جستجوی استاد راهنمای دانشگاهی

Associate Professor Guangdong Bai is affiliated with The University of Queensland's School of Electrical Engineering and Computer Science, specifically within the Discipline of Cyber Security and Software Engineering. He holds the academic rank of Associate Professor and is currently on leave. His research focuses on Trustworthy AI, Cyber Security, Software Engineering, and Formal Methods, with notable contributions to privacy compliance, IoT/ICS security, and adversarial machine learning defense mechanisms. Education includes a PhD from the National University of Singapore and undergraduate/master's degrees from Peking University. His work spans theoretical advancements and practical applications, including blockchain-based systems for EV charging and vaccine tracking, adversarial attack frameworks (e.g., GenDroid), and federated learning security. Research interests emphasize AI ethics, privacy-preserving techniques, and securing emerging technologies like autonomous vehicles and smart agriculture networks. Notable projects include AuSSE (autonomous vehicle security framework), CoreLocker (neural network usage control), and PAODING (neural network pruning toolkit). Publications (15 most recent) reflect expertise in adversarial machine learning, federated learning, and IoT security. His work bridges theory and practice, addressing real-world challenges in critical infrastructure and decentralized systems. Labs/teams include collaborations on Cyber Security and Software Engineering at UQ, with supervision of PhD candidates such as Yanjun Zhang (Deakin University) and Kailong Wang (Huazhong University of Science and Technology).

• Trustworthy AI
• Cyber Security
• Software Engineering
• +۵ مورد دیگر

Dr. Zijian Wang is a Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on improving vision model transferability, domain adaptation, and deep learning applications in computer vision and remote sensing. He holds a PhD in Computer Science from The University of Queensland (2023). Research interests include domain adaptation techniques, neural network generalization, unsupervised learning, and applications in satellite imagery analysis, vegetation segmentation, and disaster damage assessment. His work emphasizes practical solutions for open-world domain shifts and efficient data utilization. Recent publications explore transfer learning via feature space perturbation, neural network-based satellite image delivery (MIDNet), and benchmark datasets for model generalization (CIFAR-10-Warehouse). He collaborates on platforms like FluMA for influenza monitoring and RoadAtlas for infrastructure management. Currently advises PhD candidates in vegetation segmentation for Australian wheat data and crop health assessment. Active in top conferences (CVPR, ICCV, NeurIPS) and journals (IEEE TPAMI, TMM).

• Computer Vision
• Deep Learning
• Domain Adaptation
• +۵ مورد دیگر

Dr Ashley Stewart is a Research Fellow at the School of Electrical Engineering and Computer Science , University of Queensland , specializing in Quantitative Susceptibility Mapping (QSM) and Neuroimaging . Her work focuses on developing automated MRI analysis pipelines and improving clinical applications of neuroimaging techniques. Research Interests Development of automated neuroimaging pipelines (e.g., QSMxT, Neurodesk). Quantitative susceptibility mapping for traumatic brain injury and cortical iron quantification. MRI acceleration techniques (CAIPIRINHA, compressed sensing). Integration of deep learning in neuroimaging reconstruction. Clinical translation of QSM for oncology and neurodegenerative disorders. Current Funding 2025-2026: Precision Brain Susceptibility Mapping for Large-Scale Neurogenetic Research 2025-2026: Enabling the Clinical Translation of Neuroimaging Analyses through Automated Benchmarking

• Neuroimaging
• Quantitative Susceptibility Mapping
• Magnetic Resonance Imaging
• +۳ مورد دیگر

Associate Professor Weidong Xiao is affiliated with the School of Electrical and Computer Engineering at The University of Sydney, part of the Faculty of Engineering. He holds a MASc and PhD from the University of British Columbia, Canada. His research focuses on power electronics, photovoltaic systems, and renewable energy integration, with notable contributions to wireless power transfer, DC microgrids, and grid stability. He is a member of the Sydney Southeast Asia Centre and the Net Zero Institute, reflecting his commitment to sustainable energy solutions. His work includes authored books such as Power Electronics Step-by-Step (2020) and Photovoltaic Power System: Modeling, Design and Control (2017). His research addresses challenges in energy conversion efficiency, fault diagnosis in PV systems, and advanced control strategies for distributed energy resources. Key projects include developing novel coil structures for wireless power transfer, predictive control schemes for photovoltaic systems, and resilient microgrid architectures. His publications span journals like IEEE Transactions on Power Electronics and conferences such as AUPEC and IECON. His lab, the Centre for Future Energy Networks, explores next-generation energy systems, emphasizing smart grids and DC power distribution. Collaborations with industry and government bodies aim to translate research into practical solutions for sustainable energy infrastructure.

