology's Faculty of Science, School of Information Systems, specializing in process mining and business process management. With over 50 publications spanning from 2013 to 2023, his work has significantly contributed to advancing process mining methodologies and applications across various domains. Leemans' research interests focus on process discovery, conformance checking, stochastic process modeling, data quality in process mining, and the integration of process mining with exogenous data sources. His work bridges theoretical foundations with practical applications, addressing both technical challenges in process mining algorithms and organizational considerations for successful implementation in business settings. His publication record reveals a clear progression from foundational process mining techniques to more sophisticated approaches incorporating stochastic modeling, data quality considerations, and contextual factors. The research demonstrates increasing focus on practical implementation challenges, organizational adoption barriers, and the integration of process mining with broader business analytics initiatives. Leemans has made significant contributions to conformance checking methodologies, particularly through entropy-based and stochastic approaches. Leemans has supervised several researchers including Shiva Shabaninejad, Kanika Goel, Adam Burke, and Rehan Syed, indicating an active role in mentoring the next generation of process mining researchers. His collaborative work spans multiple institutions and demonstrates strong connections with leading researchers in the field such as Wil van der Aalst and Moe Wynn. His research group at Queensland University of Technology appears to focus on advancing process mining techniques while addressing practical implementation challenges in organizational settings. The work spans both theoretical algorithm development and practical applications across various industries and domains, including higher education as evidenced by his research on PhD student journeys.
Dr Kevin Witzenberger is a Research Fellow at Queensland University of Technology (QUT) in the GenAI Lab, affiliated with the ARC Centre of Excellence for Automated Decision-Making and Society. He holds a PhD from the University of New South Wales (2023) and a Master's from Lund University. His research focuses on AI ethics, education technology, and sociotechnical systems, examining the intersection of human-AI interaction, governance, and digital disruption. Dr Witzenberger co-leads discussions on generative AI implications for society and has contributed to policy submissions on AI regulation. His work spans topics like AI in education governance, platform pedagogies, and technical democracy. Notable outputs include 'Automating Education' (2023) and 'GenAI Arcade' (2025), exploring AI's role in education systems and public understanding. He collaborates widely, including with institutions like the QUT Digital Media Research Centre (DMRC). Dr Witzenberger emphasizes technical democracy, advocating for equitable AI governance. His research bridges critical theory with practical applications, addressing algorithmic bias, EdTech assessment tools, and regulatory frameworks for emerging technologies.
Dr. Amir Allahvirdizadeh is a Lecturer at the School of Earth and Planetary Sciences (EPS) at Curtin University, part of the Faculty of Science and Engineering. He holds a portfolio in the Office of the Provost and is based at Curtin Perth. His research focuses on next-generation Positioning, Navigation, and Timing (PNT) systems leveraging Low Earth Orbiting (LEO) satellites, precise orbit determination, physics-based machine learning, and lunar PNT. He is affiliated with the Institute for Geoscience Research (TIGeR) and the GNSS Research Center. Dr. Allahvirdizadeh's academic career includes roles as a Research Associate (2022–2024), Casual Academic (2013–2022), and industry experience as a Geodetic Surveyor and Project Manager. He has received awards such as the DB Johnston Award (2023) and the Curtin International Postgraduate Research Scholarship (2019). His research activities include optimizing CubeSat orbit determination, developing the Shadow Toolbox software, and analyzing GNSS signal effects in Earth's shadow. He serves as a Guest Editor for Remote Sensing, reviews for journals like GPS Solutions and IEEE Transactions, and chairs sessions at international conferences (e.g., ISPRS Technical Commission IV Symposium). As a network administrator, he manages Curtin's GNSS receiver network, contributing data to global agencies like the International GNSS Service (IGS-MGEX). Education: PhD in Spatial Sciences (funded by ARC Discovery Project DP 190102444), MSc in Geodesy. Grants: Curtin Faculty of Science and Engineering Research Grant (2025), TIGeR Small Grant (2022). Labs/Teams: GNSS Research Center, TIGeR, and the Innovation Central Perth - Binar Space Program.
