جستجوی استاد راهنمای دانشگاهی

Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.

• Computational Statistics
• Bayesian Statistics
• Big Data
• +۴ مورد دیگر

Professor Hong Hao is a John Curtin Distinguished Professor at Curtin University, affiliated with the School of Civil and Mechanical Engineering and the Curtin Research Centre for Infrastructural Monitoring & Protection. His expertise spans Structural Dynamics, Earthquake Engineering, Blast and Impact Engineering, and Structural Health Monitoring. He holds prestigious roles like Fellow of ATSE, ISEAM, and ASCE, and has led organizations such as the International Association of Protective Structures and the Australian Earthquake Engineering Society. Education: BE (Tianjin University, 1982), MSc (UC Berkeley, 1985), PhD (UC Berkeley, 1989). Awards include the Tan Chin Tuan Fellowship and multiple Ko Medals. He has authored over 200 journal articles, with recent work focusing on blast-resistant materials, seismic fragility, and AI-driven structural health monitoring. His research emphasizes resilient infrastructure, including metaconcrete structures, corrosion-resistant materials, and sensor-based damage detection. Ongoing projects involve smart tunnel safety under BLEVE explosions and modular building systems.

• Structural Dynamics
• Earthquake Engineering
• Blast and Impact Engineering
• +۳ مورد دیگر

Dylan Campbell is a Lecturer in Computing at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. His research focuses on computer vision, optimization, and robotics, particularly in 3D vision and deep learning applications. He has held prior roles as a Research Fellow at the University of Oxford’s Visual Geometry Group and ANU’s Australian Centre for Robotic Vision. Campbell holds a PhD from ANU (2018) and a BE in Mechatronic Engineering from UNSW (2012). Research interests include geometric sensor alignment, neural radiance fields, and differentiable optimization layers. He actively supervises students (7 PhD/DPhil, 3 MEng, 9 honours) and teaches advanced courses in computer vision and robotics. Notable awards include the Marr Prize Honourable Mention (2017) and the IEEE Australia Council Postgraduate Student Paper Competition (2018). He has organized workshops at ECCV and CVPR, served as a reviewer for top conferences like CVPR/ICCV/ECCV, and contributed to datasets like SEED4D and RefRef. His work emphasizes efficient training of neural networks and leveraging symmetries in data for long-range connections.

• Computer Vision
• Optimization
• Machine Learning
• +۳ مورد دیگر

Dr. Ehsan Abbasnejad is an Associate Professor at Monash University's Department of Data Science and Artificial Intelligence, and holds adjunct positions at the Australian Institute for Machine Learning (AIML, University of Adelaide) and the Centre for Augmented Reasoning (CAR). He specializes in foundational AI, focusing on vision-language tasks, adversarial machine learning, and reinforcement learning. His work bridges theory with real-world applications in agriculture, energy, healthcare, and sports. Education: PhD in Computer Science from Australian National University (ANU). Research Interests: Machine Learning Theory and Adversarial Defenses Neural Network Robustness and Generalization Multimodal Learning (Vision-Language) Continual and Transfer Learning Applications in Energy, Healthcare, and Robotics Awards: Finalist for Australian AI Academic/Researcher of the Year (2024) Multidisciplinary competition wins (e.g., OzMineral Explorer Challenge) Advising & Grants: Australian Research Council (ARC) Discovery Project on Reinforcement Learning CSIRO's Next Generation Graduate Fund Accepting PhD students in foundational AI and applications Labs & Teams: Director of Foundational Machine Learning & Reasoning at Monash, leading global teams in AI competitions and industry collaborations (Microsoft Research, NEC Labs America).

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۳ مورد دیگر

Stephen Roberts is a Professor at the Australian National University (ANU) in the College of Science, Department of Mathematics. He is the lead developer of the ANUGA open-source hydrodynamic modeling software, which simulates dam breaks, floods, and tsunamis for governments and engineers. Roberts has made significant contributions to computational mathematics, particularly in numerical methods for partial differential equations, sparse grid data fitting, and finite element approximations scaling to millions of data points. MSc, Flinders University (1980) PhD, University of California, Berkeley (1985) His research interests include: Computational methods for shallow water wave equations Development of Python-based scientific computing frameworks Global sensitivity analysis and uncertainty quantification Adaptive mesh algorithms for fluid dynamics Recent publications focus on energy-stable numerical schemes, multiscale flood simulation, and convergence analysis in sensitivity methods. Roberts actively collaborates with environmental agencies and has led major computational science education programs at ANU. Stephen serves as Treasurer of the Computational Mathematics Group (ANZIAM) and leads projects in: Parallelization of hydrodynamic models CO2 leak detection via atmospheric measurements Optimization of sparse grid combinations His work combines theoretical advancements with real-world applications in disaster risk reduction and climate policy.

