جست‌وجوی استاد و پژوهشگر

• Neuroscience
• Complex Systems Analysis
• Statistical Physics
• +۳ مورد دیگر

Ben Fulcher is an Associate Professor and Senior Lecturer in the School of Physics at The University of Sydney, leading the Dynamics and Neural Systems Group. His research focuses on applying statistical and physics-based methods to study complex systems, with a strong emphasis on neuroscience. He investigates brain structure and function dynamics, particularly exploring the interplay between genetics, neural connectivity, and information processing mechanisms. Fulcher has pioneered tools like hctsa (highly comparative time-series analysis) for analyzing large-scale neuroimaging and physiological data. Research Interests Complex systems analysis Neural dynamics and connectomics Feature-based time-series analysis Gene-brain connectivity relationships Imaging transcriptomics Recent Work Trends Fulcher's recent articles emphasize time-series analysis applications in neuroscience, including studies on anesthesia effects, brain connectivity, and criticality in neural systems. His work bridges computational methods (e.g., generative models, matrix-product states) with biological insights, often involving interdisciplinary collaborations. Key themes include understanding how brain geometry influences dynamics and translating genetic data into connectomic predictions. Grants & Projects 2024: ARC Future Fellowship for sleep research using complex systems methods 2023: Discovery Project on network neuroscience and AI integration Advising & Labs Fulcher supervises research students and directs the Dynamics and Neural Systems Group, which develops open-source tools like the theft R package for time-series analysis. His lab’s work has been featured in media outlets discussing brain structure-function relationships and comparative neurobiology.

• 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.

Joung Ohمشاهده پروفایل

دانشیار

• Rock Mechanics
• Tunnelling Support Systems
• Numerical Modelling
• +۵ مورد دیگر

Associate Professor Joung Oh is affiliated with the School of Minerals and Energy Resources Engineering at the University of New South Wales (UNSW), where he teaches and conducts research in mining geomechanics , tunnelling , and rock mechanics for caving mechanisms . His expertise spans rock joint behavior, ground-structure interaction, and numerical modelling, with prior industry experience at Parsons Brinckerhoff in New York. PhD in Civil Engineering (Geotechnical) from University of Illinois at Urbana-Champaign Research focuses on borehole breakout analysis , hydromechanical coupling , and machine learning applications in stress estimation . Recent work includes nonlinear flow in rock fractures , shotcrete bonding mechanisms , and ground support systems in burst-prone areas . Publications emphasize numerical methods , rock anisotropy , and seismic event cascades in mining contexts. Scientific contributions include 15+ recent articles on topics like granite microcrack characterization , seismicity during hydraulic fracturing , and multiscale stress estimation . These works integrate acoustic emission tomography , UDEC-GBM simulations , and artificial neural networks for geotechnical analysis. Professional service involves editorial roles at International Journal of Mining Science and Technology and memberships in societies like the Society of Mining Professors and Australasian Tunnelling Society . Grants from ACARP funded projects on shotcrete effectiveness and horizontal stress estimation in coal mines.

David Kellermannمشاهده پروفایل

مدرس ارشد

• Continuum Mechanics
• Computational Mechanics
• Advanced Composite Materials
• +۷ مورد دیگر

David Kellermann is a Senior Lecturer in the School of Mechanical and Manufacturing Engineering at the University of New South Wales (UNSW). His research focuses on advanced composite materials, continuum mechanics, and nonlinear finite element analysis, with particular emphasis on orthotropic and hyperelastic material modeling. He has contributed significantly to the understanding of free-edge effects in laminates and fiber kinematics in biological tissues. Dr. Kellermann teaches courses such as ENGG1300 (Engineering Mechanics), ENGG2400 (Mechanics of Solids), and MECH4320 (Engineering Mechanics 3). His educational innovations, including remote learning strategies, earned him the 2018 VCATE Vice-Chancellor's Award for Teaching Excellence. His research interests span computational mechanics, buckling analysis, and material characterization. Notable contributions include developing fourth-order tensor methods for anisotropic materials and finite element frameworks for free-edge singularity analysis. He has published extensively in journals like Mathematics and Mechanics of Solids and Composites Part A . Dr. Kellermann has collaborated on projects involving micropolar modeling of composite interfaces and hyperelastic constitutive laws for biological tissues. His work bridges theory and application, addressing challenges in structural stability and material behavior under extreme conditions.

