جست‌وجوی استاد و پژوهشگر

Gordana Popovicمشاهده پروفایل

مدرس ارشد

• Statistics
• Statistical Ecology
• Biostatistics
• +۳ مورد دیگر

Dr. Gordana Popovic is a Senior Lecturer at the University of New South Wales (UNSW), affiliated with the Statistical Consulting Unit within the Research & Enterprise division. She specializes in statistical ecology, biostatistics, and computational modeling, providing methodological guidance and collaborative support to applied researchers. Her work spans ecological data analysis, clinical studies, and environmental monitoring. Her research focuses on developing and applying advanced statistical techniques, including copula models, spatial analysis, and longitudinal studies. Recent projects address wetland vegetation dynamics, cancer prognosis biomarkers, and antibody response modeling. She also teaches short courses in statistics using R and SPSS. Dr. Popovic has supervised research students such as Ben Maslen and contributed to interdisciplinary collaborations in fields like oncology, immunology, and conservation biology. Her publications emphasize methodological innovation and empirical applications in ecology and clinical research. Key journals where her work appears include Cancer , Methods in Ecology and Evolution , and PLOS Medicine . Her methodological expertise includes maximum likelihood estimation, graphical models, and design optimization for multivariate data collection.

Yu Guang Wangمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computational Mathematics
• Statistics
• +۱۰ مورد دیگر

Yu Guang Wang is an Associate Professor at Shanghai Jiao Tong University, holding positions across multiple institutes including the Institute of Natural Sciences, School of Mathematical Sciences, Department of Computer Science and Engineering, and AI Biomedicine Center of Zhangjiang Institute for Advanced Study. He also serves as Adjunct Associate Professor at both UNSW Sydney and Shanghai AI Laboratory, demonstrating his significant interdisciplinary reach. His research spans artificial intelligence, computational mathematics, statistics, and data science, with particular focus on geometric deep learning, graph neural networks, applied harmonic analysis, and Bayesian inference. His work bridges theoretical foundations with practical applications in biomedicine and protein design, creating impactful connections between mathematics and real-world problems. Wang's publication record shows a clear progression toward increasingly sophisticated applications of geometric deep learning, particularly in graph neural networks for protein engineering and biomedical applications. His recent work demonstrates strong interdisciplinary collaboration, combining topological data analysis with machine learning for complex biological systems. ICERM Semester Postdoctoral Fellowship of Brown University (2018) IPAM visitor of UCLA (2019) Long-term visitor of AI Group of Prof Pietro Lio at University of Cambridge (2022) ICML Top Reviewer (2020) University International Postgraduate Award, UNSW (2011-2015) As an advisor, Wang has successfully mentored multiple PhD and Master's students working on graph neural networks, geometric deep learning, and applications to biomedicine. His research has been supported by prestigious funding from Shanghai Jiao Tong University, Huawei Central Research Institute, Ministry of Education Key Lab in Scientific and Engineering Computing, Shanghai National Center for Applied Mathematics, National Natural Science Foundation of China, and the European Research Council. He leads an active research group focused on geometric deep learning and its applications to science and medicine.

Endre Szemerédiمشاهده پروفایل

استاد

• Combinatorics
• Theoretical Computer Science
• Discrete Mathematics
• +۱ مورد دیگر

Endre Szemerédi is the State of New Jersey Professor of Computer Science at Rutgers University and Professor Emeritus at the Alfréd Rényi Institute of Mathematics. A pioneer in discrete mathematics, he proved Szemerédi's theorem on arithmetic progressions and developed the Szemerédi regularity lemma. His work bridges combinatorics, computer science, and number theory. Education includes studies at Eötvös Loránd University and a Ph.D. from Moscow State University under Israel Gelfand. Szemerédi has held visiting positions at Stanford University, McGill University, University of South Carolina, and University of Chicago. Research fundamentally advanced extremal graph theory, combinatorial number theory, and randomized algorithms. Contributions include the Szemerédi-Trotter theorem and the Hajnal-Szemerédi theorem. Abel Prize (2012) for discrete mathematics contributions Rolf Schock Prize (2008) for work on arithmetic progressions Leroy P. Steele Prize (2008) for seminal research

