جست‌وجوی استاد و پژوهشگر

• Mobile Robotics
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Dr. Tobias Low is a Senior Lecturer in Mechanical Engineering (Mechatronics) at the School of Engineering, University of Southern Queensland. He holds a BEng and PhD from Queensland institutions. With a career spanning over 14 years at USQ, he specializes in Mobile Robotics, Computer Vision, and Mechatronics. His research integrates autonomous systems, tactile sensing, and robotic navigation in agricultural and industrial contexts. Key research areas include robotic obstacle avoidance, deep learning applications in vision systems, and sensor technologies for precision agriculture. He has published extensively on topics like tactile feedback in robotic meat processing, stereo vision for leaf segmentation, and autonomous robot navigation in livestock environments. His work often bridges mechanical engineering with interdisciplinary fields such as nanotechnology (e.g., biodegradable nanofiber materials for fertilizer delivery) and environmental monitoring. Notable projects include developing tyre strain sensing systems for slip detection and integrating WSNs with mobile robots for agricultural applications. Dr. Low’s contributions span theoretical advancements (e.g., transfer learning in crack detection) and applied engineering solutions, reflecting a commitment to practical robotics and sensor innovation.

Zifan Linمشاهده پروفایل

مدرس

• Image Enhancement
• Power Electronics
• Inverter Technology
• +۳ مورد دیگر

Zifan Lin is a Lecturer and PhD student in the Multi-Discipline Doctor of Philosophy program at the School of Engineering, University of Western Australia. His research focuses on power electronics, underwater imaging, and control systems, with notable contributions to inverter technology, DC microgrid systems, and machine learning applications in vision systems. He actively collaborates on projects spanning Australia and other regions, addressing challenges in renewable energy integration and advanced image processing. His academic background includes multi-disciplinary training in engineering, complementing his teaching duties. Research interests emphasize practical solutions for energy systems (e.g., DC-DC converters, NPC inverters) and underwater image enhancement networks like TANet and ReX-Net. Recent work explores semantic feature refinement for mask detection and robust control schemes for electric motors using reinforcement learning. Publications highlight innovations in space vector modulation strategies, finite-set model predictive control, and agricultural drying technologies. While no specific awards are listed, his prolific output in top journals like IEEE Transactions and LWT-FOOD SCIENCE AND TECHNOLOGY reflects academic recognition. His advising and grant activities remain unspecified in available data.

Mark Jessellمشاهده پروفایل

استاد

• Tectonics
• Metallogenesis
• 3D Geological Modelling
• +۵ مورد دیگر

Mark Jessell is a Professor and Western Australian Fellow at the School of Earth and Oceans, University of Western Australia. He leads the WAXI & SAXI Projects and co-leads the MinEx CRC's Automated 3D Geological Modelling Project with Dr. Mark Lindsay. His research focuses on tectonics, metallogenesis, and 3D geological modelling, particularly in the West African and Guyanese Cratons. He has developed open-source QGIS plugins for geological analysis and digital mapping, including Map2Loop and GEOL-QMAPS. Education: MSc (UK), PhD (USA), Postdoc (Melbourne), and positions at Monash University and IRD (France). Research Interests: Microstructure studies, geology-geophysics integration, and structural geophysics. Industrial Relevance: Projects funded by AMIRA, MinEx CRC, and ARC, with a focus on resource exploration and risk analysis. Awards: GSA Bruce Hobbs Medal, Western Australian Fellowship, and MinEx CRC Publication Prizes. Engagement: Co-founded Agate Project Ltd to enhance Earth science education in Africa. Recent work includes multi-disciplinary studies of Paleoproterozoic tectonics in West Africa and advancements in 3D modelling algorithms. His publications span geophysical data fusion, machine learning applications, and lithospheric mantle analysis.

• Corrosion Science
• Materials Science
• Electrochemistry
• +۳ مورد دیگر

Professor Paul Weiss is an Adjunct Professor at RMIT University's School of Science. His work focuses on corrosion science, materials engineering, and electrochemical systems. He is affiliated with RMIT's City Campus in Australia. Research interests include corrosion inhibitor screening, high-throughput methodologies, and material degradation mechanisms. Recent studies emphasize electrochemical testing, nanomaterials, and sustainable corrosion prevention strategies. His publications span topics like steel corrosion characterization, aluminum alloy protection, and bio-corrosion mitigation using quorum-sensing inhibitors. No awards or student supervision details are explicitly listed. His research integrates advanced analytical techniques like spectral unmixing and artificial neural networks for material evaluation. Collaborations likely involve industrial and academic partners in corrosion prevention and nanotechnology.