Mohammad Sedaghatمشاهده پروفایل

پژوهشگر ارشد

• Petroleum Engineering
• Reservoir Simulation
• Enhanced Oil Recovery
• +۳ مورد دیگر

Mohammad Sedaghat is an Industry Fellow at the Gas and Energy Transition Research Centre at The University of Queensland. His research focuses on petroleum engineering, reservoir characterization, and energy transition technologies, with particular expertise in fluid flow mechanisms, carbon capture and storage, and enhanced resource recovery methods. Research Interests: Sedaghat's work spans geomechanics, reservoir simulation, and environmental sustainability in energy systems. Primary domains include: Fluid dynamics in fractured reservoirs and coal seams Hydraulic fracturing and permeability enhancement techniques CO₂ sequestration and injectivity modeling Wettability alteration and chemical flooding for oil recovery Mine methane emission mitigation strategies Publication Trends: Recent articles demonstrate a strong focus on numerical modeling of subsurface processes, with recurring themes in carbon storage optimization, unconventional resource recovery, and geomechanical influences on fluid flow. Computational studies frequently employ advanced simulation platforms to address challenges in energy transition and fossil fuel extraction. Collaborations: Actively collaborates on industry and research projects including the University of Queensland Surat Deep Aquifer Appraisal Project (UQ-SDAAP), investigating multiphase flow behavior and managed aquifer recharge systems.

• Data Science
• Machine Learning
• Tensor Factorization
• +۵ مورد دیگر

Dr Thiru Balasubramaniam is a Research Fellow at Queensland University of Technology (QUT), working within the Faculty of Science, School of Computer Science. His expertise lies at the intersection of data science, machine learning, and real-world applications, with a specific focus on tensor factorization methods for managing multifaceted data from IoT and Web 3.0 applications. His educational background includes a PhD from Queensland University of Technology and a Bachelor of Engineering from Anna University. Prior to his doctoral studies, he worked as a Research Assistant at the Singapore University of Technology and Design - Massachusetts Institute of Technology (SUTD-MIT) International Design Centre, where he analyzed mobility data to personalize city environments for elderly citizens in Singapore. Dr Balasubramaniam's research interests span multiple areas of data science: Tensor and Matrix Factorization methods Pattern Mining and Text Mining applications Recommender Systems development IoT data processing Web 3.0 applications Real-time analytics for multifaceted data His publication record demonstrates consistent contributions to high-impact venues including IEEE TKDE, ACM TKDD, WWW, WISE, AusDM, and PRICAI. The trend in his recent work shows increasing application of tensor factorization techniques to diverse real-world problems including environmental monitoring, pandemic modeling, social media analysis, and smart grid technology. His research often involves interdisciplinary collaborations, particularly with Professor Richi Nayak at QUT. Scientific recognition includes: QUT-CDS first byte research funding worth 30,000 AUD Dr Balasubramaniam has been actively involved in teaching data analytics subjects at QUT since 2017, including Data Exploration and Mining, Data and Web Analytics, Data Mining Technology and Applications, and Web Computing. His teaching spans both undergraduate and postgraduate levels. His research has been supported through various collaborative grants and institutional funding mechanisms at QUT.

Guanglu Zhouمشاهده پروفایل

دانشیار

• Numerical Optimization
• Computational Methods
• Complementarity Problems
• +۳ مورد دیگر

Guanglu Zhou is an Associate Professor at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences, within the Centre for Optimisation and Decision Science. He holds a PhD from the University of New South Wales (2000) and has held academic roles at institutions including the National University of Singapore and the University of Birmingham. His research focuses on numerical optimization, computational methods, tensor computation, and industrial applications. Education : BSc, Shandong Normal University (1988) MSc, Qufu Normal University (1992) PhD, University of New South Wales (2000) Research Interests : Numerical Optimization and Computational Methods Tensor Analysis and Applications Complementarity Problems and Mathematical Programming Industrial Problem Solving Key Achievements : Recipient of the ARC Queen Elizabeth II Fellowship (2006-2010) Principal Investigator on ARC Discovery Grants focusing on optimization and computational methods Teaching : Courses in Mathematics, Computational Mathematics, Optimization, and Logistics Modeling. Grants : Led ARC projects on robust optimization methods (2006-2010) and cone optimization applications (2011-2013).

