جست‌وجوی استاد و پژوهشگر

• Biostatistics
• Public Health
• Epidemiology
• +۹ مورد دیگر

Dr. Shuvo Bakar is a Senior Lecturer in the Sydney School of Public Health at the University of Sydney, within the Faculty of Medicine and Health. He holds a PhD in Statistics from the University of Southampton, UK, and has prior experience as an Assistant Professor at Yale University, Lecturer at the Australian National University, and Scientist at Data61 (CSIRO). His research focuses on statistical methods applied to public health challenges, including Bayesian hierarchical modeling, machine learning, spatio-temporal analysis, and their applications in epidemiology, clinical trials, and environmental health. Dr. Bakar's research interests span statistical methodologies such as Bayesian adaptive designs, small area estimation, and spatial risk modeling, alongside applications in child health, infectious diseases, and extreme weather impacts on health. He is an active member of academic communities, including the Royal Statistical Society (RSS Fellow), Statistical Society of Australia, and the Australian Trials Methodology Research Network. His work also involves collaborations on grants totaling millions in funding, addressing topics like climate change impacts on health inequity and cardiovascular disease prevention in remote regions. Education: PhD in Statistics (University of Southampton, UK) Key Research Themes: Obesity, Diabetes, Cardiovascular Disease; Reproductive, Maternal & Child Health Grants/Projects: Includes NHMRC-funded trials on respiratory infections in First Nations children and MRFF grants for cardiovascular risk reduction in regional Australia. Dr. Bakar's contributions extend to editorial roles for Nature Scientific Reports and Discover Public Health , and his research has been published in journals like PloS One , Climatic Change , and Journal of the Royal Statistical Society .

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).

• 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 .

• Energy and Power Systems
• Renewable Energy Integration
• Power System Stability
• +۱۳ مورد دیگر

Evan Franklin is an Associate Professor in Energy and Power Systems within the School of Engineering at the University of Tasmania. He also serves as Associate Head of Research, reflecting his leadership in advancing engineering research at the institution. His academic work is centered on modern power systems with a strong emphasis on renewable integration, grid stability, and sustainable energy technologies. His primary research interests include energy and power systems, renewable energy integration, grid frequency control, harmonic analysis, distributed energy resources (DER), battery and compressed air energy storage, agrivoltaics, and hydrogen integration. His work bridges engineering fundamentals with real-world applications in sustainable energy systems, contributing to Australia's transition toward clean energy. The recent publications of Dr. Franklin span high-impact journals such as Energies , IEEE Transactions on Industry Applications , Renewable and Sustainable Energy Reviews , and Journal of Energy Storage . The research trends reflect a strong focus on power system stability, microgrid control, harmonic mitigation, and innovative energy storage solutions. His work increasingly integrates AI and machine learning techniques for power quality and system monitoring, while also exploring interdisciplinary applications like agrivoltaics and offshore energy systems. Dr. Franklin has successfully supervised both PhD and Master’s students, including Ahmadreza Eslami and Md Ruhul Amin, with research topics ranging from harmonic analysis to frequency control using battery storage. He has secured substantial research funding from major national and international bodies, including the Australian Research Council (ARC), Australian Renewable Energy Agency (ARENA), CSIRO, and the Blue Economy CRC. Notable projects include the ARC Training Centre in Energy Technologies for Future Grids, MoorPower wave energy projects, and studies on hydrogen integration and black-start capabilities. He leads and participates in research teams focused on renewable energy systems, including the Centre for Renewable Energy and Power Systems at UTAS. His collaborative network includes key researchers such as Professor Michael Negnevitsky, industry partners like Carnegie Clean Energy and TasNetworks, and government agencies including Hydro Tasmania and Aurora Energy. His work is instrumental in shaping resilient, sustainable, and intelligent power systems for the future.

• 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.

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.

