جست‌وجوی استاد و پژوهشگر

Wei Baoمشاهده پروفایل

دانشیار

• Networking
• Distributed Machine Learning
• AI for Networked Systems
• +۴ مورد دیگر

Dr. Wei Bao is an Associate Professor in the School of Computer Science at the University of Sydney, part of the Faculty of Engineering. He leads the I-Net (Intelligent Networking) Group and holds a B.Eng. from Beijing University of Posts and Telecommunications (2009), M.A.Sc. from the University of British Columbia (2011), and Ph.D. from the University of Toronto (2015). His research focuses on distributed machine learning, AI-driven network systems, and intelligent network optimization, with industrial collaborations at companies like Link Group. Education: Bachelor of Engineering, Beijing University of Posts and Telecommunications (2009) Master of Applied Science, University of British Columbia (2011) Ph.D., University of Toronto (2015) Research Interests: Dr. Bao's work addresses challenges in distributed machine learning, AI integration into network systems, and optimizing network performance. He emphasizes practical applications through industry partnerships, aiming to bridge academic research with real-world impact. His current projects include federated learning frameworks, IoT communication sharing architectures (e.g., sTube+), and edge computing optimizations. Publications Trends: His recent work focuses on federated learning algorithms (e.g., Federated Learning with Nesterov Accelerated Gradient ), edge computing optimizations ( SOAR: Smart Online Aggregated Reservation ), and partial label learning techniques. These reflect a blend of theoretical advancements and applied systems research. Awards: Multiple Distinguished TPC Member awards (INFOCOM 2020-2024) Best Paper Awards at ACM MSWiM (2019), IEEE NCA (2016), and others Advising & Grants: Dr. Bao supervises PhD candidates in distributed systems and machine learning. His grants include projects like Pioneering Federated Real-Time Video Analytics (ARC DP 2025) and industry collaborations via the University of Sydney's Industry Program. He also leads the Master of Computer Science program at the University of Sydney. Labs & Teams: He directs the I-Net Group, focusing on intelligent networking and distributed systems. Collaborations span institutions like The Hong Kong Polytechnic University (Dr. Dan Wang) and York University (Dr. Uyen Trang Nguyen).

• STEM Education
• Mathematics Education
• Interdisciplinary Learning
• +۴ مورد دیگر

Professor Lyn English is a leading academic in STEM and mathematics education at Queensland University of Technology's College of Education, Department of Curriculum. Her research focuses on interdisciplinary approaches, particularly integrating engineering and data science into early years education. She has authored or co-edited numerous books and journal articles, emphasizing the role of design-based problem solving and mathematical modeling in fostering critical thinking. English has contributed extensively to curriculum frameworks, including the 'Pattern and Structure Mathematics Awareness Program' (PASMAP), and has explored gender dynamics in mathematics achievement. She collaborates with institutions globally, such as Routledge, Springer, and the Australian Council for Educational Research, to advance educational methodologies. Her work highlights the importance of bridging theory and practice in STEM education. English’s research spans cognitive development in statistical and probabilistic reasoning, foundational mathematical concepts in primary education, and teacher professional development. She advocates for accelerating students' learning through innovative curricula like the RAMR framework and has conducted studies on pedagogical practices in diverse educational settings. Her edited volumes, including Handbook of International Research in Mathematics Education and Ways of Thinking in STEM-based Problem Solving , consolidate cutting-edge perspectives in the field. English’s contributions to conferences and symposia underscore her commitment to advancing STEM integration and addressing challenges in mathematics education research.

• Control Theory
• Autonomous Systems
• Reinforcement Learning
• +۳ مورد دیگر

Dr. Cheng-Chew Lim is a Professor in the School of Electrical and Mechanical Engineering at the University of Adelaide. He specializes in control theory, autonomous systems, and multi-agent reinforcement learning. His research focuses on trusted autonomous systems, secure cyber-physical networks, and decentralized decision-making models. He has published over 300 articles and supervised 50+ PhD and master’s students. Dr. Lim teaches courses in control systems, autonomous systems, and engineering project management. He has held editorial roles, including Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics, and is actively involved in professional associations like the IEEE Control and Aerospace Electronic Systems Joint Chapter. His current projects include physics-informed neural networks for medical imaging, secure distributed autonomous systems, and resilient formation control under cyberattacks. Dr. Lim has secured research grants from ARC and industry partnerships, emphasizing practical applications in robotics, cybersecurity, and smart systems.