Dr. Hoang Nguyen is a Research Fellow at Curtin University's School of Earth and Planetary Sciences (EPS), part of the Faculty of Science and Engineering. His primary affiliation is with the EPS, and he holds a role in the Office of the Provost. His work focuses on geophysical instrumentation development, seismic survey methodologies, and geochemical analysis of geological formations. Nguyen specializes in designing cost-effective geophysical tools such as electromagnetic vibrators and seismic acquisition systems, demonstrated through publications like The construction of a simple portable electromagnetic vibrator from commercially available components (2019) and A low-cost system and method for acquiring small refraction seismic surveys (2018). His research spans structural geology, mineralization processes, and lithospheric mantle studies, with notable contributions to Vietnamese and Southeast Asian geological surveys. His recent work explores interdisciplinary applications, including FAIR Data Principles in Data Mesh architectures (2025) and lithium-ion battery electrochemistry (2023). Nguyen has also contributed to medical studies on hepatitis C treatments in Vietnam (2018) and environmental monitoring in marine systems (e.g., South China Sea gas distributions, 2021). Nguyen's career demonstrates a strong focus on practical field applications, innovation in geophysical instrumentation, and collaborative research across disciplines including computer science and materials science.
Fernando Marmolejo-Ramos is an academic affiliated with Flinders University's College of Education, Psychology, and Social Work. He holds a PhD in Experimental Psychology from the University of Adelaide and has held roles including Research Fellow at the University of South Australia, Visiting Research Fellow at the University of Adelaide, and Lecturer positions across multiple institutions. His expertise spans experimental psychology, cognitive science, and statistical methodologies. Education: BA in Psychology (Universidad del Valle, Colombia, 2003), MAppSc in Psychology (University of Ballarat, Australia, 2007), PhD in Experimental Psychology (University of Adelaide, Australia, 2011). He completed a postdoctoral fellowship at Stockholm University (2014–2016). Research focuses on embodied and artificial cognition, statistical modeling, and robust research methods. Key interests include language comprehension, cross-modal perception, machine behavior, and statistical cognition. Over 120 peer-reviewed articles and numerous grants highlight his contributions to fields like AI ethics, health informatics, and educational technology. His work has been featured in outlets like Nature Human Behavior and Science . Awards include the IEPRS Postgraduate Research Scholarship and the Young and Innovators Researchers Program Fellowship. He serves on editorial boards for journals such as Cognitive Processing and Frontiers in Applied Mathematics . Notable grants include projects on AI in cardiac care, machine learning for health diagnostics, and AI ethics in governance. Current roles include leading projects on AI in education and collaborative reasoning tools for defense applications.
Shenjun Zhong is a Research Fellow at Monash Biomedical Imaging , Monash University. His research focuses on advancing medical imaging technologies through deep learning and artificial intelligence, particularly in enhancing low-field MRI image quality via image-to-image translation and synthetic image generation. He is a Chief Investigator in the National Mobile Magnetic Resonance Imaging Network project, collaborating with institutions like the University of Queensland and Hyperfine Inc. His work addresses challenges in clinical MRI applications, including CSF volume measurement accuracy and radiological image quality assessment. Key research interests include medical imaging , deep learning , multimodal learning , and biomedical engineering . Recent studies explore AI-driven solutions for consistency in brain volume measurements and parameter-efficient fine-tuning of large language models for medical tasks. Notable contributions include peer-reviewed articles on synthetic MRI image generators, cross-modal pre-training for visual question answering, and latent representations of white matter streamlines. His findings aim to improve accessibility and accuracy of portable MRI systems in clinical settings.
