جست‌وجوی استاد و پژوهشگر

Josef Dickمشاهده پروفایل

استاد

• Numerical Integration
• Quasi-Monte Carlo Rules
• Number Theory
• +۵ مورد دیگر

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.

• Innovation Management
• Entrepreneurship
• Healthcare Innovation
• +۴ مورد دیگر

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.

Liyi Zhouمشاهده پروفایل

مدرس

• Systems Security
• Blockchain Technology
• Artificial Intelligence
• +۵ مورد دیگر

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.

Christopher Ferrieمشاهده پروفایل

دانشیار

• Quantum Estimation and Control
• Quantum Machine Learning
• Quantum Information Science
• +۵ مورد دیگر

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.

• Signal Processing
• Speech Recognition
• Machine Learning
• +۶ مورد دیگر

Roberto Togneri is a Professor and Senior Honorary Research Fellow at the University of Western Australia's School of Electrical, Electronic and Computer Engineering. He has been affiliated with the university since 1988, following his PhD in 1989. His research focuses on signal processing, speech recognition, machine learning, and biometrics, with notable contributions to audio-visual recognition systems and fraud detection. Education: PhD in Electrical Engineering (University of Western Australia, 1989). Research interests include feature extraction for audio signals, neural network models for speech and speaker recognition, and applications of machine learning to fraud prevention. His work has been recognized with awards such as the Education Innovation Award (ICASSP 2019) and grants from the Australian Research Council (e.g., DP110103336 for a 3D Audio-Visual Speech Recognition System). Key projects include developing robust speech recognition systems in adverse environments and advancing graph-based fraudster group detection using spatio-temporal data. He has also contributed to editorial roles in IEEE Signal Processing Magazine and authored over 214 research outputs. Funding highlights include $279,000 for a 3D audio-visual speech recognition system (2011–2013) and $230,000 for robust speech recognition in hostile environments (2010–2012). His research aligns with UN SDGs related to innovation and infrastructure.

JuHyun Leeمشاهده پروفایل

دانشیار

• Architectural design computing
• Design cognition
• Urban complexity
• +۸ مورد دیگر

JuHyun Lee is an Associate Professor of Architecture and Computational Design in the School of Built Environment at the Faculty of Arts, Design and Architecture (ADA), University of New South Wales (UNSW) Sydney, where they also hold the prestigious title of Scientia Academic. With a professional background in architecture and construction (1998-2002), they have held academic positions across Australia including a five-year post-doctoral fellowship at the University of Newcastle (2012-2017) and a senior research fellowship at the University of South Australia (2018), following earlier research and teaching roles in South Korea (2003-2011). Lee specializes in architectural design computing, design cognition, and urban complexity, integrating computational methods, cognitive science, and architectural theory to advance architectural intelligence and human-centered design. Their research spans architectural visualization, analysis and design methods, algorithm/protocol design, and data visualization with computational approaches. They have established a strong research program examining the intersection of language, culture, and design cognition, particularly focusing on cross-cultural design communication between Australia and Korea. Lee's recent publications demonstrate a clear trajectory toward increasingly sophisticated integration of computational methods with architectural design theory, particularly in the areas of shape grammar, space syntax, and machine learning applications. Their work shows consistent focus on practical applications of computational design methods to real-world architectural problems, with growing emphasis on cross-cultural collaboration and intelligent design systems. The research portfolio reveals a deepening engagement with AI and machine learning techniques applied to architectural design assessment and generation. Scientia Academic at UNSW Sydney Associate Fellow of the Higher Education Academy (AFHEA, 2020) As an educator, Lee develops cutting-edge courses in computational design and Building Information Modeling (BIM), integrating experiential learning and industry engagement. They have secured over $11 million in research funding, including multiple ARC Discovery Projects and an Australia-Korea Foundation grant. Lee co-directs the Advanced Architectural Analytics Laboratory (A 3 LAB), leading interdisciplinary research on design automation, spatial analysis, and machine learning applications in architecture, while also leading cross-cultural initiatives like the Australia-Korea Architects' Network (AKAN). Lee supervises multiple HDR students working on culturally sustainable urban design, socio-spatial patterns in public housing, and computational layout generation. Their research has significant implications for improving design communication across cultural boundaries and developing more coherent, clear, and accessible built environments through computational design approaches.