• Computational stochastic mechanics
• Machine learning in engineering
• Engineering safety assessment
• +۷ مورد دیگر

Dr Dee Wu serves as a Senior Lecturer at the School of Civil and Environmental Engineering at the University of Technology Sydney (UTS), specializing in the integration of computational mechanics, machine learning, and engineering design. With a strong research profile focused on structural reliability and safety assessment, Dr Wu develops innovative frameworks that bridge theoretical mechanics with practical engineering applications, particularly in the realm of composite materials and uncertain structural behavior. Dr Wu's research interests center on computational stochastic and non-stochastic mechanics, with particular emphasis on machine-learning-aided engineering safety assessment, nondeterministic methods for isogeometric analysis with polymorphic uncertainties, and AI techniques for composite material design. Their work addresses critical challenges in structural engineering where uncertainty quantification becomes essential for safety evaluation. The research output reveals a clear trajectory toward developing virtual modeling techniques that significantly enhance computational efficiency while maintaining accuracy in structural analysis. Dr Wu's publications demonstrate expertise in phase-field methods, support vector regression variants (including Extended SVR, Capped SVR, and Twin SVR), and uncertainty quantification frameworks that handle both aleatoric and epistemic uncertainties. These techniques have been successfully applied to fracture mechanics, buckling analysis, vibration analysis, and impact assessment problems. Dr Wu actively pursues funded research in three main areas: Digital twin applications in Civil Engineering, Machine learning aided engineering analysis and design, and Safety assessment for Smart City initiatives. Currently, they are a key participant in the ARC Discovery Project 'Assessment of Dynamic Pile Driving Using Machine Learning' (DP230102781), running from June 2023 to May 2026, working alongside researchers Khabbaz M, Fatahi B, and Zhang X. In teaching, Dr Wu delivers courses including Introduction to Civil and Environmental Engineering (48310), Advanced Engineering Computing (48371), and Finite Element Analysis (49047), demonstrating commitment to both foundational and advanced engineering education. Their ORCID identifier is 0000-0002-7284-5024, and they maintain an active Google Scholar profile reflecting their substantial research contributions in computational structural engineering.

Scott Anthony Sissonمشاهده پروفایل

استاد

• Computational Statistics
• Bayesian Statistics
• Statistical Extreme Value Theory
• +۳ مورد دیگر

Professor Scott Anthony Sisson is a leading academic at the University of New South Wales (UNSW) , holding the position of Professor of Statistics and Data Science in the School of Mathematics and Statistics . He serves as Director of the UNSW Data Science Hub (uDASH) and Deputy Director of the UNSW AI Institute (UNSW.ai) . Previously, he was Deputy Director of the Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and held leadership roles in the Australasian Society of Bayesian Analysis and Statistical Society of Australia . PhD in Statistics (Bristol University, 2002) MSc in Environmental Statistics and Systems (Lancaster University, 1997) BSc in Mathematics and Statistics (Lancaster University, 1996) His research focuses on computational statistics and Bayesian inference , with expertise in machine learning , extreme value theory , and high-dimensional data analysis . He develops simulation-based algorithms for complex statistical problems and applies these to diverse scientific challenges like seagrass decline, urban flood modeling, and drug delivery systems. His recent work spans quantum computing for statistics, graphon modeling, and synthetic likelihood methods. Scientific awards include: 2024 Fellow of the International Society of Bayesian Analysis 2023 Fellow of the Institute of Mathematical Statistics 2017 ARC Future Fellowship 2010 Queen Elizabeth II Research Fellowship 2006 John Yu Fellowship His advising team has mentored students in statistical modeling, Bayesian computation, and applied data science. Grants from the Australian Research Council and industry collaborations support his research in government and scientific applications. He contributes as Associate Editor for Journal of Computational and Graphical Statistics and Statistics and Computing .