Dr Erdun Gao is a Grant-Funded Researcher at the Australian Institute for Machine Learning (AIML), part of the Faculty of Sciences, Engineering and Technology at the University of Adelaide. His research focuses on machine learning, causal discovery, multimodal systems, and data hiding techniques. He is eligible to supervise Masters and PhD students as a co-supervisor. Key research interests include cross-modal learning, domain generalization, federated learning, and scalable causal inference methods. His work addresses challenges such as missing data imputation, adversarial model behavior, and secure information embedding in images. Notable contributions span technical areas like DAG structure learning, diffusion model bias mitigation, and high-order score estimation in incomplete datasets. Publications from 2019-2025 showcase advancements in both foundational AI theory and applied data science solutions. His team develops algorithms for real-world applications in healthcare, image processing, and secure data transmission. Dr Gao’s current projects involve: Exploring multimodal LLM vulnerabilities through adversarial 'distraction' techniques Developing scalable causal discovery frameworks for mixed data types Innovating reversible data hiding methods for medical imaging applications
Xin Yuan (a.k.a. Vernon) is a Senior Research Associate at the School of Electrical and Mechanical Engineering, Faculty of Sciences, Engineering and Technology at the University of Adelaide. His research focuses on Artificial General Intelligence (AGI), cognitive decision-making, robotics, autonomous systems, network security, and cyber-physical systems, with practical applications in industries like meat processing and intelligent transport systems. He has secured over $535k in Tier-3 funding from organizations such as Meat & Livestock Australia and collaborates with South Australian industry partners and zoos to develop engineering solutions for animal welfare and enrichment. Dr. Yuan teaches courses in programming, digital electronics, and autonomous systems, impacting over 3,000 engineering students. His work includes developing advanced robotic systems for red meat processors and autonomous vehicles. Recent research trends in his publications emphasize knowledge tracing algorithms, control systems for neural networks, and graph-based machine learning. He actively engages in student-industry partnerships and leads innovative projects like autonomous micro-air vehicles and animal behavior monitoring systems. Grants & Funding: Over $535k in Tier-3 funding for industry projects. Collaborations: South Australian Zoos, Meat & Livestock Australia, Australian Meat Processor Corporation. Projects: Shadow Robot systems, energy-efficient UAV-UGV tracking, sealion protection systems. His research portfolio bridges theoretical advancements in AGI with real-world applications, emphasizing interdisciplinary innovation in engineering and AI.
Lemai Nguyen is an Associate Professor in Information Systems and Business Analytics at Deakin Business School, Faculty of Business and Law, Deakin University. Her research and teaching are deeply rooted in the intersection of artificial intelligence, digital health, and socio-technical systems, with a strong emphasis on responsible and human-centered technology design. Her research interests include: Human-AI interaction and empathetic AI Applied machine learning in business and healthcare Digital health and health informatics Socio-technical analysis of digital technologies Digital and data analytics maturity assessment Creativity and problem-solving processes Ethics and responsible AI Her recent publications reflect a strong focus on the application of AI and digital technologies in healthcare, particularly in diabetes management, digital health literacy, and patient-provider collaboration. She also explores AI in marketing (e.g., AI influencers), tourism, and sustainability. Her work spans both technical development and socio-organizational impact, often using frameworks like UTAUT and socio-ecological-technical models. She has received several scientific honors, including: Deakin Vice-Chancellor’s Award for Teaching Excellence (2023) Best Paper Award in Healthcare (2023) Multiple Best Track Awards at the Australasian Conference on Information Systems (ACIS) Fellow of the Australasian Institute of Digital Health (FAIDH) Senior Certified Professional, Australian Computer Society Lemai Nguyen actively supervises PhD and Master’s students, with current research on empathetic AI, blockchain adoption, and Indigenous health technologies. She has led and contributed to research grants from the Department of Health, Western Victoria PHN, Tata Consultancy Services, and others, focusing on digital health evaluation, data analytics maturity, and technology implementation. She is also deeply involved in professional service, serving as: Section Editor, Australasian Journal of Information Systems Review Editor, Digital Health Frontiers Track Co-chair for Digital Health at ACIS (2011–2013, 2017–2024) Track Co-chair for AI in Business and Society at PACIS 2024 Associate Editor for AI in Business and Society at ICIS 2024 Ad-hoc reviewer for top journals including European Journal of Information Systems and Journal of the Association for Information Systems She teaches subjects such as Machine Learning in Business, Artificial Intelligence for Business, Digital Business Analysis, and Predictive Analytics, shaping the next generation of data-savvy business leaders.