• Computer Science
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Dr. John Stavrakakis is a Lecturer at the School of Computer Science within the University of Sydney. His research focuses on machine learning , graph algorithms , and 3D graphics optimization , particularly in developing efficient computational methods for spectral clustering , deep learning architectures , and network-aware visualization systems . Current research emphasizes random projection techniques for graph convolutional networks and topology-aware clustering methods Specializes in 3D graphics streaming and distributed visualisation environments Collaborates on network security behavior through computational models of protection motivation His recent publications highlight innovations in graph construction , k-NN search efficiency, and parameter-free optimization frameworks. Dr. Stavrakakis supervises PhD students including Michael Ashshiddiq Podbury, with expertise spanning data mining , network security , and interactive visualization . Contact: john.stavrakakis@sydney.edu.au

• High Dimensional Statistical Inference
• Machine Learning
• Data Science
• +۱۱ مورد دیگر

Professor Bala Rajaratnam is a leading academic in Business Analytics at the University of Sydney Business School . His research spans high-dimensional statistical inference , machine learning , and data science , with applications in economics , finance , biomedical sciences , and environmental modeling . Key focus areas include graphical models , network analysis , and Bayesian methodology . His recent work examines partial correlation screening , covariance estimation , and climate field completion . Publications highlight interdisciplinary collaborations across climate science , financial engineering , and biomedical research . He received a 2019 Australian Research Council Discovery Project grant for Principled statistical methods for high dimensional correlation networks .

• Forecast Combination
• Econometrics
• Time Series Analysis
• +۲ مورد دیگر

Andrey Vasnev is Professor of Business Analytics at the University of Sydney Business School. He holds an MA in Economics from NES Moscow, a PhD in Economics from Tilburg University, and an MEd from the University of Sydney. His research specializes in forecast combination methodologies for business, finance, and economics, particularly focusing on information integration from different temporal levels and optimal weighting strategies. His work addresses practical challenges in consolidating diverse predictions to improve forecast accuracy. Recent publications demonstrate his focus on advancing forecasting techniques through statistical innovations and machine learning applications in graph neural networks. His research consistently addresses both theoretical foundations and practical implementation challenges in predictive modeling.

• Robotics
• Artificial Intelligence
• Task and Motion Planning
• +۴ مورد دیگر

Rahul Shome is a tenure-track lecturer in the School of Computing at the Australian National University (ANU), specializing in Robotics & AI. His research focuses on planning algorithms for human-centric robotics applications, with expertise in task and motion planning, multi-robot systems, and collaborative automation. He holds a Ph.D. and M.S. from Rutgers University, advised by Prof. Kostas E. Bekris, and completed postdoctoral work at Rice University under Prof. Lydia E. Kavraki. Education: Ph.D. and M.S. in Robotics, Rutgers University (advisor: Prof. Kostas E. Bekris) Postdoctoral Research Associate, Rice University (advisor: Prof. Lydia E. Kavraki) Research Interests: Development of practical robotics solutions with theoretical guarantees Integration of AI techniques for real-world automation challenges Multi-robot coordination and manipulation in dynamic environments Human-centric robotics emphasizing privacy, ethics, and collaboration His publications span journals and conferences like IEEE Robotics and Automation Letters, with awards including the Best Paper Award at IEEE MRS. Shome actively serves on editorial and organizing boards for major robotics venues. Current research directions explore algorithmic foundations in stochastic decision-making, topology, and machine learning integration. Advising & Opportunities: Open PhD and research positions in robotics planning and collaborative automation Focus areas: AI-driven robotics, human-robot interaction, and perception systems

Minh Nguyenمشاهده پروفایل

پژوهشگر ارشد

• Probability Theory
• Stochastic Processes
• Reinforced Random Walks
• +۲ مورد دیگر