• Power Electronics
• Photovoltaic Systems
• Renewable Energy Integration
• +۴ مورد دیگر

Lian Xu is a Research Fellow in the Department of Computer Science and Software Engineering at the University of Western Australia (UWA). She holds a PhD from UWA (2021) and specializes in computer vision, deep learning, and annotation-efficient methodologies. Her work focuses on weakly supervised learning, multi-modal data fusion, and applications in healthcare, agriculture, and maritime surveillance. Education: PhD in Computer Science, University of Western Australia (2021) Research Interests: Weakly supervised semantic segmentation Multi-modal learning with vision and language Computer vision applications in healthcare and agriculture Efficient learning strategies for limited labeled data Her research output emphasizes techniques such as auxiliary task integration, token-based transformers, and spectral analysis for hyperspectral imaging. Recent work includes advancements in small object detection for maritime surveillance and crop stress classification using hyperspectral data. Awards: Recipient of the Award for Growth in Innovation and Entrepreneurship (2021) for contributions to applied research. Teaching & Engagement: Facilitated courses on computer vision (CITS4402) and visualization (CITS2401). Co-organizer of workshops at DICTA 2024, ACCV 2022, and VCIP 2021 on small object detection and deep learning limits. Grants: Co-investigator on the Empowering Robots with 3D Vision and Reasoning for Enhanced Home Assistance project (2024–2025).

• Computer Vision
• Deep Learning
• Annotation-efficient learning
• +۵ مورد دیگر

Dr. Sachith Seneviratne is a Research Fellow at the University of Melbourne's Melbourne School of Design, specializing in Computer Vision and Health. His research bridges Machine Learning, Urban Design, and Public Health, with a focus on Deep Learning, Contrastive Representation Learning, and Agent-Based Modelling. He holds a PhD in Machine Learning from Monash University, exploring automated parallelized machine learning models. His work has collaborations with NASA, Harvard University, and Currnt Research Hub. He is affiliated with the Transport, Health and Urban Design (THUD) research group, focusing on 'Data and Value,' 'Future Cities,' and 'Healthy Communities and Infrastructure.' His recent publications span facial deepfake detection, urban design impacts on health, and automated architecture generation using LLMs. Research interests include generative design, automated infrastructure planning, and the ethical implications of facial recognition under privacy constraints (e.g., masked faces). He has pioneered methods for collecting population-level bicycling data via smartphones/Bluetooth beacons and developed novel architectures like Arch-LLM for neural architecture generation. His work on subgrid-informed flood mapping and physics-informed neural networks demonstrates cross-disciplinary innovation. Consulting work includes machine learning projects for NASA and Harvard. His lab teams focus on creating interpretable AI systems for environmental and health applications, such as Ginn-kan for physics simulations and Ginn-lp for symbolic equation discovery. Current projects aim to quantify urban design's impact on non-communicable diseases and improve walkability through micro-scaled spatio-temporal analysis.

• Machine Learning
• Computer Vision
• Deep Learning
• +۹ مورد دیگر

Benoit Liquet-Weiland is a Professor at Macquarie University's School of Mathematical and Physical Sciences, affiliated with the Astrophysics and Space Technologies Research Centre and Data Horizons Research Centre. His research focuses on statistical methodologies applied to biomedical and environmental data, including multi-state models, survival analysis, Bayesian modeling, and machine learning. He teaches advanced courses in statistics such as Generalized Linear Models and The Mathematical Engineering of Deep Learning. Education: HDR (PhD Supervisor diploma) in Statistical Modelling and Biomedical Applications from University of Bordeaux (2009), and a PhD in Statistics from Université de Bordeaux (2002). Research Interests: Model selection and variable selection Survival analysis and multi-state models High-dimensional omics data analysis Machine learning applications in healthcare and ecology Statistical methods for precision medicine Recent work emphasizes interdisciplinary applications, including predicting ICU outcomes for COVID-19 patients, detecting pleiotropic effects in cancer datasets, and coral classification using satellite imagery. He leads or collaborates on projects like FightMND MCR Fellowship and computational analysis in brain tumor imaging. Recipient of the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Active in supervising PhD students in statistical methodologies and applications.

