Xuan Liang is a Lecturer in Statistics at the Research School of Finance, Actuarial Studies and Statistics (RSFAS), Australian National University. With a PhD from Peking University and postdoctoral experience at Monash University, his research focuses on spatial statistics, nonparametric modeling, and environmental data analysis. Education: PhD in Statistics (Peking University, 2017), BSc in Statistics (Zhejiang University, 2012) His work addresses methodological challenges in spatial panel data analysis, network modeling, and air pollution quantification. He has developed novel techniques for meteorological confounder adjustment in air quality assessments and contributed to distributed data analysis methods. Recent research trends include: Advancing quasi-score matching for spatial econometric models Improving subbagging algorithms for big data Creating robust distributed data aggregation frameworks Refining spatial autoregressive panel data methodologies Scientific contributions include: ANU Vice-Chancellor’s Citation for Outstanding Contribution to Student Learning (Early Career), 2022 CBE Teaching Commendation for Outstanding Teaching, 2020 Co-development of the ggmatplot R package for matrix visualization Co-inventor of Chinese patent 201811183512.0 for air quality assessment He teaches advanced courses in time series analysis, regression modeling, and mathematical statistics at ANU, while maintaining active research collaborations in econometrics and environmental statistics.
Dr. Feras Dayoub is a Senior Lecturer at the School of Computer and Mathematical Sciences (Faculty of Sciences, Engineering and Technology) at the University of Adelaide , specializing in Embodied AI and Robotic Vision within the Australian Institute for Machine Learning (AIML) . He co-directs the CROSSING French-Australian laboratory for human-autonomous agent teaming and holds an Adjunct position at the Queensland University of Technology (QUT) , serving as an Associate Investigator at its Centre for Robotics . Previously, he was a Chief Investigator at the ARC Centre of Excellence for Robotic Vision . His research focuses on advancing reliable deployment of computer vision and machine learning on mobile robots in real-world environments. Applied projects include agricultural automation , environmental conservation , and autonomous infrastructure monitoring . He has published extensively on topics like object detection , domain adaptation , 3D representation learning , and vision-language navigation , with a particular emphasis on robustness in dynamic and partially observed environments. Dr. Dayoub is also an educator specializing in programming , computer vision , and robotic perception . He contributes to open-source robotics research through tools like AARK (Autonomous Racing Toolkit) and has led teams developing solutions for precision agriculture (e.g., Deepfruits fruit detection system) and environmental monitoring (e.g., Crown-Of-Thorns starfish detection ). Key Collaborations : CROSSING Lab, QUT Centre for Robotics Research Themes : Embodied AI, Robust Perception, Domain Adaptation
Professor Luke Prendergast is the Deputy Dean of the School of Computing, Engineering & Mathematical Sciences (SCEMS) at La Trobe University (LTU) and holds a Professorship in the Department of Mathematics and Statistics. He previously served as Head of Department (2014–2020) and led LTU's Statistics Consulting Platform. His research focuses on robust statistics, meta-analysis, dimension reduction, and applied statistics, leading the DRAMA research group. Collaborations span fields like endocrinology, disability studies, and respiratory health. He actively contributes to research grants, including projects on Prader-Willi syndrome and exercise for disability populations. Professor Prendergast's recent work emphasizes statistical software development (e.g., the rquest package) and applications in biostatistics, such as metabolomics analysis and health intervention fidelity. His articles address topics like quantile-based hypothesis testing, geospatial accessibility for disability care, and motivational interviewing efficacy. Professional roles include NHMRC grant review panels, editorial boards for Nutrients and Respirology , and leadership in the Statistical Society of Australia (SSA Vic). His teaching includes courses in meta-analysis, linear models, and data-based critical thinking. Grants funded projects on exercise programs for cerebral palsy populations and community-university partnerships for disability inclusion. Luke's work bridges statistical theory with real-world health challenges, emphasizing robust methodologies and interdisciplinary collaboration.
