جست‌وجوی استاد و پژوهشگر

Catherine Greenhillمشاهده پروفایل

استاد

• Combinatorics
• Graph Theory
• Applied Probability
• +۶ مورد دیگر

Professor Catherine Greenhill is a faculty member at the School of Mathematics and Statistics, UNSW Sydney , where she serves as Professor and head of the Combinatorics group. Her academic career spans institutions including the University of Queensland, University of Oxford, University of Leeds, University of Melbourne, and Australian National University. D.Phil., University of Oxford (1996) M.Sc. (Research) in Combinatorics (1992) B.Sc. (Hons) in Pure Mathematics (1991) Her research focuses on the intersection of discrete mathematics , theoretical computer science , and probability , particularly in asymptotic combinatorics , probabilistic methods , and analysis of algorithms . Her work includes asymptotic enumeration of combinatorial structures and design of randomized algorithms for graph sampling and counting. Her recent publications (2025–2021) center on random graphs and hypergraphs , with key contributions to switch Markov chains , degree sequence analysis , and chromatic number bounds . These works reflect her expertise in probabilistic combinatorics and algorithmic complexity . Scientific Awards: Fellow of the Australian Academy of Science (2022) Christopher Heyde Medal in Pure Mathematics (2015) June Griffith Fellowship (2013) Hall Medal (2010) Advising and Grants: She has supervised numerous PhD/Masters students and secured multiple ARC Discovery Grants (2019–2021, 2014–2016, 2012–2014). Her grants address topics like hypergraph modeling, random discrete structures, and network analysis in illicit drug trafficking.

Yang Songمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Multimodal AI
• +۷ مورد دیگر

Yang Song is an ARC Future Fellow and Scientia Associate Professor at the School of Computer Science and Engineering , University of New South Wales (UNSW) . She serves as Associate Head of School (Research) and Co-Director of iCinema , focusing on AI and Computer Vision applications for social good. Education: BEng in Computer Engineering (Nanyang Technological University, Singapore), PhD in Computer Science (UNSW, 2013) Research Areas: Biomedical image analysis, human-centred AI, graph data modeling, neuro-symbolic learning, and AI trustworthiness. Her work develops domain-specific deep learning models for radiological segmentation, histopathology cancer analysis, and 3D reconstruction. Recent projects address explainability in LLMs, fairness in AI, and human-robot interaction frameworks. With over 200 peer-reviewed publications in top venues like CVPR , MICCAI , and NeurIPS , her research spans biomedical imaging, robotics, and general multimodal AI. Scientific Awards include: 2024: ARC Industrial Transformation Research Hub for Human-Robot Teaming 2023: Google Inclusion Research Award 2022: NHMRC Ideas Grant for computational brain imaging 2021: UNSW Engineering Research Excellence Award 2020: Scientia Fellowship (UNSW) 2019: ARC Future Fellowship She supervises 24 current PhD/MPhil students and has graduated 15 advisees, including placements at Harvard University and Siemens Healthineers. Her grants include collaborations with Surf Life Saving Australia and industry partnerships for AI-driven solutions.

• Quantum Information Science
• Quantum Computing
• Quantum Optics
• +۳ مورد دیگر

Professor Andrew Doherty is a prominent academic in the Faculty of Science at the University of Sydney, specializing in quantum information science and quantum computing. His research focuses on quantum error correction, quantum control, and foundational aspects of quantum mechanics, particularly involving quantum trajectories and entanglement. He leads projects within the Sydney Nanoscience Hub (SNH), contributing to advancements in superconducting qubits and topological codes. His work bridges theoretical and experimental quantum physics, with grants including the 'Quantum and Advanced Technologies' project (2024) and the ARC Training Centre for Future Leaders in Quantum Computing (2023). His publications emphasize scalable error suppression, photonic qubit systems, and code concatenation strategies, reflecting a commitment to both fundamental science and applied quantum technologies. Research Themes: Quantum error correction, quantum measurement theory, topological codes, and superconducting circuits. Key Collaborations: Involvement with international teams in quantum computing and nanoscience. Labs/Teams: Active member of the Sydney Nanoscience Hub (SNH). Professor Doherty's contributions span over 100 articles, with recent work addressing noise-aware decoding and Gottesman-Kitaev-Preskill (GKP) states. His research aims to advance fault-tolerant quantum computing and deepen understanding of quantum correlations.

