جستجوی استاد راهنمای دانشگاهی

Scientia Professor Gary Froyland is a Professor at the University of New South Wales (UNSW), affiliated with the School of Mathematics & Statistics. He leads the ARC Laureate Centre for Dynamical Systems and Data and holds an Einstein Visiting Fellowship from the Einstein Foundation Berlin. His academic credentials include a BSc (Hons 1, Medal) in Pure and Applied Mathematics from the University of Queensland and a PhD in Mathematics from the University of Western Australia. Professor Froyland's research spans two primary domains: dynamical systems and optimization. In dynamical systems, he investigates the interplay of probability and geometry in nonlinear and chaotic systems, employing tools from ergodic theory, functional analysis, and differential geometry. His work extends to applications in oceanography, atmospheric science, and granular flows. In optimization, he focuses on decision-making in complex systems with uncertain information, developing novel approaches in mathematical programming that have been applied to mining, logistics, and medical treatment planning. His recent publications demonstrate a strong focus on coherent structures in dynamical systems, linear response theory, and applications to geophysical phenomena. The research shows increasing interdisciplinary collaboration, particularly with climate scientists and data analysts, reflecting a trend toward applying advanced mathematical techniques to real-world problems in environmental science and engineering. J.D. Crawford Prize (2025) Elected Member of the Academy of Europe / Academia Europaea (2024) ARC Laureate Fellow (2024-2029) Fellow of the Society for Industrial and Applied Mathematics (SIAM) (2021) Fellow of the Australian Academy of Science (2020) Vice-Chancellor's Award for Teaching Excellence - Postgraduate Research Supervision (2015) Professor Froyland actively supervises PhD and honors students, with current advisees including Kevin Felipe Kühl Oliveira, Nicholas Peters, and Kathrin Völkner. His research is supported by multiple grants, including an ARC Laureate Fellowship (2024-2029) for "Breakthrough mathematics for dynamical systems and data," an Einstein Visiting Fellowship (2022-2026), and several ARC Discovery Projects. His work has practical applications in climate science, mining optimization, and medical treatment planning, particularly in radiotherapy. He leads the ARC Laureate Centre for Dynamical Systems and Data, which brings together researchers to develop new mathematical approaches for analyzing complex dynamical systems. The center focuses on creating methods to identify coherent structures in spatiotemporal data, with applications spanning environmental science, social science, health science, and engineering.

• Dynamical Systems
• Machine Learning
• Optimisation
• +۱۲ 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
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Professor Gregor Verbic is a faculty member at the University of Sydney in the School of Electrical and Computer Engineering , where he serves as Director of the Centre for Future Energy Networks . Previously, he held an assistant professor position at the University of Ljubljana and was a NATO-NSERC Postdoctoral Fellow at the University of Waterloo. His career spans academic research, industry leadership as Head of Interenergo's Investment Department, and extensive collaboration with IEEE. PhD in Electrical Engineering (University of Ljubljana) Senior IEEE Member Research Interests focus on transforming power systems to zero-carbon grids through: Aggregation and control of distributed energy resources (DERs) Frequency control with wind generation and electric vehicles Stochastic optimization for multi-energy systems Smart home energy management with phase change materials Notable Contributions include: 2006 IEEE prize paper for voltage instability prediction 2010-2024: 15 recent publications on DER coordination, network tariffs, and low-inertia grid stability Teaching includes courses on: ELEC3203/ELEC9203: Electricity Networks ELEC5213: Engineering Optimisation ELEC5206: Sustainable Energy Systems Labs & Initiatives Centre for Future Energy Networks The Net Zero Institute

• Voltage instability prediction
• Electricity markets
• Stochastic optimization
• +۵ 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