• Computational Mathematics
• Numerical Analysis
• Tsunami Modelling
• +۵ مورد دیگر

Quoc Thong Le Gia is an Associate Professor in the School of Mathematics & Statistics at the University of New South Wales (UNSW), Sydney. He holds a PhD in Mathematics from Texas A&M University (2003), an MS in Mathematics from Texas A&M University (2000), and a BSc in Mathematics and Computer Science from UNSW (1998). His research focuses on Numerical Analysis , Approximation Theory , Partial Differential Equations , and Stochastic Processes , with particular expertise in problems on spherical domains. His work bridges theoretical mathematics with practical applications in computational science, data science, and machine learning. Le Gia's recent publications demonstrate a strong focus on numerical methods for PDEs on spheres, stochastic analysis, and machine learning applications. His work shows consistent progression from theoretical foundations to practical implementations, with increasing interdisciplinary applications in recent years. L. F. Guseman Prize in Mathematics, Texas A&M University (2003) As a dedicated academic mentor, Le Gia has supervised numerous PhD, Master's, and Honours students across computational mathematics and data science topics. He has secured significant research funding through ARC Discovery Projects including DP220101811 (2022-2024) and DP180100506 (2018-2020). Professionally, he serves as External Associate Editor for Frontiers in Applied Mathematics and Statistics , Secretary for ANZIAM's Computational Mathematics Group, and Co-chair of Mathematics of Computation and Optimisation (AustMS Special Interest Group).

• Numerical Analysis
• Approximation Theory
• Partial Differential Equations
• +۵ مورد دیگر

Professor Anthony Richardson is a mathematical ecologist at the University of Queensland , holding a joint position with CSIRO Environment since 2005. He leads the Mathematical Marine Ecology Lab , focusing on marine conservation through mathematical, statistical, and computational modeling. School of the Environment, Faculty of Science, University of Queensland Affiliate, Centre for Marine Science Affiliate, Centre for Biodiversity and Conservation Science Research Interests span marine spatial planning (climate-smart protected areas, 3D ocean zoning), marine ecosystem modeling (lower trophic levels, plankton-dynamics), and climate change impacts (carbon sequestration, fisheries productivity, and marine diseases). His work has been cited in IPCC Assessment Reports and emphasizes international collaboration through projects like the Global Working Group on Climate Change Synthesis . Recent Publications focus on Zooplankton's role in carbon cycling Climate-smart marine protected area design Ecosystem modeling under climate uncertainty Marine megafauna connectivity patterns Supervision is offered for >250 peer-reviewed papers and advanced projects in climate-smart conservation planning, with established funding from ARC Discovery Projects and Norwegian Polar Institute .

• Marine Spatial Planning
• Ecosystem Modeling
• Climate Change Impacts on Marine Systems
• +۶ مورد دیگر

Dr. Divya Jayakumar Nair serves as Senior Lecturer in the School of Civil and Environmental Engineering at the University of New South Wales (UNSW), where she also holds the position of Associate Dean International – South Asia for UNSW Engineering. Her academic work is centered at the Research Centre for Integrated Transport Innovation (rCITI), focusing on transportation systems, network optimization, and disaster management applications. Dr. Nair's research interests span transportation engineering, disaster management, and urban mobility systems. Her work investigates complex network design problems, particularly in pre-disaster evacuation planning, transportation resilience, and food rescue logistics. She employs advanced analytical methods including traffic equilibrium modeling, network optimization techniques, and data-driven approaches to solve real-world transportation challenges with significant societal impact. Her recent publications demonstrate a strong focus on transportation resilience during disasters, with particular attention to equity considerations in pre-event planning. Dr. Nair's research shows how transportation networks can be optimized to improve emergency response while ensuring equitable access to safety for all population segments. Her work bridges theoretical network optimization with practical applications in urban environments facing climate-related challenges. Dr. Nair collaborates extensively with leading researchers in transportation including S. Travis Waller, Vinay V. Dixit, David Rey, and Taha Rashidi. Her collaborative approach spans multiple institutions and addresses pressing challenges in urban transportation systems worldwide.

