جستجوی استاد راهنمای دانشگاهی

• Bayesian Statistics
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Dr. Yanan Fan is a Senior Principal Research Scientist at CSIRO's Data61 and an Adjunct Professor of Statistics at the University of New South Wales (UNSW). His research focuses on Bayesian models, computational methods for real-world problems, and interdisciplinary applications in fields like medical imaging, cosmology, and climate science. He holds a PhD in Statistics from the University of Bristol and has over 20 years of academic experience at UNSW's School of Mathematics and Statistics. Education: PhD in Statistics, University of Bristol, UK Undergraduate Degree in Mathematics, University of Melbourne Research Interests: Fan develops Bayesian semiparametric models, approximate Bayesian computation (ABC), and scalable computational methods for medical imaging (e.g., PET), cosmology, and climate modeling. He also investigates gender bias in educational evaluations and leads initiatives like the Data4Good stream of UDASH. His work emphasizes practical problem-solving through advanced statistical techniques. Leadership & Contributions: As Team Leader of Bayesian Computational Methods and Applications at Data61, he drives innovation in statistical methodologies. He has served on the Scientific Committee of MATRIX research institute and as an Associate Editor for major statistical journals. His projects include probabilistic climate projections and bias analysis in student evaluations. Labs & Groups: Active member of the StatML Group and leader of the Bayesian Computational Methods team, focusing on integrating machine learning and statistical computing.

• Data Visualization
• Machine Learning
• Neural Networks
• +۵ مورد دیگر

Professor Masa Takatsuka is a faculty member in the School of Computer Science at The University of Sydney, holding the rank of Professor. He serves as Associate Head of School (Postgraduate). His research focuses on data visualization, machine learning, and human-computer interaction, with notable contributions in spectral clustering, neural networks, and collaborative systems. He teaches courses such as COMP5048 Visual Analytics, COMP5116 Design of Networks & Distributed Systems, and others. Research interests include: Data visualization techniques (e.g., SOM-based methods) Graph neural networks and spectral clustering algorithms Interactive systems for large displays and collaborative environments Geospatial analysis and visualization tools like GeoVISTA Studio Recent publications span topics like random projection forests in graph convolutional networks (2023), topology-aware clustering (2018), and crowd-powered evaluation visualization (2017). His work emphasizes practical applications in domains like medical data analysis and social media sentiment exploration. He has secured grants including ARC Linkage Projects (2011, 2010) and industry partnerships. Current research students include Michael ASHSHIDDIQ PODBURY working on topological clustering methods. Labs/teams: Collaborations involve UnderworldGUI (geodynamic modeling tools) and projects leveraging self-organizing maps (SOM) for multidimensional data analysis.

Gregory Markowskyمشاهده پروفایل

مدرس ارشد

• Stochastic processes
• Complex analysis
• Graph theory

Gregory Markowsky serves as a Senior Lecturer in the School of Mathematics at Monash University, maintaining an active research profile with continuous publication output through 2025. His academic appointment shows no indication of part-time status, emeritus designation, or former staff classification. His research centers on three interconnected domains: Stochastic processes, particularly planar Brownian motion and random walk theory Complex analysis with applications to probabilistic models Graph theory focusing on algebraic and combinatorial structures These areas converge in his investigation of probabilistic phenomena with geometric interpretations. Recent publication trends reveal sustained productivity with 61 research outputs cataloged, including 4 articles in 2025 alone. His work demonstrates consistent methodological focus on stochastic analysis and combinatorial structures, with increasing cross-disciplinary applications in forensic science and econometrics as evidenced by 2024-2025 publications. Markowsky has secured three significant research projects funded by the Australian Research Council, including: "Planar Brownian motion and complex analysis" (2014-2017) as Primary Chief Investigator "Finite Markov chains in statistical mechanics and combinatorics" (2014-2017) "AMSI Industry Internship: Melbourne Storm" (2013) His ORCID profile (0000-0003-1656-337X) documents extensive scholarly activity with international collaborations. Professional engagement includes contributions toward UN Sustainable Development Goals through mathematical applications, though specific goal alignments aren't detailed in available materials. His email contact Greg.Markowsky@monash.edu remains active with current research dissemination.