Nan Yeمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Statistics
• Optimization
• +۴ مورد دیگر

Dr. Nan Ye is a Senior Lecturer in Statistics and Data Science at the University of Queensland's School of Mathematics and Physics. His research focuses on machine learning, statistics, and optimization, with contributions to sequential decision making, weakly supervised learning, and probabilistic graphical models. He holds a PhD in Computer Science from the National University of Singapore (NUS) and double first-class honors in Computer Science and Applied Mathematics from NUS. Previously, he held postdoctoral positions at QUT, UC Berkeley, and NUS. Dr. Ye teaches advanced courses such as STAT3007/7007 Deep Learning, covering topics from foundational machine learning to state-of-the-art deep learning architectures and applications. His work has been published in top venues like NeurIPS, ICML, and UAI, earning awards including the IJCAI-JAIR Best Paper Prize (2022) and UAI Best Student Paper Award (2014). His research interests span theoretical and applied machine learning, including reinforcement learning, optimization algorithms, and their applications in fields like healthcare and environmental science. He actively supervises students in these areas and collaborates on interdisciplinary projects. Dr. Ye's academic profile includes extensive contributions to open-source tools and educational materials, reflecting his commitment to advancing both research and pedagogy in data science.

• Algorithms for Groups and Algebras
• Group Theory
• Combinatorics
• +۳ مورد دیگر

Stephen Glasby is an Honorary Research Fellow at the University of Western Australia (UWA), affiliated with the School of Physics, Mathematics and Computing. His research focuses on algorithms for groups and algebras, group theory, and combinatorics. He has contributed to areas such as linear groups, matrix theory, and finite geometries, with a focus on structural properties and computational methods. His work includes studies on automorphism groups, irreducible linear groups, and combinatorial structures like generalized polygons and bipartite graphs. Recent articles highlight his exploration of derangements in permutation groups, probabilistic analysis of subspaces, and optimization of binomial coefficients using continued fractions. Glasby has also investigated functional equations in group theory and random generation techniques in finite geometries. Collaborations span international researchers, and his research has been supported by grants such as the ARC-funded project on finite linearly representable geometries. Despite no listed awards, his extensive publication record (over 45 peer-reviewed articles) and contributions to computational algebra tools (e.g., Magma programs) underscore his scholarly impact. Advising and grant activities include co-investigator roles in projects exploring symmetry and geometry. His work frequently intersects algebra, combinatorics, and probability, with applications in coding theory and cryptography. Ongoing research trends include the study of minimal distance in linear codes and the classification of finite groups with specific automorphism properties.

Khanh Luongمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Data Mining
• Multi-Aspect Data Clustering
• +۷ مورد دیگر

Dr. Khanh Luong is a Research Fellow at the School of Communication within the Faculty of Creative Industries, Education & Social Justice at Queensland University of Technology (QUT). She holds a PhD in Computer Science specializing in Data Science (2019) from QUT. Her research focuses on multi-aspect data, addressing challenges in high-dimensional, sparse datasets through innovative machine learning and data mining methods. She contributes to real-world applications in diverse domains like text, image, and bioinformatics data. Teaching roles include tutoring in Discrete Mathematics, Programming Principles, and Database Management for undergraduate/postgraduate students. She also designed the online unit 'Introduction to Programming' for the Master of IT program. Research interests span machine learning, data mining, multi-aspect data clustering, dimensionality reduction, and unsupervised learning. She works on the Applied Data Science project at QUT's Centre for Data Science, developing solutions for complex industry datasets. Publications emphasize multi-view clustering, manifold learning, and domain adaptation. Collaborations include roles in organizing the 2021 AusDM conference and reviewing for IEEE journals.

King-Wah Chuمشاهده پروفایل

پژوهشگر ارشد

• Numerical Analysis
• Numerical Linear Algebra
• Control Theory
• +۳ مورد دیگر

King-Wah Chu is an Adjunct Senior Research Fellow at the School of Mathematics, Monash University. He has been working in Melbourne, Australia for 30 years. His research focuses on numerical analysis, numerical linear algebra, eigenvalue problems, control theory, and matrix equations. He serves as an Associate Editor for the SIAM Journal on Matrix Analysis and Applications since 2015. Education: Hong Kong and England Affiliations: Monash University, SIAM Journal His research interests include perturbation and numerical solutions of eigenvalue problems, inverse eigenvalue problems, Riccati equations, and large-scale structured computations. He has contributed to the development of algorithms for control systems and tensor computations. Key Projects: Research visit to the International Symposium on Mathematical Theory of Networks & Systems (2000) Advising & Grants: Chu has advised on multiple projects but no specific students are listed. His work involves collaborations on large-scale algebraic Riccati equations and numerical methods. Labs/Teams: Active in the School of Mathematics at Monash University, focusing on numerical linear algebra and control systems.