• Transportation Engineering
• Disaster Management
• Network Design and Optimization
• +۴ مورد دیگر

Professor Josef Dick serves as a Professor and Deputy Head in the School of Mathematics & Statistics at the University of New South Wales (UNSW). With a distinguished career in computational mathematics, he has established himself as a leading researcher in numerical integration methods and quasi-Monte Carlo theory. His work bridges theoretical mathematics with practical computational applications across various scientific domains. Dr. Dick earned his PhD in Mathematics from UNSW in 2004 and his MSc in Mathematics from the University of Salzburg in 2001. His academic journey reflects a strong foundation in both theoretical and applied mathematics, which has informed his subsequent research contributions. Professor Dick's research primarily focuses on numerical integration and quasi-Monte Carlo rules , employing techniques from number theory , abstract algebra (particularly finite fields), discrepancy theory , wavelet theory , and statistics . His work provides rigorous analysis of practical algorithms for computational problems, with implementations often provided in Matlab to bridge theory and application. His research has successfully addressed point distributions on the unit cube for numerical integration, completely uniformly distributed sequences for Markov chain quasi-Monte Carlo algorithms, and explicit constructions of uniformly distributed points on the sphere. Analysis of his recent publications (2022-2025) reveals a consistent focus on advancing quasi-Monte Carlo methods, with increasing integration of machine learning techniques and applications to complex computational problems. His work demonstrates strong interdisciplinary connections between pure mathematics, computational science, and practical engineering applications, particularly in uncertainty quantification and high-dimensional numerical integration. Discovery project from Australian Research Council (2012-2014): "Mathematics in the round - the challenge of computational analysis on spheres" Queen Elizabeth II Fellowship from Australian Research Council (2010-2014): "Algebraic methods for Markov Chain Monte Carlo and quasi-Monte Carlo" UNSW Vice Chancellor Fellowship (2006-2009) Professor Dick has supervised numerous PhD and Honours students working on topics including Quasi-Monte Carlo methods, Discrepancy Theory, Markov chain Monte Carlo, and Uncertainty Quantification. His research has been supported by significant grants from the Australian Research Council, including serving as Chief Investigator on multiple projects. Beyond his research, he serves as an Editor for the Journal of Complexity and Journal of Approximation Theory, demonstrating his leadership in the mathematical community. He teaches courses in Algebra and Mathematical Computing for Finance at UNSW.

• Numerical Integration
• Quasi-Monte Carlo Rules
• Number Theory
• +۵ مورد دیگر

Associate Professor Dan Dongseong Kim is Deputy Director of UQ Cybersecurity and an Associate Professor at The University of Queensland (UQ), Australia. Previously, he held permanent academic positions at The University of Canterbury (UC), New Zealand (2011-2018) as a Senior Lecturer and Lecturer. His research focuses on Cybersecurity and Dependability for AI, IoT, Autonomous Vehicles, Cloud Computing, and Moving Target Defenses (MTD). Doctor of Philosophy in Computer Engineering from Korea Aerospace University Postdoctoral Research at Duke University (2008-2011) Visiting Scholar at University of Maryland (2007) Dan's work explores Graphical Security Models , Moving Target Defense for proactive resilience, and AI-Driven Cybersecurity with emphasis on adversarial robustness and interpretable models. His recent publications (2024-2025) span journals like IEEE Transactions on Dependable and Secure Computing and conferences such as DSN , addressing automated defense, evolving attacks, and hardware-aware security frameworks. He has advised 15 Ph.D. graduates, including researchers now at institutions like RMIT University, La Trobe University, and CSIRO's Data61. Current supervision includes projects on Automated Penetration Testing , AI-Based Intrusion Response , and Moving Target Defense . Dan's research is funded by agencies including the Republic of Korea's Agency for Defence Development and US Army Research Lab . His professional roles include Associate Editor for IEEE Communications Surveys and Tutorials and Steering Committee Chair for IEEE PRDC .

• Cybersecurity
• Cybersecurity Modeling and Analysis
• Moving Target Defense
• +۷ مورد دیگر

Dr. Thanh-Son Pham is an ARC DECRA Research Fellow in the Geophysics Department at The Australian National University’s Research School of Earth Sciences. His research focuses on using seismic waves to study Earth’s interior structures, from polar ice sheets to the inner core. He has pioneered methods like teleseismic P-wave coda autocorrelation and coda correlation wavefield analysis, leading to breakthroughs such as detecting J-waves in the inner core and identifying an innermost inner core layer. His work has been featured in Science , Nature Communications , and international media. He holds a PhD from ANU (2019) and has supervised research projects on Antarctic seismology and earthquake source physics. Awards include the 2024 Zatman lectureship from SEDI. Current projects include probing Antarctic ice sheets via correlation seismology and advancing machine learning tools for deep Earth studies. Education: PhD in Geophysics (ANU, 2019), Graduate Diploma in Earth System Physics (ICTP, 2015), BSc in Applied Mathematics (Hanoi University, 2013) Research interests span seismic source inversion, Antarctic ice dynamics, and inner core anisotropy. His 2024 articles address Hunga Tonga eruption mechanics and PKIKP wave analysis using deep learning. Media highlights include BBC, NYT, and ANU press releases.