James Parkinsonمشاهده پروفایل

دانشیار

• combinatorial representation theory
• random walk theory
• Hecke algebras
• +۹ مورد دیگر

Associate Professor James Parkinson is affiliated with the School of Mathematics and Statistics at the University of Sydney . He is a member of the Algebra and Geometry, Topology, and Analysis research groups, focusing on combinatorial representation theory , random walk theory , and structures like Hecke algebras , buildings , and Kac-Moody groups . Education : PhD in Mathematics from the University of Sydney (2005). His research intersects algebra , geometry , and combinatorics , particularly in buildings and Coxeter groups. Recent publications explore automorphisms, opposition diagrams, and asymptotic properties in Hecke algebras, as well as applications to probability theory and graph analysis . He has secured significant grants from the Australian Research Council in 2020 for Hecke algebras research and in 2011 for studies on loop groups. Parkinson contributes to journals like Bulletin of the Australian Mathematical Society and Innovations in Incidence Geometry .

Pascal Sunمشاهده پروفایل

مدرس پاره‌وقت

• Computer Science
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Pascal Sun is a PhD student in the School of Physics, Mathematics and Computing at The University of Western Australia, concurrently serving as a Casual Teaching Lecturer and Research Assistant at the Centre for Software Practice. His research focuses on advanced topics in machine learning and data mining, including graph representation learning, knowledge graph construction, and multimodal sentiment analysis. He has contributed to peer-reviewed publications at prestigious venues such as the ACM Web Conference and IEEE Transactions on Affective Computing. His work explores innovative applications of graph embeddings, human-LLM collaboration systems, and methods for handling non-IID data challenges. Education: Multi-Discipline Doctor of Philosophy (current) Award recognition includes the 2024 Best Demo Award and the 2023 Students Choice Award for enhancing learning experiences through academic programs. Collaborations involve researchers like Prof. Wei Liu and Dr. Du Huynh, with contributions to both theoretical advancements and practical systems development in AI and data science domains.

• 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

• Quantum Cryptography
• Data Structures and Algorithms
• Quantum Algorithms
• +۳ مورد دیگر

Dr. Troy Lee is an Associate Professor of Quantum Cryptography at the Centre for Quantum Software and Information within the Faculty of Engineering and Information Technology at the University of Technology Sydney . His research focuses on quantum algorithms, computational complexity, and graph theory, with particular emphasis on quantum-classical separations and query complexity. Education: Not explicitly mentioned Research Areas: Quantum algorithms, computational complexity, graph theory, quantum cryptography, and Boolean function analysis Teaching: Supervised the course Data Structures and Algorithms in 2022 Grants: Currently involved in quantum algorithm design and defense optimization projects (2025-2028, 2021-2024) His recent publications highlight advancements in quantum query complexity, graph algorithms, and exact learning techniques. Notably, his work includes quantum speedups for graph connectivity problems and improved bounds for Fourier-sparse function learning. Dr. Lee maintains active collaborations across theoretical computer science and quantum computing domains, contributing to both foundational and applied research in quantum software development.

Anthony Dooleyمشاهده پروفایل

استاد

• Modern Analysis
• Harmonic Analysis
• Dynamical Systems
• +۲ مورد دیگر

Prof Anthony Dooley is a Professor and Head of the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS). He previously held positions at the University of Bath (UK) and UNSW, serving as Head of School and leading strategic roles. His research focuses on Modern Analysis, including harmonic analysis and dynamical systems, with over 90 peer-reviewed publications and continuous ARC grants for 20 years. He has supervised 18 PhD students and held visiting roles at institutions like Yale, Kyoto University, and the Mittag-Leffler Institute. Education: PhD from ANU; Diplôme des Études Approfondies from Paris 6. Awards: Fellow of the Australian Institute of Company Directors. Research interests span ergodic theory, Lie groups, and stochastic processes. Contributions include work on non-singular ergodic theorems, G-measures, and entropy in dynamical systems. Service roles: Board memberships at NIDA, Australian Graduate School of Management, and international research panels. Grants: Chief Investigator for the ARC Centre of Excellence for Mathematics and Statistics of Complex Systems (MASCOS). Active in governance across academia and research institutions.