• Optimization
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Dr. Minh Dao is a Senior Lecturer in the Department of Mathematics at RMIT University's School of Science. His research focuses on optimization theory, algorithm design, and their applications in signal processing, machine learning, and wireless communications. He has contributed to advancements in distributed optimization, nonconvex programming, and federated learning frameworks. Key research interests include operator splitting methods, DC programming, and mathematical modeling for energy-efficient systems. He has published extensively in top-tier journals and conferences such as the European Journal of Operational Research and IEEE Transactions on Intelligent Transportation Systems. Dr. Dao supervises research projects on distributed optimization for federated learning, reboot sensing MIMO systems, and DC programming approaches for clustering problems. His work integrates theoretical analysis with practical applications in telecommunications and energy networks.

• Cryptography
• Machine Learning
• Database Systems
• +۳ مورد دیگر

Michael Brand is an Adjunct Professor at RMIT University's School of Computing Technologies. His research focuses on cryptography, privacy-preserving technologies, machine learning, and algorithm design. Key areas include zero-knowledge proofs, secure financial systems like FinTracer, and computational theory. His work bridges theoretical foundations with practical applications in data security and real-time processing systems. Research interests span advanced cryptographic protocols, privacy-enhancing machine learning techniques, and distributed computing paradigms. Notable contributions include innovations in blockchain tracing mechanisms and foundational work in statistical estimation theory. He actively publishes in top-tier venues, addressing challenges in secure computation, network analysis, and database systems. Collaborations emphasize interdisciplinary solutions to modern computational and security challenges.

• Pipeline Engineering
• Computational Geomechanics
• Geotechnical Earthquake Engineering
• +۵ مورد دیگر

Dr. George (Georgios) Kouretzis is an Associate Professor in the School of Engineering at the University of Newcastle. His research focuses on pipeline engineering, soil-structure interaction, computational geomechanics, and geotechnical earthquake engineering. He has contributed to the development of practical design tools for buried pipelines affected by geohazards and seismic events. His work integrates advanced numerical simulations with experimental methods, such as physical modeling and vane shear testing. Educations: PhD in Geotechnical Engineering, National Technical University of Athens (NTUA), Greece (2005) MSc in Civil Engineering, NTUA, Greece Diploma in Civil Engineering, Democritus University of Thrace, Greece Research Interests: Onshore pipeline infrastructure safety under environmental and seismic hazards Soil dynamics and computational geomechanics Seismic design of tunnels and geotechnical structures Advanced constitutive models for soft soils Dr. Kouretzis has led major projects funded by the Australian Research Council (ARC), focusing on environmental impacts on pipelines and laboratory apparatus development. He collaborates internationally, aiming to establish a national buried pipeline research hub. His awards include recognition for research and teaching excellence from IACMAG and ACCM. He serves as an associate editor for the Canadian Geotechnical Journal and actively reviews for leading journals and funding bodies. His academic roles include course coordination for geotechnical engineering courses and supervision of postgraduate students. Parallel to academia, he provides expert consultancy for energy and transportation infrastructure projects, including pipelines and seismic-resistant tunnels. He is a member of the Australian Geomechanics Society and served as Secretary of its NSW-Newcastle Chapter.

• Robotics
• Medical Robotics
• Collaborative Robotics
• +۵ مورد دیگر

Dr. Mats Isaksson is a Professor in the Department of Mechanical and Product Design Engineering at Swinburne University of Technology. He holds leadership roles as Director of the Intelligent Robotics Lab and leader of the Intelligent Robotics Program under the Manufacturing Futures Platform. His career includes roles at ABB Robotics, Deakin University, and Colorado State University. Education: Ph.D. in Robotics, Deakin University (2013) M.Sc. in Engineering Physics, Lund University, Sweden (1997) Research Interests: Medical robotics, collaborative robotics, manufacturing automation, agricultural robotics, control systems, and parallel kinematics. His work bridges robotics with industry applications, emphasizing automation, ergonomics, and sustainability. Publications: Recent work focuses on autonomous ultrasound imaging, ergonomic surgical tools, and robotics in agriculture/medicine. Key themes include sensor fusion, robotic surgery ergonomics, and additive manufacturing for repair solutions. Awards: Outstanding Reviewer Award, Mechanism and Machine Theory Journal (2017) EURON/EUnited Robotics Technology Transfer Award (2004) Grants & Leadership: Leads ARC Training Centre for Collaborative Robotics in Advanced Manufacturing. Active in industry partnerships (e.g., Ford, Haemograph) and research on cobotics, smart materials, and sustainable manufacturing. Supervises multiple PhD projects in robotics, medical devices, and automation. Labs & Initiatives: Founded the Intelligent Robotics Lab (2021) and contributes to the Australian Cobotics Centre. Projects include autonomous vertical farming systems and robotic solutions for automotive repair.