• Volcanology
• Volcanic Hazard Assessment
• Computational Geophysics
• +۲ مورد دیگر

Sara Barsotti is a researcher at the National Institute of Geophysics and Volcanology (INGV) specializing in volcanology and volcanic hazard assessment. Her key roles include: Associate Editor for Volcanology at Frontiers in Earth Science Review Editor for Geohazards and Georisks at Frontiers in Earth Science Contributor to the EU Center of Excellence for Exascale in Solid Earth (ChEESE) Member of the EUROVOLC citizen-science initiative Collaborator with European volcano observatories Her research focuses on computational geophysics, volcanic hazard modeling, and operational monitoring systems. She investigates tephra dispersion dynamics, lava flow behavior, and probabilistic hazard assessment using high-performance computing. Her work emphasizes European volcanic systems—particularly Icelandic eruptions—and integrates multidisciplinary approaches to improve crisis management protocols and public safety during volcanic events. Analysis of her recent publications reveals a strong trend toward operationalizing advanced computational methods for real-time hazard assessment. Key developments include exascale computing applications for solid earth simulations, refinement of the Aviation Colour Code system for aviation safety, and citizen-science data integration for eruption monitoring. These efforts consistently target practical risk mitigation strategies during active volcanic crises like the 2021 Fagradalsfjall eruption. Scientific awards: None mentioned in the provided text. While specific advised students or individual grants aren't documented, her leadership in major European projects (ChEESE, EUROVOLC) demonstrates substantial involvement in funded research initiatives. These projects coordinate transnational collaborations involving observatories, research centers, and emergency management agencies across Europe. Barsotti actively participates in integrated European volcano infrastructure teams, contributing to standardized monitoring protocols, hazard communication frameworks, and crisis response systems. Her work bridges institutional observatories (e.g., Icelandic Meteorological Office, INGV sections) with community-based monitoring tools, enhancing data collection and public engagement during volcanic unrest.

Colin Capraniمشاهده پروفایل

دانشیار

• Bridge Engineering
• Structural Reliability
• Vibration Serviceability
• +۳ مورد دیگر

Dr. Colin Caprani is an Associate Professor and Head of Structural Engineering at Monash University's Department of Civil and Environmental Engineering. His research focuses on bridge traffic loading, structural reliability, vibration serviceability, and Intelligent Transportation Systems. He holds qualifications including a PhD, BSc(Eng), and DipEng, and is a Chartered Professional Engineer (CPEng) and Chartered Structural Engineer (CEng). He has contributed to editorial roles for journals like Advances in Structural Engineering and Computers & Concrete , and led community initiatives such as mini-symposiums on bridge loading and maintenance. His professional affiliations include the Institution of Structural Engineers and the International Association for Bridge and Structural Engineers. Recent projects include the ARC Research Hub for Nanoscience-based Construction Materials and studies on pavement behavior. Awards include the 2017 Best Paper Award and 2021 Institution of Structural Engineers Research Award. Teaching commitments span courses like CIV2226 and CIV4210, emphasizing structural analysis and bridge engineering.

• Blockchain Technology
• Internet of Things (IoT)
• Cybersecurity
• +۲ مورد دیگر

Dr. Volkan Dedeoglu is an active researcher at Queensland University of Technology (QUT), specializing in blockchain technology and IoT systems within the School of Computer Science. His work focuses on developing privacy-preserving frameworks and trust architectures for distributed systems. Research Focus: Blockchain applications in IoT and cyber-physical systems Privacy-preserving data sharing and threat intelligence Decentralized trust and reputation management Secure data aggregation and marketplace frameworks His recent work explores cutting-edge applications like CypherChain for privacy-preserving data aggregation in blockchain-based demand response programs and Priv-Share for differential privacy in cyber threat intelligence sharing. These publications demonstrate a consistent focus on bridging theoretical blockchain innovations with practical cybersecurity challenges in IoT ecosystems. Collaborative Research: Dr. Dedeoglu frequently collaborates with QUT colleagues including Raja Jurdak, Salil Kanhere, and Sidra Malik, indicating active participation in QUT's distributed systems and cybersecurity research groups.