Kerry Taylor is an Associate Professor (Data Science) at the School of Computing, Australian National University (ANU). She holds visiting roles at the University of Surrey (UK) and University of Melbourne. Her career spans 20 years at CSIRO, UN big data projects with ABS, and interdisciplinary research in data management, IoT, and semantic technologies. She lectures in data mining and convenes ANU's postgraduate applied data analytics programs. Education includes a BSc (Hons 1) in Computer Science from UNSW (1983) and a PhD in Computer Science and Technology from ANU (1996). She co-chaired the W3C/OGC Spatial Data on the Web working group (2015-2017) and serves on editorial boards for Knowledge-Based Systems and International Journal of Distributed Sensor Networks . Research focuses on ontologies, semantic web, machine learning in IoT, and spatial data systems. Active projects include government information frameworks, distributed IoT facilities, and sensor data integration. Her work emphasizes interdisciplinary applications of logic-based and semantic approaches to data challenges.
Buyung Kosasih is a Professor in the School of Mechanical, Materials, Mechatronic and Biomedical Engineering at the University of Wollongong. He has held this position since 2000 and focuses on teaching and research in mechanical engineering, including Machine Dynamics, Finite Element Methods, and Renewable Energy Technology. His research spans fluid dynamics in industrial processes, renewable energy systems, and aqueous lubrication. Key projects include 3D-printed surfboard fin optimization and steel coating dynamics. Research interests emphasize experimental and computational fluid dynamics, particularly in renewable energy turbines and tribological systems. Notable awards include the 2013 Outstanding Contribution to Teaching and Learning Award. He has supervised numerous students and led over 20 funded projects, including ARC grants for steel innovation and renewable energy. Collaborative work includes the Steel Research Hub and HVAC/cool roof efficiency studies.
Professor Josef Dick serves as a Professor and Deputy Head in the School of Mathematics & Statistics at the University of New South Wales (UNSW). With a distinguished career in computational mathematics, he has established himself as a leading researcher in numerical integration methods and quasi-Monte Carlo theory. His work bridges theoretical mathematics with practical computational applications across various scientific domains. Dr. Dick earned his PhD in Mathematics from UNSW in 2004 and his MSc in Mathematics from the University of Salzburg in 2001. His academic journey reflects a strong foundation in both theoretical and applied mathematics, which has informed his subsequent research contributions. Professor Dick's research primarily focuses on numerical integration and quasi-Monte Carlo rules , employing techniques from number theory , abstract algebra (particularly finite fields), discrepancy theory , wavelet theory , and statistics . His work provides rigorous analysis of practical algorithms for computational problems, with implementations often provided in Matlab to bridge theory and application. His research has successfully addressed point distributions on the unit cube for numerical integration, completely uniformly distributed sequences for Markov chain quasi-Monte Carlo algorithms, and explicit constructions of uniformly distributed points on the sphere. Analysis of his recent publications (2022-2025) reveals a consistent focus on advancing quasi-Monte Carlo methods, with increasing integration of machine learning techniques and applications to complex computational problems. His work demonstrates strong interdisciplinary connections between pure mathematics, computational science, and practical engineering applications, particularly in uncertainty quantification and high-dimensional numerical integration. Discovery project from Australian Research Council (2012-2014): "Mathematics in the round - the challenge of computational analysis on spheres" Queen Elizabeth II Fellowship from Australian Research Council (2010-2014): "Algebraic methods for Markov Chain Monte Carlo and quasi-Monte Carlo" UNSW Vice Chancellor Fellowship (2006-2009) Professor Dick has supervised numerous PhD and Honours students working on topics including Quasi-Monte Carlo methods, Discrepancy Theory, Markov chain Monte Carlo, and Uncertainty Quantification. His research has been supported by significant grants from the Australian Research Council, including serving as Chief Investigator on multiple projects. Beyond his research, he serves as an Editor for the Journal of Complexity and Journal of Approximation Theory, demonstrating his leadership in the mathematical community. He teaches courses in Algebra and Mathematical Computing for Finance at UNSW.