Mythreyi Velmuruganمشاهده پروفایل

عضو هیئت علمی

• Data Science
• Machine Learning
• Information Systems
• +۴ مورد دیگر

Dr. Mythreyi Velmurugan is an Associate Lecturer in the School of Information Systems at Queensland University of Technology (QUT), within the Faculty of Science. She serves as ECR Co-leader for the Health and Human Biology Theme. Her PhD focused on Explainable AI techniques for tabular data, emphasizing transparency in machine learning. She holds a PhD from QUT and is a member of Women in Technology (WiT) and an Associate Fellow of the Higher Education Academy (AFHEA). Education: PhD in Information Systems (Queensland University of Technology) Research Interests: Her work bridges data analytics and process science, with focuses on: Explainable AI (XAI) for tabular data Transparent machine learning models Predictive process analytics Healthcare social support systems Online community dynamics Publications Trends: Recent work emphasizes: Functionally-grounded evaluation frameworks for XAI Stability/fidelity of post-hoc explanations Healthcare community analysis (e.g., carer support systems) Interpretable probabilistic models for black-box demystification Professional Engagement: Women in Technology (WiT) member AFHEA certification ORCID/Google Scholar profiles maintained

• 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.

Shaoyu Zhaoمشاهده پروفایل

پژوهشگر ارشد

• Advanced composite structures
• Mechanical metamaterials
• Graphene nanocomposites
• +۳ مورد دیگر

Dr. Shaoyu Zhao is a Research Fellow (Level A) at RMIT University's School of Engineering. His research focuses on advanced composite structures, mechanical metamaterials, graphene nanocomposites, and molecular dynamics simulations. He holds an ARC DECRA Fellowship and has over 40 journal publications with 2000+ citations (h-index 26). Awards include the ICES2024 Best Paper Award and 2025 DECRA. He supervises Masters/PhD students in areas like metaconcrete and functionally graded structures. Editorial roles include Early Career Board Member for Engineering Structures (Q1), International Journal of Structural Integrity (Q1), and others. Teaching includes the course MIET1076 - Mechanical Vibrations. His work bridges nanoscale simulations (e.g., graphene interfaces) with macro-scale engineering applications (e.g., 3D-printed composites and metamaterial energy absorption). Research spans multi-physics phenomena in perovskite materials and machine learning-driven material analysis. Key Projects: Metaconcrete composites, origami metamaterials, graphene-reinforced nanocomposites Lab Focus: Multiscale modeling for aerospace composites and smart materials

• Robotics Perception
• Alternative Sensing
• Inertial Fusion
• +۶ مورد دیگر

Teresa Vidal-Calleja is an Associate Professor and Research Director at the Robotics Institute, University of Technology Sydney (UTS). She holds a Deputy Head of School (Research) role in the School of Mechanical and Mechatronic Engineering. Her research focuses on robotics perception, alternative sensing, inertial fusion, SLAM, and applications in manufacturing, agriculture, mining, and construction. Education: B.Sc. Mechanical Engineering (UNAM, 2000), M.Sc. Electrical Engineering (CINVESTAV-IPN, 2002), Ph.D. Automatic Control (Polytechnic University of Catalonia, 2007). Postdoctoral Experience: LAAS-CNRS (France), Australian Centre for Field Robotics (Sydney). Research Interests: Teresa’s work spans robotics perception, sensor fusion, autonomous navigation, and collaborative robotics. She leads projects on next-gen infrastructure robotics (ARC Discovery Grant) and is co-lead of the Australian Cobot Centre. Her innovations target hazardous environments, infrastructure maintenance, and human-robot collaboration. Funded Research: Includes grants for robotic sensors, satellite docking systems, and multi-robot collaboration. Recent projects involve Navantia, the NSW Space Research Network, and industry partnerships. Professional Roles: IEEE Senior Member, Treasurer of the Australian Robotics and Automation Association, and Associate Editor for IEEE Transactions on Robotics and other journals. She chairs conferences like the Australasian Conference on Robotics and Automation. Labs/Teams: UTS Robotics Institute, collaborating with global institutions like ETH Zürich and DLR. Her work integrates robotics with industries such as agriculture and construction, emphasizing sustainability and safety.