David Langeمشاهده پروفایل

دانشیار

• Fire Safety Engineering
• Structural Engineering
• Risk Analysis
• +۳ مورد دیگر

Associate Professor David Lange serves as Deputy Director of Higher Degree by Research at the School of Civil Engineering , University of Queensland . With academic qualifications from the University of Edinburgh (PhD and MSc), he specializes in fire safety engineering and structural engineering , focusing on timber buildings, lithium-ion battery fire safety, and risk analysis techniques. Chartered Engineer (CPEng), Fellow of Engineers Australia (FIEAust) Registered Professional Engineer of Queensland (RPEQ) APEC Engineer and International Professional Engineer (IntPE) His research explores fire behavior in mass timber structures , vertical fire spread , and infrastructure resilience . Publications highlight his expertise in probabilistic modeling, fire dynamics, and structural response to extreme conditions. Recent collaborative studies examine flame spread in ventilated façades, compartment fire behavior, and risk evaluation frameworks. He actively supervises PhD projects in areas like fire safety of low-carbon concrete, diagrid structures, and physics-informed machine learning for fire modeling.

David Wainwrightمشاهده پروفایل

پژوهشگر ارشد

• Coastal Engineering
• Environmental Hydraulics
• Coastal Geomorphology
• +۳ مورد دیگر

David Wainwright is an adjunct research fellow at the University of Queensland's School of Civil Engineering and a adjunct lecturer at the University of Newcastle. With over 20 years of industry experience as a consulting engineer, he specializes in coastal engineering , environmental hydraulics , and climate change adaptation , particularly in coastal zones. University: University of Queensland Secondary Affiliation: University of Newcastle Academic Rank: Research Fellow His research focuses on coastal geomorphology , numerical modeling of coastal processes, and risk assessment for coastal flooding and sea level rise. He has developed frameworks for probabilistic hazard lines , coastal lagoon management , and stochastic erosion modeling . His work integrates engineering design with policy considerations, including local government planning and community consultation . A chartered engineer with Engineers Australia since 2001, he contributes to coastal risk evaluation and climate adaptation guidance . He founded the consulting firm Salients Pty Limited in 2015, extending his expertise to state and national projects .

Nicole Kessissoglouمشاهده پروفایل

استاد

• Mechanical Engineering
• Dynamics
• Vibration and Vibration Control
• +۵ مورد دیگر

Professor Nicole Kessissoglou is a distinguished academic in the School of Mechanical and Manufacturing Engineering at the University of New South Wales (UNSW Sydney). She holds the position of Professor and conducts her research within the Ainsworth Building (J17), Level 4, Room 408 at the Kensington Campus. Her academic profile is prominently featured on UNSW's research portal, highlighting her expertise in structural vibration, acoustics, and fluid-structure interaction. Professor Kessissoglou's research interests span across several critical areas of mechanical engineering, with a particular focus on Structural vibration and transmission , Acoustics , Structure-fluid coupling , Active vibration control , and Active structural acoustic control . Her work falls within the broader Fields of Research categories of Mechanical Engineering, Dynamics, Vibration and Vibration Control, and Acoustics and Noise Control. Her research addresses fundamental challenges in how structures interact with fluids and how sound and vibrations propagate through these systems. Analysis of her most recent publications (2023-2025) reveals a strong focus on wave phenomena in periodic and resonant systems, with significant attention to underwater applications. Her work spans theoretical modeling, numerical simulation, and experimental validation of acoustic and vibration phenomena. Key themes include structural cloaking technology, uncertainty quantification in resonant systems, vibroacoustic responses of coated cylindrical shells, and novel acoustic metamaterial designs. Her research has important applications in naval engineering, underwater acoustics, and noise control technologies. Professor Kessissoglou maintains an extensive publication record with 140 journal articles, 9 book chapters, 187 conference papers, and various other scholarly outputs. Her work demonstrates a consistent trajectory of advancing knowledge in structural acoustics and vibration control, with increasing focus on metamaterial applications and hybrid active-passive control systems in recent years. Her research activities appear to be well-integrated within UNSW's broader engineering research ecosystem, particularly within the university's strengths in aerospace, structural engineering, and technology for good initiatives. While specific grant information isn't detailed in the available materials, her publication output suggests sustained research funding and active collaboration with other researchers in her field.