Matthias Fresacherمشاهده پروفایل

مدرس

• Abstract Algebra
• Semigroups and Monoids
• Diagram Monoids
• +۴ مورد دیگر

Matthias Fresacher is a PhD candidate in Pure Mathematics at Western Sydney University under Assoc. Prof. James East, researching diagram monoids and their generalizations. He holds a Master's and Bachelor's from the University of Adelaide, with an exchange at the National University of Singapore. His academic roles include Lecturer at Western Sydney, tutor at UTS, and Indigenous academic mentor at the University of Adelaide. Education: PhD (Pure Mathematics), Western Sydney University (2022–ongoing) MPhil (Pure Mathematics), University of Adelaide (2019–2021) Bachelor of Mathematical Sciences (Advanced), University of Adelaide (2016–2018) Exchange at National University of Singapore (2018) Research Interests: Abstract algebra, semigroups/monoids, diagram monoids, category theory, algorithms, and interdisciplinary collaborations. His publications span theoretical mathematics and applied computing, with recent focus on categorical representations and diagram monoid properties. He has received awards including the AustMS Bernhard Neumann Prize and AAG Gordon Preston Prize. Active in teaching, supervision, and academic leadership, he co-founded the Association of Australian Mathematics Students (AAMS) and serves on various committees.

Manou Rosenbergمشاهده پروفایل

پژوهشگر ارشد

• Optimization
• Energy Infrastructure
• Evolutionary Algorithms
• +۳ مورد دیگر

Dr Manou Rosenberg is a Research Fellow at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), part of the Faculty of Science and Engineering. Their work focuses on optimization and energy systems, particularly in hydrogen infrastructure networks, electricity distribution networks, and evolutionary algorithms. Rosenberg is affiliated with the Centre for Optimisation and Decision Science. Research interests include applying optimization techniques to solve complex engineering problems in energy infrastructure, rural electrification, and renewable energy systems. Their work often integrates evolutionary algorithms with geospatial analysis and machine learning methods. Publications emphasize innovative solutions for hydrogen pipeline design, rural electricity networks, and microgrid optimization. Recent work explores multi-stage approaches for spatially-aware infrastructure planning and the application of genetic algorithms to network design challenges. Rosenberg collaborates with institutions and researchers in Australia, contributing to projects that bridge theoretical optimization and practical energy system applications. They are based at the Curtin Perth campus in the New Technologies Building, Room 463.

• Coding and Information Theory
• Cryptography and Cryptanalysis
• Information Security
• +۶ مورد دیگر

Associate Professor Serdar Boztas is affiliated with RMIT University's School of Science in Australia. His academic rank is Associate Professor. He is actively involved in teaching Information Theory, Cryptography and Security, Advanced Topics in Cryptography, and Smartcard Cryptosystems. His research interests span Coding and Information Theory, Cryptography, Information Security, Wireless Communications, and Complex Networks. Research highlights include work on Reed-Solomon codes, insider threat detection, and sequence design with applications in cryptography and signal processing. His recent publications focus on error correction, network security, and algorithmic improvements in coding theory. He has supervised PhD projects on Lightweight Encryption and Insider Threat Mitigation, with notable completed projects in malware analysis and cryptographic countermeasures. Service & Engagement: Committee roles in IEEE workshops, international conferences, and advisory boards (e.g., ISC, AAECC, SECIA). Teaching: Courses emphasize Information Theory, Cryptography, and Security applications. Grants & Funding: Active in securing research funding for projects in cybersecurity and communication systems. Boztas' work bridges theoretical foundations with practical applications in information security, network efficiency, and cryptographic systems. His contributions to sequence design and threat detection methodologies are widely recognized in the field.

Serge Gaspersمشاهده پروفایل

استاد

• Algorithms and complexity
• Graph algorithms
• Quantum algorithms
• +۸ مورد دیگر

Professor Serge Gaspers is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), specializing in algorithms for computationally intractable problems. His research focuses on parameterized algorithms, quantum algorithms, and graph theory, with applications in computational social choice and constraint satisfaction. He joined UNSW in 2012 as an ARC DECRA Fellow and later held an ARC Future Fellowship. Gaspers obtained his PhD from the University of Bergen (Norway) in 2008, followed by postdoctoral positions in Montpellier, Santiago, and Vienna. His research interests include algorithms for NP-hard problems, quantum computing, and fair resource allocation. He has been awarded grants totaling over A$2 million, including an ARC Discovery Project (DP210103849) on improved algorithms via random sampling and collaborations with Data61/CSIRO and NICTA. Notable awards include the ARC Future Fellowship (2014), IJCAI 2013 Most Educational Video Award, and DECRA (2012). Teaching: Gaspers teaches COMP6741 - Algorithms for Intractable Problems . His advising spans parameterized algorithms, quantum algorithms, and graph algorithms. Research grants highlight his work in algorithms, with a focus on turbocharging heuristics and computational complexity of resource allocation problems.