Professor Tony Jan leads the Centre for Artificial Intelligence Research and Optimisation (AIRO) at Torrens University Australia's Design and Creative Technology school. He holds a PhD in Computing Science from the University of Technology Sydney (2004) and a Bachelor of Engineering from the University of Western Australia (1999). His research focuses on federated machine learning for IoT security, ensembled machine learning for real-time applications, cognitive machines for human-centric computing, and smart sensor networks for healthcare and security. He has secured ARC grants and industry partnerships with NVIDIA, IBM, and Microsoft. Awards include the 2024 SEI Global Academic Excellence Award and the 2023 Torrens University Excellence Award. Research collaborations span global partners, with contributions to UN Sustainable Development Goals in education and industry. His work bridges academia and industry, expanding AI program enrollments by 2,000+ students and enhancing student satisfaction by 15%. He advises PhD students on topics like IIoT cybersecurity and smart cities, and has produced over 97 publications since 1999. Education: PhD (UTS, 2004), BEng (UWA, 1999) Research Themes: AI for Industry 5.0, Cybersecurity, Smart Cities, Healthcare Technology Key Partnerships: NVIDIA, CIMIC, Palo Alto Networks Recent Projects: Federated learning for health IoT, drone vision intelligence, ransomware detection His work emphasizes ethical AI adoption in design and healthcare, with publications exploring AI ethics, generative AI applications, and sustainable technology integration.

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +۷ مورد دیگر

Hanyu Gu is a Senior Lecturer in the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS), part of the Faculty of Science. He holds a PhD in Power Engineering and Automation from Shanghai Jiao Tong University (1999) and has extensive industry experience in telecommunications, airline optimization, and mining. His research focuses on combinatorial optimization, decomposition methods, stochastic programming, and machine learning applications. Notable awards include second place in the 2020 ROADEF competition. He collaborates with institutions like the UTS Transportation Research Centre and has contributed to projects such as optimisation engines for airline management and underground mining algorithms. Current research explores hybrid algorithms, Bayesian optimisation, and scheduling under uncertainty. Education: Bachelor in Industrial Automation, Shanghai Jiao Tong University (1994) Master in Control Theory and Application, Shanghai Jiao Tong University (1997) PhD in Power Engineering and Automation, Shanghai Jiao Tong University (1999) Industry Experience: ZTE (1999–2001): Senior Wireless Communication Engineer CTI, Melbourne (2007–2011): Airline Management Optimisation Researcher NICTA (2011–2013): Underground Mining Optimisation Researcher Grants: ARC Linkage Project LP0883855 (2008–2012): Developed optimisation tools for transportation crewing, valued at $840,000. Research interests span decomposition methods for large-scale problems (e.g., airline scheduling), stochastic programming for resource sharing, and hybridisation of mathematical programming with constraint programming. Recent work includes Bayesian optimisation for knapsack problems and relax-and-solve algorithms for project scheduling. His articles frequently address optimisation in logistics, healthcare, and transportation, emphasizing practical industry applications and algorithmic innovation. Awards: Second place in the ROADEF 2020 competition for maintenance planning solutions. Advising & Grants: Supervises Masters and PhD students in operations research and optimisation. Collaborates with Ausgrid, UGL, and ANC on optimisation projects (e.g., employee training timetabling, logistics). Active in the Optimisation Group of UTS Transportation Research Centre, he bridges academic research with real-world challenges in scheduling, logistics, and resource management. Ongoing efforts include advancing metaheuristics and integrating machine learning with traditional optimisation techniques.

• Combinatorial Optimization
• Operations Research
• Stochastic Programming
• +۵ مورد دیگر

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +۷ مورد دیگر

Dr. Huy Truong-Ba is a Lecturer at Queensland University of Technology (QUT), affiliated with the School of Mechanical, Medical and Process Engineering and the Centre for Data Science. He holds a Doctor of Philosophy from QUT. His research focuses on reliability and degradation modeling, maintenance optimization, and engineering asset management, with expertise in Markov decision processes and condition-based maintenance strategies. His academic journey includes roles as an Associate Supervisor for multiple PhD projects, such as 'Dispatch Optimisation for Concentrating Solar Tower Power Plant Under Uncertainty' and others in solar energy systems and mechanical engineering. He has collaborated with institutions like the Asset Institute and contributed to interdisciplinary projects in energy systems and transportation. Research interests span mechanical engineering, manufacturing engineering, and engineering practice, with a strong emphasis on applying data-driven methods to optimize maintenance and asset management. His work often integrates statistical modeling and stochastic optimization to address challenges in energy infrastructure, solar power, and industrial equipment degradation. Key collaborations involve Associate Professor Michael Cholette and Professor Tommy Chan, focusing on solar thermal systems, rail infrastructure maintenance, and material degradation under high-temperature conditions. Dr. Truong-Ba's contributions bridge theoretical models with practical applications, driving advancements in sustainable energy and infrastructure resilience.