• Survey Methodology
• Multilevel Modelling
• Poststratification
• +۳ مورد دیگر

Dr Lauren Kennedy is a Lecturer at the University of Adelaide's School of Computer and Mathematical Sciences, Department of Mathematical Sciences. Her research focuses on survey methodology, multilevel modeling, poststratification, causal inference, and Bayesian statistical techniques. She specializes in addressing challenges arising from non-representative data and improving inference in social sciences through advanced statistical methods. Her work emphasizes practical applications in public opinion analysis, epidemiological modeling, and policy evaluation. Dr Kennedy is actively involved in supervising postgraduate students in Masters and PhD programs, particularly as a co-supervisor. She maintains an office in 6.56 Ingkarni Wardli Building on North Terrace campus and can be contacted at lauren.a.kennedy@adelaide.edu.au . Key research contributions include innovations in Bayesian workflow, cross-validation methodologies, and the integration of machine learning with traditional survey techniques. Her recent publications highlight advancements in causal inference frameworks and hierarchical modeling approaches for large-scale datasets.

• Spatial Statistics
• Neural Networks
• Time Series Analysis
• +۳ مورد دیگر

Dr. Pratik Nag is a Research Fellow at the National Institute of Applied Statistical Research Australia (NIASRA), University of Wollongong. He holds a PhD from King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2020–2024). His research focuses on integrating advanced statistical methods with machine learning to address challenges in predictive modeling, spatial statistics, and uncertainty quantification. He collaborates with prominent figures like Prof. Noel Cressie, Prof. Andrew Zammit Mangion, and Prof. Sumeetpal Singh. His work emphasizes coupling neural networks with traditional statistical techniques to solve real-world problems in environmental and spatial domains. Dr. Nag’s expertise includes neural networks, spatial and time series modeling, and large-scale data analysis. He has contributed to developing methods like DeepKriging for spatial interpolation and probabilistic forecasting. His peer-reviewed contributions span journals such as , , and . He actively reviews manuscripts for top-tier journals in computational statistics and environmental data science. His current research addresses extreme-scale environmental modeling, nonstationary covariance estimation, and generative adversarial networks for creative applications. Dr. Nag is affiliated with the University of Wollongong’s School of Mathematics and Applied Statistics and collaborates on high-performance computing initiatives for geospatial analysis.

Steve Kerrisonمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Internet of Things (IoT)
• Public Key Infrastructure (PKI)
• +۴ مورد دیگر

Dr. Steve Kerrison is a Senior Lecturer in Cybersecurity at James Cook University (Singapore Campus). He holds a PhD and MEng in Computer Science from the University of Bristol, and certifications including CISSP and CCSP. His expertise spans IoT cybersecurity, embedded systems, and energy-efficient computing. Education: PhD in Computer Science, University of Bristol (2010-2015) MEng in Computer Systems Engineering, University of Bristol (2005-2009) CISSP and CCSP certifications (ISC)² Research Interests: Focuses on IoT cybersecurity, PKI for IoT, energy-efficient computing, and Industry 4.0. Collaborations include EU-funded projects (ENTRA, ICT-Energy) and Singapore’s National Cybersecurity R&D Program. Teaching: Specializes in open-source tools and community-driven cybersecurity education. Formerly a Senior Research Associate at Bristol and CTO at MICROSEC, a Singapore-based IoT security startup.

Roland Goeckeمشاهده پروفایل

استاد

• Affective Computing
• Computational Behaviour Analysis
• Social Signal Processing
• +۶ مورد دیگر

Professor Roland Goecke is a leading academic in affective computing and head of the School of Systems & Computing at UNSW Canberra. He holds a PhD in Computer Science from the Australian National University (2004) and has extensive experience in roles such as Professor at the University of Canberra and Senior Research Scientist at Seeing Machines. His research focuses on algorithms for human-computer interaction, mental health applications, and multimodal signal processing. Key research interests include affective computing, computational behavior analysis, and AI-driven solutions for health and well-being. He has secured funding from ARC, NHMRC, and US NSF, and serves on editorial boards of IEEE Transactions on Affective Computing and Pattern Recognition . His work spans mental health, video/image analysis, and assistive robotics. Prof. Goecke is an experienced research supervisor, guiding students in areas like human-robot interaction and computer vision. He is a senior member of IEEE and actively contributes to the academic community through conferences and mentorship. Current affiliations include UNSW’s Canberra campus, where he leads interdisciplinary research initiatives. His lab focuses on translating computational methods into real-world applications for health monitoring and human performance analysis.