• Parameterized Complexity
• Computational Complexity
• Algorithmics
• +۶ مورد دیگر

Luke Mathieson is a Senior Lecturer and Deputy Head of School (Teaching and Learning) in the School of Computer Science at the University of Technology Sydney. His academic career spans theoretical computer science with a focus on computational complexity and its applications. Dr. Mathieson's educational background includes a PhD in Theoretical Computer Science from Durham University, a Masters and Postgraduate Diploma in Higher Education from Macquarie University, and dual Bachelor's degrees in Computer Science (Honors) and Science (Chemistry) from the University of Newcastle Australia. His research interests are centered on parameterized complexity and its applications, extending to various areas of complexity theory, algorithmics, quantum computing, graph theory, and related mathematics. A major theme of his research is the complexity of graph editing problems, a topic in which he specializes. His recent work bridges theoretical complexity with practical applications in AI education, network science, and quantum computing. Dr. Mathieson has taught an extensive range of computer science subjects, particularly focusing on the theory of computation, computational complexity, and algorithmics. At UTS, he teaches or has taught subjects including Data Structures and Algorithms, Applications Programming, Computing Science Studio, Theory of Computing Science, Programming, and Advanced Algorithms. He serves as the Course Director for the Bachelor of Science in Information Technology suite of degree programs and the Course Coordinator for the IT Core. Senior Lecturer, University of Technology Sydney, School of Computer Science (2022-present) Lecturer, University of Technology Sydney, School of Computer Science (2021-2022) Scholarly Teaching Fellow, University of Technology Sydney, School of Computer Science (2017-2021) Research Associate, University of Newcastle Australia, Centre for Information Based Medicine (2014-2017) Adjunct Lecturer, Macquarie University, Department of Computer Science (2014) Postdoctoral Fellow, Macquarie University, Department of Computer Science (2011-2013) Research Associate, University of Newcastle Australia, School of Electrical Engineering and Computer Science (2010-2011) His research demonstrates consistent productivity across theoretical computer science with notable contributions to parameterized complexity and network controllability. Recent publications show an expanding scope incorporating quantum computing applications and educational technology innovations. The QB-suite: a framework for quantum algorithm design and benchmarking (2024-2027) National Industry PhD Program: Improving biosecurity through livestock history recording (2024-2028) Random Number Generation and Analytics for Client Understanding (2018-2019) He maintains active research collaborations across multiple institutions and is affiliated with the Faculty Centre for Quantum Software and Information (QSI) at UTS, reflecting his growing involvement in quantum computing research.

Bernard Mansمشاهده پروفایل

استاد

• Algorithms and Complexity
• Graph Theory
• Distributed Computing
• +۴ مورد دیگر

Professor Bernard Mans is a Professor at the School of Computing, Macquarie University. His research focuses on algorithms, dynamic networks, graph theory, and pandemic modeling. He served as Interim Executive Dean of the Faculty of Science and Engineering (2019–2020) and co-founded the Centre for Advanced Computing: Algorithms and Cryptography (ACAC). Mans holds the INRIA-Hitachi Chair (2003) and leads projects on document intelligence, vehicular networks, and dynamic network algorithms. Education: PhD in Computer Science from Université Pierre et Marie Curie (UPMC), Paris, France (1992). Research Interests: Design and analysis of algorithms for dynamic graphs/networks, wireless/mobile networks (vehicular/human mobility), functional graphs on finite fields, and disease propagation modeling. His work bridges theoretical computer science with practical applications in urban networks and blockchain energy efficiency. Recent Projects: Scalable Document Intelligence Platform (2022–2027), Future Web Management (ongoing), and Asynchronous Algorithms in Dynamic Networks (2016–2021). Notable Contributions: Pioneered studies on information dissemination in vehicular networks using hyperfractal models, developed vaccination strategies for dynamic networks, and advanced blockchain moderation techniques. His publications span algorithm design, network optimization, and pandemic modeling. Awards: INRIA-Hitachi Chair (2003). Labs/Teams: Co-founder of ACAC and affiliated with Data Horizons and Future Communications Research Centres at Macquarie University.