Dr. Azadeh Ghari-Neiat is a Senior Lecturer in Software Engineering at the University of Queensland's School of Electrical Engineering and Computer Science. She completed her PhD in Computer Science from RMIT University in 2018. Prior to joining UQ, she held academic positions at Deakin University as a Senior Lecturer and at the University of Sydney as a postdoctoral research fellow. Her research focuses on the intersection of Internet of Things (IoT), Mobile Computing, Crowdsourcing, and Cybersecurity. She develops innovative solutions for enhancing connectivity and security in modern computing environments through crowdsourced approaches. Key areas include service composition in sensor clouds, trust management frameworks, and optimization of drone-as-a-service systems. Her publications demonstrate consistent focus on IoT service ecosystems, with recent work exploring blockchain applications and machine learning techniques for dynamic systems. The research trends show evolution from fundamental service composition to AI-driven optimization in distributed environments. Dr. Ghari-Neiat leads projects involving energy service crowdsourcing and secure architectures for cyber-physical systems. Her work maintains strong emphasis on practical applications in delivery systems, UAV networks, and IoT marketplaces.
Olga Kokshagina serves as an Associate Professor in Innovation & Entrepreneurship at The University of Sydney, with adjunct research appointments at Monash University's Emerging Technology Lab and the UNU Hub - Learning Planet Institute. She is also an active member of the French Digital Council. Her research program investigates technology-mediated collaboration in complex innovation systems, focusing on healthcare transformation, deep tech commercialization, and co-design methodologies. Kokshagina has led high-impact projects with global institutions including the World Health Organization, OECD, STMicroelectronics, Vall d’Hebron Hospital, and Roche, demonstrating strong translational research capabilities. Her scholarly work centers on value-based healthcare innovation, digital platform governance, and AI-enhanced collaborative systems. She examines how organizational capabilities evolve during technological transitions, particularly in healthcare ecosystems, and investigates regulatory frameworks for algorithmic control in digital markets. Kokshagina's research bridges theoretical innovation management with practical applications, evidenced by her co-founding of Ninti—an initiative advancing women's health in workplace environments—and her Open Covid-19 crowdsourcing campaign that mobilized global expertise during the pandemic. Analysis of her 2021-2025 publications reveals a cohesive trajectory examining innovation in socio-technical systems. Key themes include value digitalization in healthcare, mission-oriented interdisciplinary collaboration, and the impact of big data on technology management. Her work consistently addresses grand challenges through mixed-methods approaches, spanning conceptual frameworks in journals like Research Policy to applied studies in Technovation and R&D Management, with increasing focus on quantum readiness and AI-augmented learning systems. No scientific awards are documented in the provided materials. Kokshagina currently holds a 2025 research grant for "Co-designing societal readiness and scenario building for quantum" through the University of Sydney Nano Institute/Catalyst program. While no student supervision activities are mentioned, her collaborative projects involve multi-institutional teams across industry, government, and academic sectors. Kokshagina maintains active roles within the University of Sydney Nano Institute and contributes to international policy discourse through the French Digital Council. Her Ninti initiative exemplifies her commitment to human-centered innovation, while ongoing collaborations with healthcare providers like Roche and Vall d’Hebron Hospital demonstrate sustained engagement with real-world implementation challenges in value-based care systems.
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 Dongbin Wei is an Associate Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney (UTS), with a career spanning academia and industry. He holds a PhD in Materials Processing Engineering from the University of Science and Technology Beijing (2001) and academic appointments from 2005–2012 at the University of Wollongong (Research Fellow to Lecturer) and 2013–2017 at UTS (Senior Lecturer) before his promotion to Associate Professor in 2018. His research lies at the intersection of Mechanical Engineering , Manufacturing Engineering , and Materials Processing , focusing on: Ultrasonic Additive Manufacturing (UAM) Micro Metal Forming and Size Effects Tribology and Lubrication Numerical Simulations of Material Processing Composite Material Fabrication Key contributions include: Development of the Springback Path–Displacement Adjustment (SP-DA) method for stamping accuracy Advancements in femtosecond laser texturing for silicon wettability control Studies on nanolubrication in hot rolling Optimization of micro-deep drawing parameters He has secured competitive grants from the Australian Research Council (ARC) and industry partners like Weir Minerals Australia Ltd , including projects on: Revolutionizing mineral separation via additive manufacturing Super high-speed grinding technologies Mechanics of micro composite drill fabrication As a lead supervisor, he guided the 2022 thesis 'Creation and Validation of 3D Printable Mineral Separation Spiral' . His work bridges theoretical analysis, computational modeling (FEM/FEA), and practical validation in advanced manufacturing systems.