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

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

Jia Wuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Mining
• Deep Learning
• +۷ مورد دیگر

Dr. Jia Wu is an Associate Professor and Research Director of the Centre for Applied Artificial Intelligence at Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (2009) and is an IEEE Senior Member. His research focuses on artificial intelligence, data mining, graph neural networks, and anomaly detection, with over 200 publications in top-tier journals/conferences like IEEE TPAMI, TKDE, and conferences like KDD, IJCAI, and NeurIPS. He has received awards including the Heidelberg Laureate Forum Fellowship (2019) and multiple best paper awards. Education: PhD in Computer Science (UTS, 2009). Current roles include Director of HDR (Higher Degree Research) and Associate Editor for IEEE TNNLS and ACM TKDD. He leads projects in AI-driven cybersecurity, personalized banking solutions, and disaster response systems. Research interests emphasize graph-based learning, fake news detection, and deep learning applications. His recent work explores hypergraph neural networks for fraud detection and brain graph analysis for neurological disorders. He has pioneered scalable semi-supervised clustering techniques and transformer-based hypergraph models for anomaly detection. Awards include CIKM'22 Best Paper Runner-Up, ICDM'21 Best Student Paper, and the 2023 Faculty of Science and Engineering Collaboration Award. His work spans 13 active research projects, including mitigating AI deepfakes in identity systems and enhancing disaster response networks through graph-based simulations. Labs/Teams: Leads teams in the Data Horizons Research Centre, Future Communications Research Centre, and Hearing Research Centre. Collaborates internationally in AI, data mining, and social network analysis.

• Data Science and Data Analytics
• Machine Learning
• Recommender Systems
• +۷ مورد دیگر

Guandong Xu is a Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he has been employed since 2012. He also serves as the Director of the UTS-Providence Smart Future Research Centre, which focuses on disruptive technology for sustainability, and leads the Data Science and Machine Intelligence Lab dedicated to research excellence and industry innovation in data science and artificial intelligence. Dr. Xu holds a PhD in Computer Science from Victoria University, Australia, along with MSc and BSc degrees in Computer Science and Engineering. After holding various research positions at European and Australian universities, he joined UTS in 2012 and was promoted to Associate Professor in January 2017, then to Professor in January 2019. His research spans data mining, machine learning, social computing, recommender systems, text mining, predictive analytics, and user behavior modeling. He has published over 240 papers in these areas with increasing citations from academia. His recent work demonstrates a strong focus on integrating large language models with recommendation systems, causal inference in recommendation, multimodal learning, and fairness in AI systems. His publications reveal sophisticated graph-based approaches and addressing challenges in dynamic recommendation scenarios, particularly through temporal modeling and hypergraph structures. Dr. Xu has received numerous prestigious awards including the Digital Disruptors Winner for ICT Research Project of the Year (2021), eBay's Leaders' Choice Award (2021), and was elected Fellow of Institution of Engineering and Technology (IET), UK (2021) and Fellow of Australian Computer Society (ACS) (2022). He has shown strong academic leadership as founding Editor-in-Chief of Human-centric Intelligent Systems Journal, Assistant Editor-in-Chief of World Wide Web Journal, and founding Steering Committee Chair of the International Conference of Behavioural and Social Computing Conference. He has supervised over 25 high degree research students and secured over $8 million in research funding from ARC, government, and industry sources, including projects like 'Smart Personalized Privacy Preserved Information Sharing in Social Networks' and 'A Secured Smart Sensing and Industry Analytics Facility for Industry 4.0.' Dr. Xu directs the Data Science and Machine Intelligence Lab at UTS, which aligns with UTS research priority areas in data science and artificial intelligence. The lab focuses on research excellence and industry innovation across academia and industry, with particular emphasis on developing advanced techniques for recommendation systems, knowledge graphs, and multimodal learning applications.