Hongfu Sunمشاهده پروفایل

مدرس ارشد

• Biomedical Engineering
• Deep Learning
• Magnetic Resonance Imaging (MRI)
• +۳ مورد دیگر

Hongfu Sun is a Senior Lecturer at the School of Engineering, University of Newcastle. His research focuses on innovating MRI mechanisms for clinical and research applications, particularly in Quantitative Susceptibility Mapping (QSM). He is internationally recognized as a pioneer in QSM and integrates MR physics, signal processing, and AI for medical imaging advancements. Sun holds a Ph.D. in Biomedical Engineering from the University of Alberta, Canada. Professional Experience: Senior Lecturer at University of Newcastle (current) ARC DECRA Research Fellow at University of Queensland (2021–2023) Postdoctoral Researcher at University of Calgary (2015–2019) Research Interests: Focuses on MRI innovation, including QSM, deep learning for medical imaging, and AI-driven reconstruction techniques. His work addresses challenges like sub-millimeter resolution and artifact reduction in MRI. Recent projects involve generative AI models for MRI analysis and accelerated quantitative imaging methods. Grants and Funding: AU$1.69M in grants, including a 2021 ARC DECRA for microscopic MRI techniques 2024 NHMRC grant for Parkinson’s disease MRI diagnostics Teaching: Course coordinator for Medical Imaging and Signal Processing at University of Newcastle Focus on biomedical imaging, computational methods, and signal analysis Labs/Teams: Leads research in MRI innovation, collaborating on QSM, deep learning applications, and translational imaging techniques. Active in interdisciplinary projects combining physics, AI, and clinical medicine.

• Biomedical signal and image analysis
• Deep learning
• Computational statistics
• +۵ مورد دیگر

Chee-Ming Ting is an Associate Professor in the School of Information Technology at Monash University Malaysia. His expertise lies in machine learning, data science, and biomedical engineering, with a focus on signal processing, computational neuroimaging, and computer-aided detection. Previously, he held positions at King Abdullah University of Science and Technology (Research Scientist) and Universiti Teknologi Malaysia (Senior Lecturer). He has authored over 26 journal papers and 43 conference papers, and has secured research grants totaling RM2.5 million as PI/Co-PI. Education: PhD in Mathematics - Statistics, Master of Engineering in Electrical Engineering, and Bachelor of Engineering (Hons.) in Electrical & Electronics Engineering. Research interests include biomedical signal/image analysis, deep learning, spatio-temporal modeling, and neuroimaging applications for disease prediction and patient monitoring. He has supervised 9 graduate students (4 PhD, 5 Masters) and currently oversees 10 PhD candidates. Awards include the IEEE Signal Processing Society Malaysia's Research Excellence Award (2019, 2022) and several national/international innovation awards. His work contributes to UN Sustainable Development Goals related to health and technological advancement. Key projects include frameworks for neurological disease prediction using brain networks and generative adversarial networks for medical imaging enhancement.

Daniel Horsleyمشاهده پروفایل

دانشیار

• Combinatorial Designs
• Edge Decomposition of Graphs
• Graph Theory
• +۳ مورد دیگر

Daniel Horsley is an Associate Professor and ARC Future Fellow at the School of Mathematical Sciences, Monash University. His research focuses on combinatorial designs and edge decomposition of graphs, with notable contributions to extremal graph theory, Zarankiewicz problems, and graph decomposition theorems. Current Role: ARC Future Fellow, Associate Professor Affiliation: School of Mathematical Sciences, Monash University Active Projects: 'The Zarankiewicz problem through linear hypergraphs and designs' (2022–2025), 'Edge decomposition of dense graphs' (2017–2022), and more Research interests span combinatorial designs, graph decomposition, and extremal combinatorics. His work emphasizes theoretical advancements in design theory, with applications in discrete mathematics and optimization. Recent articles address semi-inducibility, Zarankiewicz numbers, and embedding partial designs, reflecting his expertise in structural and extremal combinatorics. Key awards include the ARC Future Fellowship. His research outputs include over 57 publications in journals like Journal of Graph Theory , SIAM Journal on Discrete Mathematics , and European Journal of Combinatorics . Grant projects include collaborations with ARC, University of Queensland, and University of Melbourne, focusing on Steiner systems, compressed sensing, and combinatorial structure analysis. Advising PhD students and mentoring researchers in discrete mathematics and combinatorial design theory.

• Combinatorial Optimization
• Scheduling Theory
• Operations Research
• +۳ مورد دیگر

Professor Daniel Oron is affiliated with the University of Sydney, where he joined in 2004 after completing his PhD in Operations Research at the Hebrew University of Jerusalem. His research focuses on Combinatorial Optimization, particularly Scheduling Theory, addressing challenges like batch scheduling with setups, customer delivery models, and scheduling under deteriorating conditions. He teaches courses such as Quantitative Business Analysis, Management Science, and Business Analytics Honours. His editorial role includes serving on the board of the Journal of Industrial & Management Optimization . Recent research contributions span multi-agent scheduling, energy recharging in scheduling, and coupled task optimization. He advises two current PhD students: Johnson (Two-agent scheduling problems) and Renjie Yu (Multi-agent scheduling with parallel batching). Publications highlight advancements in scheduling algorithms, resource allocation, and optimization under constraints. Notable works include minimizing late jobs with step-learning models and analyzing parameterized complexity in single-machine scheduling.

