جستجوی استاد راهنمای دانشگاهی

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.

• Intelligent monitoring and maintenance of civil infrastructure
• Optimum asset maintenance and management
• Structural reliability analysis and safe life prediction of structures and infrastructure
• +۴ مورد دیگر

Dr. Mojtaba Mahmoodian is a Senior Lecturer in Civil and Infrastructure Engineering at RMIT University's School of Engineering, City Campus, Australia. He is a Chartered Engineer in both Civil Engineering and Asset Management with Engineers Australia. His work spans diverse infrastructure types including bridges, pipelines, railways, buildings, ports, and offshore platforms. Dr. Mahmoodian's research focuses on intelligent monitoring and maintenance of civil infrastructure, optimum asset maintenance and management, structural reliability analysis and safe life prediction of structures, and deterioration modeling of structures and infrastructures. His work integrates advanced technologies including digital twins, artificial intelligence, and machine learning to enhance infrastructure monitoring and management systems. He has developed innovative approaches for corrosion modeling, structural health monitoring, and real-time structural integrity assessment. His recent publications demonstrate a strong trend toward the integration of digital twin technology with artificial intelligence for infrastructure monitoring. The research spans civil infrastructure domains including bridges, railways, ports, and pipelines, with emphasis on real-time monitoring, predictive maintenance, and optimal sensor placement. His work shows increasing sophistication in applying machine learning techniques to accelerate finite element analysis and improve structural assessment accuracy. RMIT Vice-Chancellor's Award for Outstanding Contributions in Learning and Teaching (2019) Chartered Engineer in Civil Engineering and Asset Management (2019) Chair of special sessions at major international bridge conferences (IABMAS 2018, 2020) Conference chair for ICWSS 2017: International Conference on Water Supply Systems Kharazmi Festival Award for innovation in concrete testing (2004) Dr. Mahmoodian actively supervises Masters and PhD research students, with current projects focusing on AI-enabled health monitoring of bridge structures through digital twin technology, computer vision for damage detection, automatic condition assessments of civil infrastructure, and intelligent monitoring and maintenance systems. His research has significant practical applications for infrastructure asset management and maintenance optimization. His work involves collaboration with various research teams focusing on structural health monitoring, digital twin development, and AI applications in civil engineering. These teams work on developing innovative monitoring systems that integrate sensor networks, data analytics, and visualization tools to provide comprehensive infrastructure assessment capabilities.

Zoltan Kocsisمشاهده پروفایل

مدرس

• Mathematical Logic
• Set Theory
• Lattices
• +۲۸ مورد دیگر

Dr. Zoltan Kocsis is a mathematical logician and educator based at the University of New South Wales (UNSW), where he currently holds the position of Adjunct Lecturer. His research spans multiple areas including proof theory, nonstandard analysis, category theory, and computational logic, with a focus on logical verification, degree of satisfiability, and structured decompositions. PhD in Mathematics from the University of Manchester (2019) Adjunct Lecturer at UNSW Work with interactive theorem proving software like Agda Dr. Kocsis' work on degree of satisfiability explores the probability of logical formulas holding in algebraic structures, revealing gaps in equations from Heyting algebras to group theory. His research on structured decompositions and spined categories provides categorical frameworks for fixed-parameter tractability and tree-width generalizations. In formal verification, he contributed to the seL4 kernel on RISC-V architecture. Recent publications include Proof-theoretic methods in quantifier-free definability (2025), Apartness relations between propositions (2024), and Degree of satisfiability in Heyting algebras (2024). Earlier works cover pseudo-random sequences, homology, and nonstandard analysis in group theory. He has received the CSIRO SCS Engineering and Technology award (2021) and the IBM Prize (2018) for his contributions to logical satisfiability. His collaborations include work with Ben Bumpus on spined categories and Jade Master on structured decompositions, alongside teams at CSIRO and Tallinn University of Technology.

Andi Hanمشاهده پروفایل

مدرس

• Large Generative Models
• Riemannian Optimization
• Efficiency of Foundation Models
• +۳ مورد دیگر

Andi Han is a Lecturer in Data Science at the School of Mathematics and Statistics, University of Sydney . He earned his PhD in Business Analytics from the University of Sydney Business School in 2023 and served as a postdoctoral researcher at RIKEN AIP’s Continuous Optimization Team until 2025. Research Interests: Large generative models (diffusion models, large language models) Optimization on manifolds Efficiency of foundation models Graph neural networks for biology and chemistry Awards: DAAD AInet Fellowship (2025) PhD Completion Award (USYD, 2023) Best Paper Award (IEEE SCCI, 2022) University Medal (USYD, 2019) Business Analytics Prize (USYD, 2018) Teaching: STAT5002: Introduction to Statistics (Unit Coordinator & Lecturer, S2 2025) MATH1061: Mathematics 1A (Lecturer, S2 2025) His recent publications focus on Riemannian optimization techniques, diffusion models, and graph neural networks (GNNs), with applications in protein sequence generation, transformer optimization, and AI for science. Collaborative work spans institutions like RIKEN, Zhejiang Lab, and A*STAR. He actively organizes workshops, including Deep Generative Model in Machine Learning: Theory, Principle and Efficacy at ICLR 2025.