Dr. Minh Nguyen is a Research Fellow in Probability Theory at Monash University's School of Mathematics. His research develops mathematical frameworks for self-interacting random processes, particularly reinforced random walks on graphs and integer lattices. Nguyen's work establishes fundamental properties of vertex-reinforced jump processes, including convergence behaviors on complete graphs and nonlinear reinforcement mechanisms. His publications rigorously analyze how local interaction rules generate complex global behaviors in stochastic systems. Additional research interests include combinatorial probability, random graph theory, and applications of stochastic methods to quantum phenomena. Nguyen serves as reviewer for leading probability journals and maintains international collaborations with researchers in Sweden and Australia.

John Ormerodمشاهده پروفایل

دانشیار

• Statistics
• Bayesian Inference
• Data Mining
• +۴ مورد دیگر

Associate Professor John Ormerod is affiliated with the Statistics Research Group at the School of Mathematics and Statistics , University of Sydney . His research focuses on advanced statistical methodologies, including Variational Bayes , Generalized Linear Mixed Models , Splines , and Missing Data analysis. Current research students: Rajan Shankar and Jackson Zhou Key research strengths: Precision and Digital Health , Data and Decisions , National Security His work spans computational statistics, bioinformatics, and applications in health and biological data. Recent publications emphasize scalable Bayesian inference, variable selection, and single-cell data analysis frameworks. Grants include multiple Australian Research Council (ARC) Discovery Projects (2021, 2017, 2012, 2010) supporting research in feature selection, network modeling, and computational efficiency. He has supervised projects on modern regularization techniques and fast expectation propagation , contributing to BMC Bioinformatics , Bioinformatics , and Journal of Computational and Graphical Statistics .

David Doweمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۵ مورد دیگر

David L. Dowe is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of Information Technology. He holds academic qualifications including a PhD in Mathematics from Monash University (1991), MSc in Econometrics from the London School of Economics (1987), and BSc (Honours) in Mathematics from the University of Melbourne (1982). His research focuses on Minimum Message Length (MML) inference, artificial intelligence, and machine learning applications in diverse domains such as healthcare, environmental science, and bioinformatics. Teaching responsibilities include units like FIT4009 (Advanced Topics in Intelligent Systems), FIT5158 (Customer Relationship Management and Data Mining), and others. He has led or collaborated on ARC-funded projects like 'New Statistical Techniques for Galactic Archaeology' (2016–2019) and 'Rating and ranking sports players using MML' (2011–2017). His work contributes to UN Sustainable Development Goals related to education and innovation. Recent research highlights include developing deep learning frameworks for brain disorder identification, predictive AI models for bacterial drug resistance, and hybrid statistical-LSTM forecasting. He emphasizes interdisciplinary applications of MML in fields ranging from climate modeling to medical imaging.

Laleh Tafakoriمشاهده پروفایل

مدرس ارشد

• Extreme Value Theory
• Dependence Modelling via Copulas
• Time Series Analysis
• +۸ مورد دیگر

Dr. Laleh Tafakori is a Senior Lecturer in Statistics and Analytics at RMIT University’s School of Science, based at the City Campus in Australia. Her research focuses on extreme value theory, dependence modeling via copulas, time series analysis, machine learning, financial econometrics, and stochastic processes. She actively supervises research projects in areas such as diabetes prediction, climate modeling, and network analysis. Her teaching interests include applied analytics, statistical inference, and stochastic processes. Dr. Tafakori’s work bridges statistical theory and real-world applications, particularly in healthcare, finance, and environmental science. Recent projects include predicting diabetes onset, analyzing extreme precipitation patterns, and developing machine learning models for risk assessment. She collaborates internationally, with studies conducted in Saudi Arabia, Oman, and Europe. Research Highlights: Predictive modeling in healthcare, network influence analysis, and climate data forecasting. Supervision Interests: Machine learning applications, time series modeling, and complex systems analysis.