• Reliability and Degradation Modelling
• Maintenance Optimization
• Engineering Asset Management
• +۵ مورد دیگر

Lindon Roberts is a Lecturer in the School of Mathematics and Statistics at the University of Sydney . He holds a DPhil (Doctor of Philosophy) from the University of Oxford and previously served as an MSI Fellow at the Australian National University. His research focuses on numerical optimization, including nonconvex, derivative-free, and stochastic optimization, with applications in machine learning. He currently teaches courses such as MATH2070 (Optimization and Financial Mathematics) and FMAT3888 (Projects in Financial Mathematics). Education & Professional Background: PhD (DPhil) in Mathematics, University of Oxford MSI Fellow, Australian National University Research Interests: Numerical optimization techniques Derivative-free and stochastic optimization methods Algorithm design for machine learning applications Nonconvex optimization challenges His work bridges theoretical optimization and practical implementation, particularly in imaging and data-driven fields. Funding & Grants: 2024: 'Robust Derivative-Free Algorithms for Complex Optimisation Problems' (Australian Research Council DECRA) 2022: 'Theory and Algorithms for Numerical Optimisation' (University of Sydney Faculty Startup) Key Research Trends in Publications: Recent articles emphasize optimization algorithms for imaging (e.g., X-ray and neutron beam techniques) and bilevel learning frameworks. His work on inexact hypergradients and scalable subspace methods highlights advancements in handling complex optimization landscapes.

• Numerical Optimization
• Derivative-Free Optimization
• Stochastic Optimization
• +۳ مورد دیگر

Sobhan (Sean) Arisian is Associate Professor of Supply Chain and Logistics at La Trobe Business School. He leads the Sustainable Operations Management discipline and serves as Associate Investigator at the ARC Training Centre in Optimisation Technologies (OPTIMA). Research focuses on supply chain resilience, digitalization, and sustainability—particularly decarbonization of maritime transport and disaster relief logistics. Recent publications demonstrate strong emphasis on quantitative optimization models, with recurring themes in: Robust optimization for disruption management Sustainable logistics decarbonization Humanitarian supply chain coordination Quantum computing applications in operations Awarded the Inaugural Collaborative Research Award by BAM/ANZAM and Outstanding Reviewer recognition. Secured $382,742 from Australian Government for Quantum Enhanced Optimisation project and $234,400 DFAT National Focused Grant. Collaborates with University of Cambridge scholars and industry partners on digital transformation strategies. Editorial roles include Associate Editor for Transportation Research Part E and IEEE Transactions on Engineering Management.

• Supply Chain Resilience
• Digital Transformation
• Sustainable Operations
• +۳ مورد دیگر

Dr. Jiaming Ma is a post-doctoral researcher at RMIT University's Centre for Innovative Structures and Materials (CISM), working on the ARC Laureate Fellowship project. His research focuses on material and structural innovations for sustainable civil engineering, including rammed earth, metamaterials, and topology optimization. He holds a Researcher academic rank and is affiliated with the School of Engineering's Department of School of Engineering. Dr. Ma has received notable awards such as the 2022 Hangai Prize and Best Paper Award. He has led roles in organizing academic symposia and professional associations, including the Chinese Association of Professionals and Scholars in Australia. His work emphasizes sustainable construction and digital fabrication methods. Research Interests: Rammed earth, Sustainable building materials, Topology optimisation, Digital Construction, Computational morphogenesis, Shell structures. These areas address environmental sustainability and advanced structural solutions. Key achievements include pioneering bio-binder stabilization for rammed earth and developing deployable tubular structures. His articles span structural optimization, metamaterials, and sustainable design, reflecting interdisciplinary contributions to civil engineering and materials science. Awards include the Hangai Prize for shell structure innovation, DNA Paris Design Award, and leadership in academic conferences. He supervises research projects on metamaterials and rammed earth construction. Professional contributions include roles as President of the Chinese Association of Professionals in Australia and organizing the IASS Annual Symposium 2023. His work bridges academic research with industry applications, particularly in eco-friendly construction practices.