• Extremal Graph Theory
• Probabilistic Graph Theory
• Combinatorics
• +۵ مورد دیگر

Bertille Granet is a researcher at Monash University, specializing in extremal and probabilistic graph theory. Their work involves deep structural analysis of graphs and random graph models, with publications in leading combinatorics journals. Research Interests: Extremal Graph Theory Probabilistic Graph Theory Random Graphs Graph Decompositions Tournaments Hamilton Cycles The recent research output demonstrates a strong focus on decomposition problems in graphs and digraphs, probabilistic methods in combinatorics, and structural properties of regular and random graphs. The work often involves collaboration with leading figures in the field, indicating integration into major research networks. Scientific Output Trends: Decomposition of graphs into paths and cycles Hamiltonicity in regular and bipartite tournaments Random graph processes and hitting times Conjectures in directed graphs (e.g., Seymour's conjecture) Perfect matchings in regular graphs Extremal density problems Scientific Awards: Advising and Grants: No information is available regarding supervision of students or grant funding. Labs and Research Teams: There is no explicit mention of labs or formal research teams. However, the high level of collaboration suggests active participation in international research networks in combinatorics.

Marika Kieferovaمشاهده پروفایل

مدرس ارشد

• Quantum Computing
• Quantum Algorithms
• Quantum Simulation
• +۳ مورد دیگر

Marika Kieferova is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), and a researcher at the UTS Centre for Quantum Software and Information (QSI). She previously held a postdoctoral position at UTS and earned her PhD in Physics and Astronomy from the University of Waterloo (2019) with a cotutelle from Macquarie University. Research Interests Her work spans quantum computing, quantum simulation, and quantum information theory. Key areas include developing quantum algorithms for Hamiltonian simulation, error mitigation strategies, and entanglement-induced optimization challenges in quantum neural networks. She explores non-Abelian anyon braiding, engineered dissipation for correlated states, and bound states of interacting photons in superconducting qubit arrays. Article Trends Her recent publications focus on quantum dynamics in many-body systems, error suppression techniques, and algorithmic advancements. Topics include phase transitions in random circuits, superdiffusive quantum transport, and randomized multi-product formulas for efficient simulation. These works highlight her contributions to quantum chemistry, topological quantum computing, and NISQ-era applications. Scientific Awards QIP Best Poster Award (2020) IQC Achievement Award (2019) Grants and Leadership She leads the QB-suite grant for quantum algorithm design (2024-2027) and contributes to defense quantum optimization projects (2021-2024). She serves as an associate editor for Quantum Science and Technology and participates in peer review for Physical Review A.

• Computational complexity theory
• Algebraic computation
• Cryptography
• +۵ مورد دیگر

Youming Qiao is an Associate Professor at the School of Computer Science, University of Technology Sydney (UTS), specializing in theoretical computer science. He obtained his PhD from Tsinghua University in 2012 under Andrew Yao and László Babai, followed by postdoctoral work at Singapore's Centre for Quantum Technologies. His research spans computational complexity, algebraic computation, cryptography, quantum information, and group theory, with a focus on tensor isomorphism, group isomorphism algorithms, and their applications in post-quantum cryptography. PhD in Computer Science, Tsinghua University (2012) Bachelor's in Computer Science, Tsinghua University Postdoc at Centre for Quantum Technologies, National University of Singapore His recent work includes polynomial-time algorithms for matrix tuple canonical forms, improved p-group isomorphism testing, and novel reductions bridging tensor isomorphism with graph theory. He actively supervises Master's and PhD students, including Euan Mendoza and Gang Tang, and contributes to quantum algorithms and complexity theory through grants like the ARC Discovery Project (2020-2024) and SQA Scholarships. Qiao is affiliated with UTS's Centre for Quantum Software and Information (QSI) and has developed practical post-quantum signature schemes.