• 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.

• Computer Vision
• Robotics
• Intelligent Automation
• +۵ مورد دیگر

Professor Alireza Bab-Hadiashar is a Professor of Mechatronics at RMIT University's School of Engineering, leading the Intelligent Automation Research Group. He holds a PhD in Robotics from Monash University and has held academic roles at Swinburne University and the University of Copenhagen. His research focuses on computer vision, robotics, and intelligent automation, with industrial applications in manufacturing and healthcare. He leads the Master of Robotics and Mechatronics Engineering program and has extensive experience in interdisciplinary projects involving AI, biomedical imaging, and advanced manufacturing. Research interests span computer vision, AI-driven automation, and robotics with emphasis on safety-critical systems like autonomous vehicles and medical imaging. Notable projects include defect detection in additive manufacturing and domain generalization in medical imaging. He collaborates with industry partners like Ford Australia on lightweight vehicle design and quality control. His recent publications emphasize distributed tracking systems, robotic calibration, and domain adaptation techniques. He actively supervises research projects in automated inspection, computer vision for transportation safety, and cancer detection via CT imaging. Academic memberships include IEEE, FIEAust, and professional organizations in computer vision and robotics. Labs/Teams: Director of the Intelligent Automation Research Group at RMIT. Current initiatives include developing AI solutions for Industry 4.0 and smart manufacturing systems.

Haohui Luمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Graph Machine Learning
• Health Informatics
• +۱۲ مورد دیگر

Haohui Lu is a Research Fellow at the Molly Wardaguga Research Centre within the Faculty of Health. He is registered to supervise postgraduate research and holds a PhD from The University of Sydney, awarded in March 2024. His work bridges artificial intelligence and public health, with a focus on ethical and explainable AI systems. Research Interests: Artificial Intelligence and Machine Learning Graph Machine Learning and Knowledge Graphs Health Informatics and Clinical Decision Support Bias Mitigation and Fairness in AI Large Language Models for Healthcare Statistical Modeling and Real-World Data Analysis His recent publications demonstrate a strong trend in applying machine learning—particularly graph-based and language models—to public health challenges such as disease progression, policy analysis, and undernutrition. He frequently publishes in high-impact journals like PLoS One , Scientific Reports , and Neurocomputing , and presents at top venues such as EMNLP. His work emphasizes fairness, interpretability, and practical impact for policymakers and communities. Scientific Contributions and Recognition: Active contributor to UN Sustainable Development Goals, particularly in health and well-being. Presented research at EMNLP 2024 on LLMs for disease prediction. Research disseminated through news outlets and widely shared on academic platforms like Mendeley. Advising and Leadership: Registered as a postgraduate research supervisor, indicating a role in mentoring future researchers. Provides technical leadership in AI tool development, research data platforms, and LLM applications, supporting multidisciplinary and international collaborations. Laboratories and Teams: Based at the Molly Wardaguga Research Centre, a hub for health research, where he contributes to projects integrating AI with real-world health data and policy.

• Network Science
• Cybersecurity
• Data Analysis
• +۳ مورد دیگر

Matthew Roughan is a Professor and Interim Director of the Teletraffic Research Centre at the University of Adelaide. His research focuses on network science, cybersecurity, data analysis, and statistical modeling. He is affiliated with the University's academic community and contributes to both theoretical and applied research in these domains. His work spans diverse areas including satellite maneuver analysis, causal network discovery, entropy rate estimation, and protocol reverse-engineering. Recent projects highlight advancements in machine learning applications for space surveillance, robust causal network development, and optimization algorithms for multi-modal systems. Roughan's articles reflect a commitment to interdisciplinary approaches, blending mathematics, computer science, and domain-specific knowledge. Notably, his contributions to entropy theory, cybersecurity tool development, and space technology analysis underscore his impact in both academic and practical contexts. His work in Teletraffic Research Centre involves managing interdisciplinary projects and fostering innovation in network and data analysis methodologies.