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.
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.
Professor Fernando Calamante is a Professor of Biomedical Engineering at The University of Sydney and Director of Sydney Imaging Core Research Facility. He leads the National Imaging Facility node and focuses on advanced MRI methodologies, particularly Diffusion and Perfusion MRI, to study brain connectivity and neurological disorders. His work includes developing the MRtrix software, widely used in diffusion MRI analysis. He holds extensive funding (~$50M) and has been recognized with awards like ISMRM Fellowship and NHMRC grants. His research spans super-resolution imaging, brain connectomics, and clinical applications in stroke and tumors. Education: BSc (Physics, Argentina), PhD (Magnetic Resonance Imaging, University College London). Career highlights include leadership roles at The Florey Institute and ISMRM presidency (2021-2022). Research interests include: Novel MRI methods for brain connectivity and super-resolution imaging Applications of Diffusion and Perfusion MRI in neurology Integration of structural and functional connectomics Key achievements: Over 200 publications, software innovations, and leadership in global MRI societies.
Associate Professor Naseem Ahmadpour is a leading researcher in Human-Computer Interaction (HCI) and Design at the University of Sydney 's School of Architecture, Design and Planning. She directs the Affective Interactions Lab and serves as Associate Dean (Research) and Chair of the EDI Committee . PhD, University of Montreal MSc, Chalmers University of Technology BSc, Bahonar University Her research critically examines the ethical implications of sociotechnical systems in care and work futures, focusing on participatory design with vulnerable populations. She explores immersive technologies like VR for mental health, pain management, and clinical training, while advocating for equity in design practices. Recent work includes 15+ publications (2023-2026) on VR applications, ethical design tools, and sociotechnical critiques. She has received the SOAR Prize (2022-2023) and led grants on VR in clinical education, digital volunteering, and aging technologies. Dr. Ahmadpour supervises PhD students in projects like Collaborative Reflection in Mental Health and Cultural Bias in Digital Design . She contributes to the Design. Think. Make. Break. Repeat methodology handbook and co-leads the VirtX Community of Practice for immersive realities.
Andy McLennan is a Professor in the School of Economics at the University of Queensland since 2007, following roles at the University of Minnesota (1987–2005) and the University of Sydney. His research focuses on mathematical economics and game theory, with contributions to computational game theory, fixed point theory, and algebraic geometry. Notable collaborations include work with Richard McKelvey on the Gambit software package for game analysis. Education: B.A. in Mathematics from the University of Chicago (undergraduate), PhD in Economics from Princeton University (1982). Prior faculty positions included the University of Toronto and Cornell University. Research Interests: Explores intersections between pure mathematics and economics, including applications of topology (Vietoris-Begle theorem), differential geometry (Morse-Sard theorem), and computational complexity in markets. Recent work includes the 'Index +1 Principle' for equilibrium stability and fixed point index theory. Software & Tools: Co-developed Gambit , a widely used open-source toolkit for analyzing finite games. Authored technical software for solving systems of equations and 3D visualization tools for academic use. Books: Authored Advanced Fixed Point Theory for Economics (Springer, 2018), The Algebra of Coherent Algebraic Sheaves , and The Nature and Origins of Modern Mathematics . Personal: Lives in Brisbane with his partner Shino Takayama (also an economist) and their son Sean. Enjoys Japanese language, classical music, and strategic games like Go and chess.