• Operating systems
• Distributed systems
• Computer Science
• +۳ مورد دیگر

Professor Willy Zwaenepoel is a distinguished academic and Dean of the Faculty of Engineering at the University of Sydney. He holds Fellowships from ACM, IEEE, and ATSE. His research focuses on distributed systems, operating systems, and experimental computer science. Previously, he spent two decades at Rice University and nine years as Dean of EPFL's School of Computer and Communication Sciences before joining Sydney in 2018. Education: BS/MS, Ghent University (1979) MS/PhD, Stanford University (1980/1984) Research Interests: His work emphasizes distributed systems and operating systems, with contributions to key-value stores, transactional systems, and large-scale graph processing. Recent projects include optimizing geo-replicated systems and improving datacenter scheduling efficiency. Articles Trends: Recent publications address OS scheduling (Nest), distributed graph mining (Tesseract), and transactional systems' performance limits. He explores hardware-software co-design for multicore systems and energy-efficient data centers. Awards: Fellow of ACM (2021) Fellow of IEEE (2020) Fellow of ATSE (2019) Grants & Advising: Active grants include adaptive key-value store research (2021) and large-graph processing systems (2018). He advises PhD students and postdocs on distributed systems and storage challenges. Labs/Teams: Leads the Sydney systems research group focusing on scalable distributed systems and cloud infrastructure.

• Privacy-Preserving Machine Learning
• Reinforcement Learning
• Data Privacy and Security
• +۵ مورد دیگر

Associate Professor Kee Siong Ng is affiliated with the School of Computing at the Australian National University (ANU). His research focuses on privacy-preserving technologies, reinforcement learning, distributed systems, and blockchain applications. He has contributed extensively to areas such as privacy-preserving machine learning, federated learning, entity resolution, and scalable database systems. His work emphasizes balancing computational efficiency with privacy guarantees in data-driven environments. Key research contributions include methodologies for secure data processing in federated learning frameworks, privacy-preserving reinforcement learning for population-level systems, and blockchain-based digital identity solutions. He has led projects like Integrated Graph Analytics and contributed to initiatives involving the Australian Medicare dataset. His research often intersects theoretical foundations with practical implementations, addressing challenges in scalability and real-world applicability. Ng has published over 24 peer-reviewed articles, with notable works appearing in venues like IEEE Transactions on Parallel and Distributed Systems and Transactions on Machine Learning Research . His articles frequently explore cutting-edge topics such as differential privacy, approximation algorithms, and multi-agent systems. Collaborations include industry partnerships and interdisciplinary efforts involving health informatics and financial intelligence. His projects include Integrated Graph Analytics (2018–2021): Focused on scalable graph-based data analysis. Translational Fellowship (2018–2022): Bridging theoretical research with practical applications. Research on Data Sets for Health and Pharmaceutical Schemes (2020): Analyzing Medicare and pharmaceutical data with privacy safeguards. Ng's work prioritizes ethical AI and privacy-by-design principles, with a focus on real-world deployment challenges in distributed and federated systems.

Robert Lanfearمشاهده پروفایل

مدرس ارشد

• Molecular Evolution
• Phylogenetics
• Bioinformatics
• +۵ مورد دیگر

Robert Lanfear is a Senior Lecturer at the Australian National University (ANU), affiliated with the Division of Ecology and Evolution. He holds a PhD from the University of Sussex (2008) and has held postdoctoral positions at ANU and Macquarie University. His research focuses on molecular evolution, phylogenetics, and bioinformatics, with a particular emphasis on somatic mutations in plants and the development of phylogenetic software tools like PartitionFinder and IQ-TREE. Education: BSc and Honors in Ecology, MSc in Artificial Intelligence, PhD in Developmental Biology (University of Sussex). His work integrates comparative methods, genomics, and computational approaches to understand mutation dynamics, evolutionary processes, and the development of robust analytical tools for evolutionary studies. Research interests include plant somatic mutation accumulation, genomic evolution in plants, and statistical methods for phylogenetic analysis. He leads projects on scalable phylogenetic methods for large genomic datasets and collaborates on real-time genomic epidemiology. His software contributions include IQ-TREE, DecentTree, and RWTY, which are widely used for phylogenetic inference and analysis. Supervision: Actively recruiting students for research on plant somatic mutations and molecular evolution. Projects span fieldwork, genomic sequencing, and computational modeling. Labs/Teams: Associated with ANU’s Ecology and Evolution research groups, focusing on bioinformatics and genomics. Collaborations include software development for evolutionary analysis and pandemic genomic studies.