Anita Liebenauمشاهده پروفایل

دانشیار

• Combinatorics
• Graph Theory
• Extremal Combinatorics
• +۵ مورد دیگر

Associate Professor Anita Liebenau is affiliated with the School of Mathematics and Statistics at UNSW Sydney , where she joined in 2018. Her academic journey includes a DECRA Fellowship , postdoctoral positions at Monash University and University of Warwick , and a PhD from the Free University Berlin under Tibor Szabó. Her research focuses on Extremal combinatorics Probabilistic combinatorics Asymptotic enumeration Random graphs Ramsey theory Combinatorial games on graphs Her work analyzes large discrete structures, clique-cycle Ramsey properties, and threshold biases in tournament games, often intersecting with asymptotic methods and hypergraph theory. Recent publications highlight trends in Sidorenko configurations , multicolor Turán problems , universal graph structures , and asymptotic enumeration of graphs and hypergraphs. These contributions blend probabilistic techniques with extremal combinatorics and game-theoretic approaches. Key scientific awards include the DECRA Fellowship , recognizing her early-career research excellence. She supervises projects in Extremal combinatorics Asymptotic enumeration Graphs and hypergraphs Contact details: Email a.liebenau@unsw.edu.au, Location The Red Centre, Room 6105, UNSW Sydney, NSW 2052, Australia.

Kihun Namمشاهده پروفایل

مدرس

• Stochastic Analysis
• Backward Stochastic Differential Equations
• Financial Mathematics
• +۳ مورد دیگر

Kihun Nam is a Lecturer in the School of Mathematics at Monash University, specializing in stochastic analysis with applications to mathematical finance and game theory. His research focuses on backward stochastic differential equations (BSDEs) and their applications in derivative pricing, stochastic control, and mean-field games. His primary research areas include: Backward Stochastic Differential Equations theory Stochastic control and optimization Financial mathematics and derivative pricing Mean-field game theory Machine learning for PDEs Nam's publications demonstrate deep expertise in BSDE theory, with recent expansion into financial applications and game-theoretic modeling. His work combines theoretical advances in stochastic analysis with practical applications in finance, particularly in pricing models and market microstructure. He has received multiple awards including the Academic Initiative Program Award (2015, 2016) and Samsung Scholarship for his PhD studies. Nam currently leads research projects including 'Can green investors drive the transition to a low emissions economy?' funded through Monash University's Centre for Quantitative Finance and Investment Strategies. His work involves collaborations across mathematical finance and sustainability economics.

• Human Performance
• Sports Science
• Data Science
• +۴ مورد دیگر

Andrew Novak is a Senior Research Fellow at the University of Technology Sydney (UTS), affiliated with the School of Sport, Exercise and Rehabilitation and the Human Performance Research Centre (HPRC). His research focuses on human performance in sports, esports, and military settings, emphasizing data-driven approaches using tools like Tableau, R, and AWS. He collaborates with Rugby Australia, the Australian Defence Science and Technology Group, and the Office of Naval Research Global. Novak holds a PhD from the University of Newcastle and has published over 50 peer-reviewed papers. He is accredited as a Level 2 Sports Scientist and Exercise Scientist. Teaching roles include co-developing the 'Performance Analysis and Data Science' subject in UTS's Masters of High Performance Sport program. His research interests span performance analysis, team dynamics, and decision-making, with recent work on environmental impacts on football performance, cooperative networks in invasion games, and esports perceptual-motor skills. He has secured grants for projects like applying machine learning to cricket performance and military team dynamics. Award highlights include a runner-up for the best sports science research poster (2016). Professional experience includes roles at Fusion Sport, developing athlete management software, and bicycle fitting expertise through Specialized Bicycles.

• Neuroscience
• Neuroimaging
• Substance Use Disorders
• +۷ مورد دیگر

Valentina Lorenzetti is a Professor at the School of Behavioural and Health Sciences within the Faculty of Health Sciences . Her research focuses on understanding the neurobiological and behavioral impacts of substance use disorders, particularly cannabis use, through advanced neuroimaging techniques like fMRI and MRI. Key Affiliations ENIGMA Addiction Working Group International Cannabis Toolkit (iCannToolkit) Consortium Collaborative studies across multiple countries Research interests center on cannabis-related neuroanatomical changes , brain functional connectivity , addiction neuroscience , and standardization of cannabis dosage for public health policy. She investigates how chronic cannabis use affects brain morphology (hippocampus, amygdala), cognitive function, and mental health outcomes. Recent studies include systematic reviews on real-time neurofeedback for addiction , THC unit standardization , and cross-disorder comparisons of brain abnormalities . Her work addresses sex differences in substance use , transdiagnostic impulsivity/compulsivity , and neuroimaging methodologies . Collaborations span institutions studying alcohol dependence , pathological gambling , and obesity-related brain changes . She contributes to policy frameworks for legal cannabis markets and has developed tools like the iCannToolkit to standardize cannabis use measurement across cultural contexts.

• 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.