Georg Gottwaldمشاهده پروفایل

استاد

• Dynamical Systems
• Geophysical Fluid Dynamics
• Climate Modeling
• +۵ مورد دیگر

Professor Georg Gottwald is a distinguished academic in the School of Mathematics and Statistics at the University of Sydney, where he has been a faculty member since 2002, progressing from Lecturer to his current position as Professor since 2013. He also holds a Visiting Professor position at the University of Surrey in the UK since 2013. His extensive research career spans dynamical systems theory, geophysical fluid dynamics, and the intersection of machine learning with complex systems. Professor Gottwald's research focuses on dynamical systems theory as an abstract formalism for studying systems evolving in time and space. His work has significant applications across diverse fields including climate modeling, biological systems, and complex networks. He is particularly known for developing methods for model reduction of complex dynamical systems, stochastic modeling approaches, and the application of machine learning techniques to dynamical systems. His research aligns with the Faculty of Science Research Strengths in Understanding the Universe, Fundamental Laws of Nature, Complex Systems, Climate and Environmental Change, Data and Decisions, and National Security. His most recent publications demonstrate a strong trajectory toward integrating machine learning with dynamical systems theory, particularly in developing stable generative models, learning dynamical systems with random feature maps, and combining data assimilation with machine learning for forecasting. His work spans pure mathematical theory to practical applications in climate science, finance, and biological systems, showing remarkable breadth while maintaining deep mathematical rigor. Future Fellowship, 'Stochastic methods in mathematical geophysical fluid dynamics', Australian Research Council, 2010-2014 Australian Research Fellowship, 'Stochastic methods in mathematical geophysical fluid dynamics', Australian Research Council, 2010-2015 (declined) Australian Research Fellowship, 'Geometric methods in geophysical fluid dynamics', Australian Research Council, 2004-2009 Professor Gottwald has successfully supervised numerous PhD and Master's students who have gone on to academic and industry positions worldwide. His current research group includes postdocs and PhD students working on machine learning for dynamical systems, stochastic model reduction, physics-informed machine intelligence, and tensor methods for scientific machine learning. He has secured multiple ARC Discovery Project grants and has been involved in significant international collaborative research projects. He is actively involved with the Sydney Dynamics Group, which he co-founded in 2007, fostering collaboration between the University of Sydney and UNSW. Professor Gottwald maintains strong editorial commitments as Associate Editor for Geophysical and Astrophysical Fluid Dynamics, SIAM Journal of Applied Dynamical Systems, and Journal of Computational Dynamics, and serves on the Editorial Advisory Board for Chaos and the Editorial Board for Physical Review E. His professional activities demonstrate leadership in the dynamical systems community through organizing workshops, seminars, and special journal issues.