• Time Series Analysis
• Econometric Theory
• Functional Data Analysis
• +۳ مورد دیگر

Dr Won-Ki Seo is a Senior Lecturer in the School of Economics at the University of Sydney. His research focuses on time series analysis, econometric theory, and functional data analysis. He holds a Ph.D. in Economics from the University of California, San Diego. Research Interests: Dr Seo's work centers on cointegration analysis in functional spaces, Hilbertian processes, and the application of advanced mathematical frameworks to econometric problems. His recent studies explore tail behavior of Lévy processes, functional principal component analysis, and nonlinear time series modeling. Recent work includes analyzing stopped Lévy processes with Markov modulation and developing methodologies for functional time series inference Key contributions to cointegration theory in Banach spaces and functional data econometrics Dr Seo has published extensively in top journals like Econometric Theory and Journal of Time Series Analysis . His research bridges theoretical econometrics and practical applications in financial and environmental economics. Contact: won-ki.seo@sydney.edu.au | Office: A02 Social Sciences Building

Julie Clutterbuckمشاهده پروفایل

دانشیار

• Geometric Analysis
• Partial Differential Equations
• Spectral Theory
• +۳ مورد دیگر

Julie Clutterbuck is an Associate Professor in the School of Mathematics at Monash University. Her research focuses on geometric analysis, partial differential equations, and spectral theory. She has led major projects funded by the Australian Research Council, including studies on curvature flows, spectral estimates, and optimal shapes. Clutterbuck has been recognized with the Gavin Brown Prize (2014) for outstanding mathematical research. She contributes to academic activities as a conference organizer and speaker, including roles at the AMSI Winter School and the New Zealand Mathematical Society Colloquium. Her teaching commitments include advanced courses in metric spaces and multivariable calculus. Her research explores geometric evolution equations, eigenvalue problems, and capillary surfaces. Notable projects include analyzing the fundamental gap conjecture and studying ancient solutions to curvature flows. Collaborations span international institutions, reflecting her contributions to global mathematical research networks.

Sonny Phamمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computer Vision
• Data Mining
• +۳ مورد دیگر

Associate Professor Sonny Pham leads research in artificial intelligence at Curtin University's School of EECMS. His work balances theoretical foundations with practical applications in computer vision, data mining, and deep learning. As head of the IAMAI research group, he collaborates with industry partners on security systems, healthcare AI, and sustainable technologies. His research explores: Computationally efficient deep learning architectures Compressed sensing for high-dimensional data Robust statistical methods for real-world problems Applications in computer vision and industrial automation Recent publications demonstrate a focus on medical imaging interpretation and efficient neural networks, with applications spanning radiology report generation, semantic segmentation for autonomous systems, and cybersecurity. His team's work consistently bridges theoretical AI advancements with industrial applications. Honors include: Multiple WANMA Awards (2021-2024) for industry-impactful research INCITE Award for social impact technology (2024) IEEE Young Author Best Paper Award (2010) Over $5M in competitive research funding including MRFF and DFAT grants He leads the IAMAI research group with 12+ graduate students and coordinates Curtin's Master of Artificial Intelligence program. Industry collaborations include Alcoa Australia, iCetana, and HyprFire.

Kwan-Wu Chinمشاهده پروفایل

استاد

• Resource Allocation
• Internet of Things
• Maritime Networks
• +۳ مورد دیگر

Kwan-Wu Chin is a Professor in the School of Electrical, Computer and Telecommunications Engineering at the University of Wollongong, where he also serves as Head of Postgraduate Studies (HPS) and co-directs the Wireless Technologies Lab (WTL). His research focuses on resource allocation problems in Internet of Things (IoT) systems, maritime networks, edge computing platforms, and integrated sensing-communication systems. Chin leads an active research group currently supervising five PhD students working on UAV networks, edge computing, maritime systems, and metaverse resource allocation. He has graduated over 20 PhD students who now hold positions in academia and industry. Chin serves as editor for Elsevier Computer Communications and IEEE Internet of Things Journal. His work develops optimization techniques using graph theory, stochastic processes, and machine learning for next-generation wireless systems.

• Applied Mathematics
• Numerical and Computational Mathematics
• Pure Mathematics
• +۷ مورد دیگر

Dr. John Shepherd is an Associate Professor in the School of Science at RMIT University, specializing in applied mathematics, numerical and computational mathematics, and their applications in engineering and environmental systems. His research focuses on analyzing nonlinear problems, particularly in bioreactor dynamics, fluid mechanics, and nuclear energy policy. He has contributed to studies on anaerobic digestion models, reactor stability, and the role of nuclear energy in climate change mitigation. Education: Doctorate in Applied Mathematics (not explicitly stated in text, inferred from title). His work bridges theoretical analysis and real-world applications, such as optimizing methane production in waste digesters and evaluating environmental policies for nuclear energy. He actively supervises research projects, including the analysis of anaerobic digester dynamics. Dr. Shepherd’s publications span interdisciplinary topics, emphasizing the intersection of mathematics, engineering, and environmental science. He engages with policy discussions on nuclear energy’s role in decarbonization, advocating for its integration into clean energy strategies. His research highlights the importance of multiscale analysis in understanding complex systems like bioreactors and fluid flows. Collaborations involve industry and international institutions, reflecting his commitment to practical solutions for sustainability challenges.