• Biostatistics
• Survival Analysis
• Machine Learning
• +۱۰ مورد دیگر

Hassan Asghar is a Senior Lecturer in Computer Science at Macquarie University's School of Computing and a Research Scientist at CSIRO since 2014. His research focuses on privacy, cryptography, and information security, including quantifying privacy risks, developing secure cryptographic protocols, and analyzing authentication systems. He has contributed to areas such as differential privacy, secure multiparty computation, and malware analysis. His work integrates mathematics (number theory, probability, and combinatorics) with computer science disciplines like machine learning and algorithms. Notable projects include Privacy Preserving Data Sensing Algorithms (2018–2019) and the Macquarie University Cyber Security Hub (2016–2023). He has authored over 40 research outputs, including studies on adversarial attacks in AI, malware obfuscation, and privacy-preserving data release. Awards: ACM AsiaCCS 2019 Best Paper Award; 2023 Faculty of Science and Engineering Inter-School Collaboration Award. Key Research Themes: Privacy quantification, cryptographic protocol design, biometric authentication, and adversarial machine learning. Collaborations: Extensive international collaborations in cybersecurity and privacy research. Asghar’s contributions span technical publications, policy submissions (e.g., NSW Cybersecurity Inquiry), and practical tools like ConvoCache for chatbot efficiency. He leads the Future Communications Research Centre at Macquarie and actively engages in interdisciplinary cybersecurity initiatives.

• Privacy
• Cryptography
• Information Security
• +۶ مورد دیگر

Yu Zhang is a Lecturer in Computing (IoT/Networking) at the School of Computing, Macquarie University, Australia. He holds a PhD in Computer Science from RMIT University, an M.Eng in Distributed Computing from the University of Melbourne, and a B.Eng in Software Engineering from Southwest University for Nationalities. His research focuses on mobile computing, wireless sensor networks, embedded systems, on-device machine learning, and IoT, with emphasis on security, edge computing, and privacy preservation. He is affiliated with the Future Communications Research Centre and Smart Green Cities Research Centre. Dr. Zhang’s work integrates theoretical advancements with practical applications in IoT and edge computing. His research outputs span secure device communication, energy-efficient networking protocols, and anomaly detection frameworks. Collaborations include projects like the CSIRO Data61 PhD Scholarship. He actively contributes to IEEE and ACM, addressing challenges in privacy-aware systems and federated learning.

• Mobile Computing
• Wireless Sensor Networks
• Embedded Systems
• +۵ مورد دیگر

Dr. Wei Zhang is a Senior Lecturer and Associate Head of People and Culture at the University of Adelaide's School of Computer and Mathematical Sciences, with a secondary appointment as Honorary Lecturer at Macquarie University. She holds a PhD in Computer Science from the University of Adelaide (2017) and leads research in big data analytics, text mining, machine learning, and IoT. Her work has resulted in over 100 peer-reviewed publications, including top-tier journals like ACM Computing Surveys and IEEE Communications Surveys. Dr. Zhang currently holds an ARC Early Career Industry Fellowship (IE230100119) and leads an ARC Discovery Project (DP240103070), with total funding exceeding AUD $3M from ARC, Google, and DST over five years. Her research focuses on text summarization, AIoT systems, adversarial attacks on neural networks, and medical information extraction. Professional roles include Program Coordinator, Course Coordinator, and outreach contact at her school. She actively contributes to academic governance as Associate Head of People & Culture and serves on conference committees (ACL, KDD, WWW) and journal editorial boards. Industrial collaborations span six years with roles in project leadership and technical advisory roles across multiple sectors. Dr. Zhang is a member of ACM, IEEE, and ACS, and her work has been recognized by prestigious awards such as top 100 global researchers in Network Security (SciVal). Her lab develops AI-driven solutions for IoT-enabled agriculture (e.g., mushroom cultivation monitoring) and smart construction systems, with applications in energy optimization, healthcare, and environmental monitoring.

• Big Data Analytics
• Text Mining
• Machine Learning
• +۶ مورد دیگر

Dr George Stamatescu is a Research Fellow at the School of Economics and Public Policy, University of Adelaide. His research focuses on operations research, sequential decision making, and complex systems analysis. He is currently working on workforce planning for large-scale projects. Dr Stamatescu has experience in mathematical techniques for analytical system studies and has contributed to multi-camera tracking systems and neural network optimization research. Education details are not explicitly listed, but his professional roles include tutoring in Mathematics and Control Systems. He has supervised a Master's thesis titled 'Analysis of New Methods for Inference in Markov Decision Processes' (2021-2024). No scientific awards are mentioned in the provided texts. His work intersects with optimization theory and practical applications in health systems and project management. Dr Stamatescu's publications span operations research methodologies, machine learning, and surveillance technologies. His recent focus on workforce planning reflects his expertise in applying theoretical models to real-world logistical challenges.