Xiaoyang Wangمشاهده پروفایل

مدرس ارشد

• Graph Processing
• Graph Neural Networks
• Spatial Data Processing
• +۱۰ مورد دیگر

Xiaoyang Wang is a Senior Lecturer in the School of Computer Science and Engineering (CSE) at the University of New South Wales (UNSW). He holds a Bachelor's and Master's degree in Computer Science from Northeastern University, China, and earned his PhD from CSE UNSW. Dr. Wang's research focuses on database systems with a special emphasis on query processing and data mining on large-scale graph, spatial, and streaming data. His expertise extends to data-driven machine learning, smart contract analysis on blockchain, and FinTech with financial network analysis. His work spans Graph Processing, Graph Neural Networks, Spatial Data Processing, AI for Databases (AI4DB), Database for AI (DB4AI), and FinTech applications. His publication record shows significant contributions to the field with 7 book chapters, 56 journal articles, 61 conference papers, 7 edited conference proceedings, and 4 conference abstracts. Recent publications (2022-2025) demonstrate his strong research trajectory in advanced graph processing techniques, neural network applications, and innovative database approaches. Key themes include hierarchical contrastive learning, robust attack frameworks, temporal graph processing, influence maximization, knowledge graph-enhanced reasoning, and rumor mitigation. Dr. Wang actively recruits PhD students interested in pursuing research in related fields and encourages current undergraduate and master's students at UNSW to contact him about research opportunities. He maintains an active research agenda with practical implications for industries dealing with large-scale network data, financial technology applications, and data-intensive systems. He can be reached at xiaoyang.wang1@unsw.edu.au and is located in Engineering building K17-501D at UNSW.

Nicholas Wormaldمشاهده پروفایل

استاد

• Combinatorics
• Random Graphs
• Graph Theory
• +۴ مورد دیگر

Nicholas Wormald is a Professor in the Department of Mathematical Sciences at Monash University, Australia. He joined the university in 2013 as an Australian Laureate Fellow, following a decade as a Canada Research Chair in Combinatorics and Optimization at the University of Waterloo, Canada. His primary research focuses on combinatorics, probabilistic methods, and random structures, with notable contributions to random graph theory, enumeration, and applications in optimization. Wormald holds a PhD from the University of Newcastle. His work spans topics including Hamilton cycles in random regular graphs, k-core emergence, probabilistic combinatorics, and algorithms for graph generation. He has contributed to foundational results such as the sudden emergence of giant components in random graphs and the contiguity of random graph models. He serves on editorial boards of journals like the Electronic Journal of Combinatorics and Random Structures and Algorithms . His research interests also include underground mine optimization and the study of Steiner trees. Notable projects include enumeration and random generation of contingency tables, properties of large discrete structures, and the analysis of random structures' applications. Wormald has authored or co-authored over 250 publications, with recent work focusing on asymptotic enumeration, hypergraphs, and the probabilistic analysis of combinatorial structures.

Lydia Cuiمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Biomedical Image Processing and Analysis
• Machine Learning
• +۶ مورد دیگر

Dr. Lydia Cui is a Senior Lecturer at La Trobe University's Department of Computer Science and Information Technology. She holds a PhD and MPhil from the University of Sydney and a Bachelor's from Harbin Institute of Technology. Her research focuses on AI-driven biomedical image analysis, machine learning, and precision oncology, with emphasis on multi-modality imaging fusion, cancer diagnosis, and graph neural networks. She actively collaborates with industry and hospitals to translate AI technologies into clinical workflows. Dr. Cui leads the Department’s Teaching & Learning and Postgraduate Course Coordination roles. She has received notable awards, including the SNMMI 2015 International Best Paper Award. Her teaching includes Data Mining, Computer Vision, and Image Processing courses. Research interests include segmentation of biomedical images, AI for disease prognosis, and integration of imaging with non-imaging biomarkers. Recent publications span top-tier journals like IEEE Transactions on Medical Imaging and conferences such as MICCAI. She supervises students in AI and biomedical informatics. Funded projects include 'Multi-modality data-driven health monitoring in Industry 4.0' with Rudder Technology.