• Rammed earth
• Sustainable building materials
• Topology optimisation
• +۳ مورد دیگر

Dr Adrianne Jenner is a Senior Lecturer in the School of Mathematical Sciences at Queensland University of Technology (QUT). She holds a PhD in Applied Mathematics from the University of Sydney (2015-2019), and conducted postdoctoral research at the Université de Montréal (2019-2021) and the University of Sydney. Her research focuses on mathematical biology, particularly oncology, virotherapy, immunology, and agent-based modelling. She is an active member of professional societies including the Society for Mathematical Biology and the Australian Mathematical Society. Her teaching spans abstract mathematical reasoning, calculus, and mathematical biology applications. Jenner's research emphasizes computational approaches to understand disease mechanisms, including cancer drug resistance, tumor microenvironment dynamics, and in silico clinical trials. She has contributed to high-impact studies on topics like glioblastoma immunotherapy, coral bleaching, and multiple sclerosis pathogenesis. Jenner actively engages in public outreach, including media appearances on ABC and science festivals. Her work bridges theoretical models with real-world applications, leveraging agent-based simulations and Bayesian methods to address challenges in healthcare and environmental science. Jenner collaborates extensively with interdisciplinary teams to translate mathematical insights into actionable strategies for disease treatment and ecological conservation.

• Mathematical Biology
• Oncology
• Virotherapy
• +۶ مورد دیگر

Ahmad Attarha is a Research Fellow at the School of Computing , College of Engineering and Computer Science , Australian National University (ANU). He recently submitted his PhD thesis and holds a B.Sc. and M.Sc. in Electrical Engineering from Semnan University, Iran (2012 and 2015). Education: B.Sc. (2012), M.Sc. (2015) in Electrical Engineering Current Role: Research Fellow in Power Systems and Smart Grids His research focuses on power systems operation and optimisation , with specific emphasis on Grid Integration of Distributed Energy Resources (DER) , Distributed Optimisation , and Robust Optimisation techniques for distribution networks. Publications highlight trends in DER aggregation , shaped operating envelopes , and robust optimization for distribution systems. Key areas include voltage regulation , network security constraints , and price-sensitive bidding strategies in energy markets. He is affiliated with the Smart Grid group at ANU and contributes to projects related to grid flexibility , distributed battery storage , and renewable energy integration .

• Power Systems Operation and Optimisation
• Power System Operation and Planning
• Grid Integration of DER
• +۲ مورد دیگر

Dr. Mahakim Newton is a Lecturer in Data Science at the University of Newcastle, Australia, and an Adjunct Senior Research Fellow at Griffith University’s Institute for Integrated and Intelligent Systems. He holds a PhD in Computer and Information Sciences from the University of Strathclyde, alongside degrees from Bangladesh University of Engineering and Technology (BUET). His research focuses on artificial intelligence, machine learning, bioinformatics, and computer education, with notable contributions to protein structure prediction and IoT interoperability. Education: Ph.D., University of Strathclyde M.Sc.Engg., Bangladesh University of Engineering and Technology B.Sc.Engg., Bangladesh University of Engineering and Technology Research Interests: Dr. Newton’s work bridges AI and computational biology, including protein-ligand binding affinity prediction, bioinformatics algorithms, and IoT edge networks. He explores machine learning techniques for environmental informatics and drug discovery, emphasizing scalable solutions for complex problems. Teaching: He coordinates courses like Computing Project , Algorithms , and Deterministic and Stochastic Optimisation at the University of Newcastle, integrating practical and theoretical insights. Grants and Labs: Involved in interdisciplinary projects on water quality prediction and edge computing interoperability. His collaborations span academic and industrial partners, advancing applications in healthcare and environmental monitoring.

• Artificial Intelligence
• Big Data Analytics
• Bioinformatics
• +۲۱ 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).

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