Jonathan Spreerمشاهده پروفایل

دانشیار

• Low-Dimensional Topology
• Combinatorics
• Computational Geometry
• +۳ مورد دیگر

Jonathan Spreer is an Associate Professor at the School of Mathematics and Statistics, The University of Sydney , where he has held positions since 2021. His research bridges low-dimensional topology , combinatorics , and parameterized complexity theory , with applications in mathematical software development . Education : Doctorate in natural sciences (2011, University of Stuttgart; advisor: Prof. W. Kühnel) Diploma in mathematics and computer science (2008, University of Stuttgart) Maîtrise in mathematics (2007, Université Pierre et Marie Curie) Research themes include: Tight triangulations : Generalizing convexity in discrete geometry, with recent focus on 3- and 4-manifolds. 3- and 4-manifold topology : Computational methods for minimal triangulations and crosscap number algorithms. Graph-encoded manifolds : Studying combinatorial representations of manifolds via colored graphs. Parameterized complexity : Developing efficient algorithms for topological problems under bounded parameters. Scientific contributions : Lead developer of the simpcomp software package for simplicial complex analysis in GAP. Key publications on Turaev-Viro invariants, JSJ decomposition complexity, and quantum invariants of 3-manifolds. ARC Discovery Project grants for Triangulations: linking geometry and topology with combinatorics (2022) and Trisections, triangulations and the complexity of manifolds (2019). Teaching and supervision spans computational geometry, topology, and related fields. Current research students include William Hobkirk, Damian Lin, and James Morgan, working on problems in 3-manifold topology and algorithmic complexity.

Ali Eshraghمشاهده پروفایل

مدرس ارشد

• Time Series Forecasting
• Stochastic Modeling
• Operations Research
• +۶ مورد دیگر

Dr. Ali Eshragh is an Honorary Senior Lecturer at the University of Newcastle's School of Information and Physical Sciences, specializing in statistical modeling and machine learning for big data analysis. His research focuses on developing automated forecasting systems to analyze big time series data and generate accurate future predictions, with applications spanning energy demand forecasting, supply chain optimization, and pandemic modeling. Dr. Eshragh holds a PhD from the University of South Australia and a BSc from Sharif University of Technology in Iran. His research expertise spans Time Series Forecasting (30%), Operations Research (30%), and Stochastic Analysis and Modeling (40%), as reflected in his Fields of Research codes. He has established the ForBiD (Forecasting and Big Data) research group, which has rapidly grown to include members from the University of Newcastle, University of Queensland, Monash University, UC Berkeley, and Yale University. His publication record demonstrates a strong trajectory in applying advanced statistical methods to real-world problems, with recent work focusing on reinforcement learning for combinatorial optimization, efficient algorithms for big time series data analysis, and probabilistic modeling of complex systems like pandemics. His research bridges theoretical advances in randomized numerical linear algebra with practical applications in forecasting. Dr. Eshragh has received the Australian Society for Operations Research 2017 Rising Star Award and has secured over $3.6 million in research funding from various sources including the Australian Research Council, Department of Education, and industry partners like Coca Cola Amatil and Sanitarium. He actively supervises PhD and honors students in areas including reinforcement learning algorithms, big time series data analysis, and Markov decision processes. His industry collaborations with Australian food and beverage supply chains have directly motivated his academic research, reflecting his commitment to translating theoretical advances into practical solutions.

Lei Chenمشاهده پروفایل

استاد

• Data-Driven Machine Learning
• Crowdsourcing-based Data Processing
• Web Data Management
• +۶ مورد دیگر

Lei Chen is a Chair Professor at the Department of Computer Science and Engineering , Hong Kong University of Science and Technology (HKUST) , where he serves as Director of the Big Data Institute . He received his PhD from the University of Waterloo after completing BS and MS degrees at Tianjin University and Asian Institute of Technology , respectively. As an IEEE Fellow and ACM Distinguished Scientist , his research spans Data-Driven Machine Learning , Crowdsourcing-based Data Processing , Uncertain Databases , and Knowledge Graphs . His work on graph neural networks , spatial crowdsourcing , and privacy-preserving systems has earned multiple best paper awards at top venues like VLDB , SIGMOD , and KDD . Current research focuses on agentic neural graph databases and large language model optimization . His 15 most recent publications (2022-2025) address: Secure spatial query systems (Hu-Fu) Decentralized graph neural network training (Sancus) Explainable AI frameworks Temporal graph learning Polynomial activation functions for weather forecasting Knowledge graph applications in recommender systems Scientific Awards: VLDB Best Research Paper (2022) SIGMOD Test-of-Time Award Google Faculty Award (2013) Multiple conference best paper awards He has supervised 40+ PhD/MPhil students , many of whom now hold academic/research positions at institutions like BeiHang University , Sun Yat-sen University , and Huawei Noah's Ark Lab . His group ( Database Research Group ) leads projects in graph neural networks , knowledge graphs , and blockchain systems .