Ben Duanمشاهده پروفایل

عضو هیئت علمی

• Reinforcement Learning
• Graph Neural Networks
• Intelligent Transportation Systems (ITS)
• +۴ مورد دیگر

Ben Duan is an Adjunct Lecturer at the Department of Data Science & AI, Faculty of Information Technology, Monash University. He holds a PhD in Big Data and Electronics from the University of Technology Sydney. Previously, he was a Research Fellow at the Hong Kong University of Science and Technology. His research focuses on reinforcement learning, graph neural networks, intelligent transportation systems (ITS), and fintech. He teaches courses such as FIT5221: Intelligent Image and Video Analysis, FIT5215: Deep Learning, and FIT5217: Statistical Data Modeling at Monash’s Suzhou campus. Dr. Duan’s work aligns with UN Sustainable Development Goals related to sustainable cities and communities (SDG 11) and industry innovation (SDG 9). His recent research explores AI-driven solutions for traffic congestion, stock trend prediction using graph neural networks, and spiking neural networks for neural activation control. He has contributed to over 28 publications and a patent with the Monash Suzhou Research Institute. He supervises PhD students interested in reinforcement learning, graph neural networks, or ITS, offering full scholarships at Monash Suzhou. Key collaborations span transportation systems, fintech, and data-driven industrial processes.

Xin Xiaمشاهده پروفایل

پژوهشگر ارشد

• Recommender Systems
• Machine Learning
• Graph Neural Networks
• +۳ مورد دیگر

Dr. Xin Xia is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on advancing machine learning techniques, particularly in the domain of recommender systems, with an emphasis on on-device deployment, graph-based learning, and self-supervised methods. He has contributed to optimizing recommendation systems for edge computing environments and improving their efficiency in resource-constrained settings. Key research areas include: On-device recommendation systems and edge AI Graph neural networks for recommendation Self-supervised learning and contrastive techniques Efficient model optimization and knowledge distillation His publications analyze trends such as transitioning cloud-based systems to on-device applications, reducing communication overhead in distributed models, and simplifying graph contrastive learning frameworks. These contributions highlight his expertise in bridging theoretical advancements with practical, deployable solutions. Dr. Xia’s work has been presented at top-tier conferences such as SIGIR, CIKM, and AAAI, reflecting the academic rigor and industry relevance of his research.

• Combinatorics
• Combinatorial Design
• Latin Squares
• +۵ مورد دیگر

Ian Wanless is a Research Professor at the School of Mathematics, Monash University. He specializes in combinatorics, with a focus on Latin squares, graph theory, and matrix permanents. His work bridges theoretical mathematics and applications in information technology, such as coding and experimental design. Wanless leads collaborative projects funded by the Australian Research Council, including studies on combinatorial structures and hypergraph matchings. He has authored over 120 peer-reviewed articles, with recent work addressing quantum states, Latin square properties, and quasigroup isomorphisms. Key Awards: Australian Mathematical Society 2009 Medal Collaborations: Global partnerships with institutions in the US, Europe, and Asia His research emphasizes universal solutions to combinatorial puzzles, such as Sudoku and the four-color theorem, while prioritizing theoretical rigor over direct applications.

• Machine Learning
• Medical Data Analysis
• Time-Series Data Analysis
• +۳ مورد دیگر