• Operations Research
• Sequential Decision Making
• Complex Systems Analysis
• +۳ مورد دیگر

Guanfeng Liu is the Vice Dean at the NNU-MQ Joint Institute, Macquarie University, within the Faculty of Science and Engineering. His research focuses on Recommender Systems, Privacy-Preserving Technologies, Graph Neural Networks, and Data Analytics. He leads projects like the 'Intelligent Health Data Analytics Platform' and 'Data-intensive Scheduling Optimisation in Database Systems.' His work emphasizes ethical AI, with contributions to trust prediction, spatio-temporal data analysis, and privacy-aware algorithms. In 2023, he won the Faculty of Science and Engineering Award for Inter-School Collaboration. Liu has published extensively in top journals like IEEE Transactions and ACM Conferences, with over 200 publications and a h-index of 34. Key projects include scalable document intelligence platforms and confidentiality preservation in graph learning. His research bridges theoretical advancements with practical applications in smart cities and healthcare, leveraging collaborative filtering and contrastive learning techniques.

• Recommender Systems
• Privacy-Preserving Technologies
• Graph Neural Networks
• +۵ مورد دیگر

Shadi Jaradat is a Researcher at Queensland University of Technology (QUT) in Australia, affiliated with the School of Civil & Environmental Engineering. He holds a PhD in Civil & Environmental Engineering and a Master’s degree in Computer Science from the University of Queensland. His research focuses on data analytics, deep learning, NLP, and traffic safety, particularly leveraging AI techniques to enhance transportation safety through crash narrative analysis and multimodal data fusion. Shadi’s PhD research explores Deep Natural Language Processing-based mining of crash narratives , aiming to uncover insights for improving traffic safety. His academic background bridges computer science and civil engineering, enabling interdisciplinary approaches to real-world challenges. Key research areas include crash severity analysis, cyberattack detection in industrial systems, and multimodal data applications for traffic safety. His recent publications emphasize AI-driven methodologies for traffic safety, such as text mining for crash patterns, GPT models for multimodal analysis, and vision transformers for infrastructure inspection. Shadi collaborates with the Centre for Data Science at QUT, advancing data-driven solutions in transportation and cybersecurity domains. While no scientific awards are listed, his work demonstrates impactful contributions to traffic safety and AI applications. Shadi actively engages in cross-cultural crash analysis and contributes to projects addressing urban mobility challenges through advanced analytics.

• Data Analytics
• Deep Learning
• Natural Language Processing (NLP)
• +۳ مورد دیگر

Professor Gleb Beliakov is a faculty member at Deakin University's School of Information Technology within the Faculty of Science Engineering and Built Environment. He has held roles including Associate Head of School (Research) from 2009–2012 and currently leads the Computational Intelligence and Data Analytics Research cluster and the Data to Intelligence research centre. His expertise spans fuzzy systems, computational mathematics, numerical optimization, and high-performance computation. Education: PhD and MSc from the Russian Peoples Friendship University. Research Interests: Focuses on fuzzy systems, aggregation functions, parallel programming, and their applications in decision-making and data analysis. Grants: Includes projects on Trusted Autonomy (CRC), Satellite Communication Systems (Defence), and Explainable AI (ARC). His research emphasizes fuzzy measures, Choquet integrals, and their use in addressing complex problems like resource optimization and multimedia piracy. He has supervised numerous PhD and master's students, contributing to advancements in preference mining, image processing, and watermarking techniques.

• Fuzzy systems
• Computational mathematics
• Numerical optimization
• +۵ مورد دیگر

Jian He is a Teaching Professor at the School of Mathematics , Monash University, Australia. His research focuses on advancing cross-spectral biometric systems, medical image processing, generative adversarial networks (GANs), and robust machine learning. He has contributed significantly to improving face recognition technologies under challenging conditions such as thermal-to-visible spectrum translation and adversarial attacks. His work includes developing frameworks for medical imaging segmentation, enhancing security in biometric systems, and exploring novel applications in audio-visual synchronization and video generation. Key research themes include: Cross-spectral face recognition and thermal imaging analysis Medical image segmentation and 3D reconstruction Generative models for text-to-image synthesis and video generation Adversarial attacks and defense mechanisms in AI systems His publications demonstrate expertise in integrating deep learning with biometric security, healthcare AI, and computer vision applications. Notable contributions include surveys on cross-spectral recognition and innovations in latent diffusion models for high-quality video synthesis. No scientific awards or grants are explicitly listed in the provided texts. He currently has no recorded advisees, though his research areas suggest potential collaborations in interdisciplinary teams focused on AI and medical imaging.

• Biometrics
• Computer Vision
• Medical Imaging
• +۳ مورد دیگر