Mike Le Pelleyمشاهده پروفایل

استاد

• Cognitive Psychology
• Attention
• Learning
• +۷ مورد دیگر

Professor Mike Le Pelley is a cognitive psychologist working in the School of Psychology at the University of New South Wales (UNSW). His research program spans multiple domains of cognitive science, focusing on the intersection of mind, brain and behavior. Professor Le Pelley's work aims to characterize the mental processes underlying human cognition and how these processes influence ongoing behavior in both healthy individuals and those with psychiatric conditions. His primary research focus is the relationship between attention and learning. Specifically, he investigates how attention changes as a result of learning about relationships between events. For example, as described in his profile, when driving, individuals prioritize the road ahead while trying to ignore distractions like squabbling children in the back seat. Professor Le Pelley's research examines the mechanisms that govern such attentional prioritization. While the core of his research focuses on fundamental processes in healthy cognition, he also studies how dysfunction in these processes contributes to conditions such as addiction and schizophrenia. His work has significant implications for understanding cognitive processes in both healthy individuals and those with psychiatric conditions. Professor Le Pelley has published extensively on topics including attentional capture by reward-related stimuli, associative learning, and the neural mechanisms underlying cognitive processes. His research frequently employs eye-tracking and neurophysiological methods to examine cognitive processes. His recent publications (2023-2025) demonstrate a clear research trajectory examining how reward systems influence attentional mechanisms. A significant portion of his work investigates how outcome variance, rather than just reward value, modulates attentional priority. His research on inner speech has provided important neurophysiological evidence regarding motor preparation and how this process is disrupted in schizophrenia. Professor Le Pelley's work consistently bridges basic cognitive mechanisms with clinical applications, particularly in understanding addiction and schizophrenia. Professor Le Pelley has made substantial contributions to theoretical frameworks in cognitive psychology, particularly in the areas of associative learning and attention. His 2010 book 'Attention and Associative Learning: From Brain to Behaviour' (co-edited with Chris Mitchell) is a significant contribution to the field. His collaborative work extends across multiple institutions and disciplines, reflecting the interdisciplinary nature of his research.

• Artificial Intelligence
• Robotics
• Manufacturing Systems
• +۵ مورد دیگر

Professor Will Browne is the Chair in Manufacturing Robotics at QUT, collaborating with the ARM Hub and CSIRO to advance robotics in manufacturing, healthcare, and industry. He holds a Doctorate in Engineering from the University of Wales and has over 30 years of expertise in AI, robotics, and Learning Classifier Systems (LCS). His research focuses on transparent AI systems like LCS for Explainable AI (XAI), human-robot collaboration, and advanced manufacturing solutions. He co-leads the $5M SfTI Robotics Spearhead project and is internationally recognized for LCS contributions, including co-authoring the first LCS textbook with Ryan Urbanowicz. Publications emphasize emotion recognition, embodied AI, and scalable machine learning. His work bridges academia and industry through the ARM Hub, fostering innovation in robotics and design-led manufacturing. Key roles include co-track chair at GECCO (2011–2025) and editorial board memberships. Research spans evolutionary computation, robotics control, and energy systems optimization.

• Combinatorics
• Latin squares
• Matrix permanents
• +۵ مورد دیگر

Prof Ian M. Wanless is a Professor in the School of Mathematics at Monash University, Melbourne, Australia. He has held academic positions at institutions including the Australian National University (ANU), University of Melbourne, Christ Church Oxford, and Charles Darwin University. His research primarily focuses on combinatorics, with specializations in Latin squares, matrix permanents, graph theory, and algebraic structures. He has made significant contributions to the enumeration and properties of Latin squares, including groundbreaking work on transversals, orthogonality, and symmetry. Wanless has also explored connections between Latin squares and algebraic structures like quasigroups and loops. His education includes a PhD from ANU (supervised by Brendan McKay) and postdoctoral fellowships at Oxford and ANU. He has been awarded an Australian Research Council Future Fellowship (2011) and has led major research initiatives, including organizing international conferences (e.g., 5ICC in 2017). His work spans theoretical results and computational methods, with over 100 publications in top journals like Journal of Combinatorial Theory and SIAM Journal on Discrete Mathematics . Wanless’s research interests extend to design theory, hypergraphs, and permutation polynomials. He co-edits the Electronic Journal of Combinatorics and has held leadership roles in professional societies, including president of the Combinatorial Mathematics Society of Australasia. His current projects include studies on perfect 1-factorizations, covering radii of permutation sets, and algebraic properties of Latin squares.