Dr. Weitong Chen is a Senior Lecturer at the School of Computer and Mathematical Sciences, University of Adelaide, and an ARC EC Industry Fellow at the Australian Institute for Machine Learning (AIML). He holds a PhD from the University of Queensland (2020), with prior roles as a Post-Doc Research Fellow and Associate Lecturer there. His research focuses on machine learning applications in medical data, particularly time-series analysis, semi-supervised learning, and IoT. He collaborates widely across academia, industry, and government, supported by multiple grants. His work emphasizes healthcare applications, adversarial robustness, federated learning, and unlearning mechanisms. Education: PhD in Machine Learning, University of Queensland (2020) Master's Degree, University of Queensland Bachelor's Degree, Griffith University Research Interests: Medical Data Analysis (e.g., EHRs, radiology) Time-Series Modeling (healthcare IoT, irregular data) Adversarial Machine Learning (backdoor attacks, robustness) Federated Learning (modality incompleteness, clustered frameworks) Data Privacy (unlearning, compliance) Grants & Collaborations: ARC EC Industry Fellowship Industry partnerships in healthcare and IoT Labs/Teams: Australian Institute for Machine Learning (AIML) Cross-disciplinary health tech collaborations

Diane Donovanمشاهده پروفایل

استاد

• Combinatorics
• Design theory
• Latin squares
• +۶ مورد دیگر

Diane Donovan is an Affiliate Professor in the School of Mathematics, Faculty of Science at the University of Queensland. Her research spans combinatorics, design theory, and their interdisciplinary applications to cryptography, coding theory, and biological systems. Her work demonstrates strong theoretical foundations while increasingly incorporating real-world applications across diverse scientific domains. Professor Donovan's research interests focus on combinatorial structures, particularly Latin squares, orthogonal arrays, and design theory. Her work explores critical sets in Latin squares, biembeddings of combinatorial structures, and applications to coding theory and cryptography. She has made significant contributions to the understanding of partial Latin squares, Heffter arrays, and their connections to graph theory and finite geometry. Her recent work shows an expanding focus on interdisciplinary applications, particularly in biological systems and energy modeling. Analysis of her recent publications reveals a trajectory from purely theoretical combinatorial mathematics toward increasingly interdisciplinary applications. While maintaining strong theoretical contributions to design theory and combinatorics, her work now frequently intersects with biological modeling, medical research, and energy systems. This evolution demonstrates how fundamental mathematical structures can provide powerful tools for addressing complex problems in diverse scientific fields, from cancer modeling to gene expression analysis and petroleum engineering. Professor Donovan maintains an active research profile with numerous collaborations across disciplines, particularly with researchers in mathematics, biology, and engineering. Her publication record shows consistent productivity with significant contributions to top journals in combinatorics and discrete mathematics. Her work demonstrates the power of combinatorial methods to address both theoretical questions and practical applications across scientific domains.

Joel Millerمشاهده پروفایل

دانشیار

• Infectious Diseases
• Network Theory
• Stochastic Analysis and Modelling
• +۷ مورد دیگر

Joel Miller is an Associate Professor in Mathematics & Statistics at La Trobe University, School of Computing Engineering and Mathematical Sciences. His research focuses on infectious disease modelling, network theory, and ethical considerations in public health policy. He has held academic positions at Monash University, Penn State University, and the University of British Columbia Centre for Disease Control. Miller earned his PhD from the University of Cambridge and has contributed to significant projects, including the Australian Centre for AI in Medical Innovation and federal studies on pandemic response. His teaching includes courses on Vector Calculus, Infectious Disease Modelling, and Network Epidemiology. Notable awards include the 2021 La Trobe honourable mention for research impact and the RAPIDD Postdoctoral Fellowship. Education: PhD (University of Cambridge), MMath (University of Cambridge), BS (Harvard Mudd College). Research interests span epidemic dynamics, network structures, stochastic simulation algorithms, and ethical frameworks in computational modelling. He has led grants including a 2020 federally-funded study on pandemic testing capacity and contributed to the National COVID plan for Remote/Indigenous communities. Scientific Awards: Larkins Fellowship, RAPIDD Postdoctoral Fellowship, and multiple fellowships. His work has been featured in journals like PLOS Computational Biology and Science Advances. Teaching and Advising: Supervised numerous PhD/Masters students and developed courses like Dynamics of Spreading Processes on Networks. His mentorship includes projects on hepatitis C epidemiology, hypergraph contagion, and AI-driven network structure analysis. Professional Activities: Editorial roles in journals like PNAS and Science Advances, and media collaborations with outlets like Reuters and the New York Times.