Daniel O'Sheaمشاهده پروفایل

مدرس

• Computational Mechanics
• Biomechanics
• Continuum Mechanics
• +۴ مورد دیگر

Dr. Daniel O'Shea is a Lecturer in the School of Civil and Environmental Engineering at UNSW Sydney. His primary roles include teaching courses such as ENGG2400 Mechanics of Solids, CVEN4309 Sustainable Timber Engineering, and CVEN2303 Structural Analysis and Modelling. He currently supervises Honours and Masters students in hyperelastic modeling of biological tissues. His research focuses on computational mechanics, biomechanics, continuum mechanics, and advanced finite element methods, with a specialization in anisotropic materials and fibre-reinforced composites. He earned his PhD in 2019 with a thesis on hyperelasticity for biological tissues and composites using fourth-order tensors. Dr. O'Shea’s research activities span hyperelasticity, shell analysis, and structural engineering applications. His work bridges theoretical continuum mechanics with practical engineering challenges, particularly in composite materials and biological tissue modeling. He maintains active collaborations through platforms like LinkedIn, ResearchGate, and Google Scholar, and his publications emphasize innovative solutions for material behavior analysis and computational methods.

• Knowledge Representation and Reasoning
• Quantum Computation
• Artificial Intelligence
• +۳ مورد دیگر

Professor Sanjiang Li is affiliated with the University of Technology, Sydney (UTS) as a Professor in the Faculty of Engineering and Information Technology , specifically within the Centre for Quantum Software and Information . With a PhD in Mathematics from Sichuan University and a BSc from Shaanxi Normal University, his research spans quantum computation , knowledge representation and reasoning , and formal verification of quantum systems . His recent work focuses on quantum circuit transformation , including novel methods like adaptive divide-and-conquer and Monte Carlo Tree Search frameworks. He has advanced symbolic verification techniques using tensor decision diagrams and explored classical-quantum hybrid algorithms for resource-efficient training. Dr. Li has received prestigious awards such as the ARC Future Fellowship and Alexander von Humboldt Research Fellowship . He supervises PhD students including Calum Holker and Guangxi Li , and contributes to quantum software development through funded projects like the Sydney Quantum Academy and ARC Discovery Projects .

• Business Analytics
• High-Dimensional Data Analysis
• Matrix and Tensor Methods
• +۳ مورد دیگر

Affiliation: Zhongyuan Lyu serves as a Lecturer in the Discipline of Business Analytics at the University of Sydney Business School, where he contributes to academic instruction and research within the quantitative analytics domain. Research Expertise: His scholarly work centers on developing advanced methodologies for high-dimensional matrix and tensor data with latent structures. This research bridges theoretical statistics and practical business applications, focusing on pattern extraction from complex datasets through innovative algorithmic frameworks. Current investigations emphasize robust modeling techniques applicable to large-scale commercial data environments where hidden structural relationships require sophisticated analytical approaches.

• Quantum Optics
• Quantum Technologies
• Many-Body Physics
• +۴ مورد دیگر

Sahand Mahmoodian is a theoretical physicist and ARC Future Fellow at the University of Sydney's Faculty of Science, Department of Physics. He is affiliated with the Sydney Nano Institute and focuses on many-body quantum optics, exploring both fundamental phenomena and applications in quantum technologies. His work bridges quantum systems' theoretical foundations with practical advancements in quantum computing and photonics. Previously, he held positions at the Niels Bohr Institute (University of Copenhagen) and Leibniz University Hannover's Institute for Theoretical Physics. His research spans quantum photonics, chiral light-matter interactions, and quantum machine learning. He leads projects funded by the Australian Research Council (ARC), including a Future Fellowship and an ARC Centre of Excellence (EQUS) affiliation. Key research interests include: Quantum many-body systems and their optical manifestations Engineering quantum states using photonic crystals and waveguides Developing quantum technologies via nonlinear optical processes His publications highlight advancements in photon bound states, chiral quantum optics, and quantum machine learning algorithms. He has contributed to experimental collaborations involving nanofiber-coupled atoms and terahertz photonics. His work emphasizes both theoretical innovation and practical device development for quantum applications. Notable awards include the ARC Future Fellowship (2022), recognizing his contributions to quantum science. He actively engages in interdisciplinary research, bridging physics, engineering, and computational methods.