Dr. Mohammad Al-Rawi is a Senior Lecturer in Engineering at Charles Sturt University's School of Computing, Mathematics and Engineering. With over 17 years of experience, he specializes in Computational Fluid Dynamics (CFD) and Finite Element (FE) modeling, applying these to biomedical and sustainable engineering challenges. PhD in Mechanical/Biomedical Engineering, Auckland University of Technology (2012) MSc and BSc in Mechanical Engineering, University of Technology, Baghdad His research spans biomedical diagnostics (e.g., cardiovascular disease detection), thermal comfort modeling for sustainable housing, and sports engineering innovations. He has contributed to engineering education frameworks and holds a professional vibration analysis certification. Current roles include Associate Editor for two journals and active participation in ASME conferences. Engineering Education (Constructive Alignment, Project-Based Learning) Biomedical Applications (CFD in Arterial Analysis) Sustainable Technologies (Indoor Air Quality Optimization) Scientific awards include: Mobius Institute Vibration Analyst I Certification (Australia)
Professor Michael Thielscher is a Professor at the School of Computer Science and Engineering, UNSW. He is internationally recognized as Australia's highest-ranked scholar in Artificial Intelligence (ScholarGPS 2022/23/24) and a leader in General Game Playing, Cognitive Robotics, and Knowledge Representation. He holds a PhD from Darmstadt University (1994) and was previously an Associate Professor at Dresden University before joining UNSW. His accolades include the 2009 ARC Future Fellowship and the 1998 Darmstadt University Alumni Award for Research Excellence. He co-developed the award-winning FluxPlayer, World Champion in the AAAI General Game Playing Competition (2006). He has authored over 200 refereed papers and five books, and serves as Program Chair for IJCAI-28 and former President of KR Inc. His research spans AI foundations, autonomous agents, and epistemic reasoning in games. Education: Postgraduate Diploma (1992) and PhD (1994), both with distinction from Darmstadt University. Career highlights include leadership roles at Dresden University and contributions to AI competitions, conferences, and educational initiatives. His work emphasizes bridging symbolic AI with deep learning, particularly in dynamic environments requiring strategic reasoning. Research Interests: Artificial General Intelligence Epistemic Reasoning in Games Robotics and Autonomous Systems Formal Methods in AI Awards & Recognition: Top-ranked AI scholar in Australia (ScholarGPS) ARC Future Fellowship (2009) World Champion FluxPlayer (2006) Leadership & Service: Conference chairs (KR'20, IJCAI-28), editorial roles, and international advisory positions. His contributions to AI education and competition frameworks have global impact.
John Lau serves as an Associate Professor in the Department of Mathematics and Statistics at the School of Physics, Maths and Computing, The University of Western Australia. His research contributes to UN Sustainable Development Goals through advanced statistical methodology development. His core expertise spans: Bayesian nonparametric statistics and mixture modelling Statistical clustering and Markov chain Monte Carlo techniques Time-series analysis and density estimation Computational statistics with applications in image analysis Algorithmic bias mitigation in machine learning systems Recent publications demonstrate a clear trajectory toward solving real-world problems: developing causal frameworks for bias detection in classification systems (2024) and creating novel statistical models for engineering reliability prediction (2022). His work bridges theoretical statistics with practical implementations in AI fairness and industrial maintenance. No scientific awards are documented in current profiles. Academic contributions include: Supervision of at least one research student Investigator role in the 2014 cybersecurity project for smart grid assessment Collaborations across engineering and computer science disciplines
Dr. Yi Cui is a Senior Lecturer in the School of Electrical Engineering and Computer Science at the University of Queensland (UQ), Australia. He holds a Ph.D. in Electrical Engineering from UQ (2016) and previously served as a Research Associate at the University of Tennessee, Knoxville, USA. His expertise spans wide-area monitoring and control, smart grid cybersecurity, data analytics, and condition assessment of power transformers. Education: - B.Eng. and M.Eng. from Southwest Jiaotong University, China (2009, 2012) - Ph.D. in Electrical Engineering, University of Queensland (2016) Research Interests: Dr. Cui focuses on cybersecurity strategies for smart grids, data-driven approaches to power system stability, and condition monitoring of power equipment. His work integrates advanced analytics, machine learning, and sensor technologies to enhance grid resilience and transformer reliability. Grants & Projects: - Cybersecurity Defence Strategies of Distribution Synchrophasor in Smart Grids (UQ Cyber Seed Funding, 2021–2022) - Collaborations with industry partners like Northern Territory Power and Water Corporation on network analytics. Teaching & Supervision: Available for supervision in electrical engineering and computer science, focusing on renewable energy integration, smart grid technologies, and power system cybersecurity. Labs/Teams: Contributes to UQ's Cyber Security Research Group and collaborates with international institutions on cybersecurity and smart grid initiatives.