• Seismology
• Geophysics
• Antarctic Ice Sheet Dynamics
• +۳ مورد دیگر

Andrew Zammit Mangion is an Associate Professor at the University of Wollongong , affiliated with the School of Mathematics and Applied Statistics . His research focuses on spatio-temporal statistics, computational methods, and environmental informatics, with applications in climate science and geospatial data analysis. Education : PhD in Statistics (University of Sheffield, 2012), B.Eng. (University of Malta, 2007) Research Themes : Spatio-temporal modeling, Bayesian inversion frameworks (e.g., WOMBAT v2.S), deep learning integration, and statistical software development (e.g., FRK package) Grants & Projects : ARC DECRA Fellow (2018), Chief Investigator on ARC Discovery Project (greenhouse gases), ARC Special Research Initiative (Securing Antarctica's Environmental Future), and ARC Industrial Transformation Hub (TIDE). Collaborations : University of Bristol, University of Edinburgh, ESA CCI, NASA OCO-2 data projects Scientific Awards include the prestigious Australian Research Council Discovery Early Career Researcher Award (DECRA). His work spans Antarctic ice sheet analysis, CO2 flux inversion, and scalable spatial statistical models for environmental monitoring.

• Spatio-temporal Statistics
• Computational Statistics
• Environmental Informatics
• +۳ مورد دیگر

Michael Bode is a Professor in the School of Mathematical Sciences at Queensland University of Technology (QUT). His research focuses on applying mathematical and computational methods to ecological and conservation challenges, particularly in marine ecosystems and coral reef management. He holds a PhD in Applied Mathematics from the University of Queensland. Research Interests: Professor Bode specializes in larval dispersal modeling, conservation prioritization, and the application of ecosystem models to real-world management scenarios. His work integrates disciplines like applied mathematics, statistics, and ecology to address issues such as coral reef resilience, invasive species control, and climate change impacts. Grants & Projects: Securing Antarctica's Environmental Future (2020) Conserving Coral Reef Fish and Sustaining Fisheries in the Anthropocene (2019) Tackling Pests Using Game Theory (2019) New Methods for Conserving Dispersing Species on Coral Reefs (2017) Awards: 2020 Fenner Medal (Australian Academy of Science) Eureka Prize finalist (2020) Australian Research Council Future Fellowship (2017) Labs & Teams: He leads the QUT Applied Mathematical Ecology Group (QUTAMEG), which develops quantitative tools for ecological and conservation problems. His work is published in journals like Conservation Biology , Nature Sustainability , and PLoS Biology .

• Applied Mathematics
• Ecological Applications
• Conservation Biology
• +۶ مورد دیگر

Dr. Ehsan Pashajavid is a Senior Lecturer at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences, within the Faculty of Science and Engineering. His research focuses on stochastic optimization, renewable energy integration, microgrid control, and electric vehicle systems. Research interests include: Microgrid and smart grid control algorithms Renewable energy resource management Power system stability and operation Energy storage optimization Electric vehicle-grid integration His publications demonstrate significant contributions to power system resilience, with recent work emphasizing battery storage economics, fault-tolerant converters, and model predictive control for grid stability. Article trends show strong focus on renewable integration challenges and optimization techniques for modern energy systems. Awards include Senior Member status in IEEE and its Power & Energy, Industrial Applications, and Power Electronics societies. Teaching responsibilities encompass graduate courses in Renewable Power Generation Systems, Smart Grid Control, and Renewable Energy Principles.

• Stochastic modeling/optimisation
• Microgrids/Smartgrid
• Renewable Energy Resources
• +۳ مورد دیگر

David Lo is the OUB Chair Professor of Computer Science at Singapore Management University's School of Computing and Information Systems, where he directs the Information Systems and Technology Cluster and the Center for Research on Intelligent Software Engineering. An ACM Fellow, IEEE Fellow, and ASE Fellow, his research focuses on AI for Software Engineering (AI4SE), leveraging machine learning, data mining, and NLP to enhance software analytics and automation. Research Highlights: AI4SE, code LLMs, human-AI synergy in software engineering, software reliability, and empirical studies of practitioner pain points Awards: IEEE TCSE Distinguished Service Award, university-wide Teaching Excellence Award, Outstanding Graduate Supervisor Award, 2 Test-of-Time Awards, and 11 ACM SIGSOFT/IEEE TCSE Distinguished Paper Awards Leadership: General Chair of ASE'16 and MSR'22, PC Co-Chair for ASE'20, FSE'24, and ICSE'25, ACM SIGSOFT Executive Committee member His work has received over 20 awards, 37,000 citations, and an H-index of 100. As an educator, he has mentored trainees who became faculty and R&D experts globally.

• AI for Software Engineering (AI4SE)
• Machine Learning
• Data Mining
• +۵ مورد دیگر