Arthur ter Hofstedeمشاهده پروفایل

استاد

• Business Process Automation
• Process Mining
• Data Quality
• +۳ مورد دیگر

Arthur ter Hofstede is a Professor in the Faculty of Science and Head of the School of Information Systems at Queensland University of Technology (QUT). He previously held a Professorship at Eindhoven University of Technology (2010–2018) and has been a Visiting Professor at Tsinghua University (China), Sapienza University of Rome (Italy), and Sun Yat-sen University (since 2015). His research focuses on Business Process Automation (BPM), Process Mining, and Data Quality, with particular emphasis on the YAWL workflow system. He has supervised numerous PhD students in areas like process-data governance and gamified data cleaning. His work bridges academia and industry, addressing challenges in healthcare, insurance, and logistics. Key contributions include frameworks like ProcessProfiler3D for performance visualization and OrgMining 2.0 for organizational model mining. Awards include prestigious visiting scholar appointments and leadership roles in international collaborations. Education: Ph.D. and M.Sc. from Katholieke Universiteit Nijmegen (Netherlands). Research projects include Australian Competitive Grants on topics like cost-aware BPM and liquid process model collections. Notable publications explore event log imperfection patterns, privacy-preserving process mining, and robotic process automation integration. Grants: DP150103356 (Liquid Process Model Collections), DP120101624 (Cost-aware BPM) Labs/Teams: Centre for Data Science, YAWL Development Team International Collaborations: Sapienza University, Tsinghua University, Sun Yat-sen University

• Quantum Sensors
• Quantum Gravimetry
• Quantum Magnetometry
• +۴ مورد دیگر

Professor John Close is a Professor and Head of the Department of Quantum Science within the Research School of Physics and Engineering at the Australian National University (ANU). He has held this professorship since 2008 and previously served as Deputy Director of the School from 2012 to 2016. His leadership roles include Chair of the ANU Defence Working Group and Deputy Chair of ANU Academic Board (2015-2017), alongside being a Senior Fellow of the Higher Education Academy. His educational background includes: PhD in Physics from the University of California, Berkeley (1991) Postdoctoral Fellowship at the University of Washington, Seattle (1991-1994) Alexander von Humboldt Fellowship and Max Planck Research Fellowship at the Max Planck Institut für Strömungsforschung, Göttingen (1994-1998) Close's research centers on harnessing quantum fields to develop advanced quantum sensors for fundamental physics and interdisciplinary applications. His experimental and theoretical work spans quantum gravimetry, magnetometry, and Bose-Einstein condensate systems, with direct applications in mapping archaeological sites, volcanoes, aquifers, and mineral deposits. He actively explores quantum wavelet representations, higher-dimensional information processing, motion simulation, and biological quantum sensing through extensive collaborations with Earth Science, Biology, and Industry partners. Analysis of his recent publications reveals a dominant focus on compact mobile quantum sensing platforms, atom interferometry innovations, and sensor fusion techniques. His work bridges quantum physics with practical navigation, geophysical surveying, and environmental monitoring solutions, emphasizing real-world deployment of quantum technologies for precision measurements in gravimetry and inertial navigation. His scientific recognition includes: Alexander von Humboldt Fellowship (1994-1998) Queen Elizabeth II Fellowship (2000) National Teaching Award for Research Led Education (2006) As a former member of the Australian Research Council College of Experts (2015-2018), Close has significantly influenced national research funding while securing grants for interdisciplinary quantum projects. His educational leadership as Deputy Director of the Research School of Physics and Engineering (2012-2016) demonstrates commitment to research-led teaching, recognized by his national teaching award. Close leads the Atom Laser Research Group, driving experimental work in quantum sensor development and Bose-Einstein condensate applications. His team focuses on translating quantum phenomena into deployable technologies for defense, resource exploration, and environmental monitoring through industry and international academic partnerships.