Find Academic Supervisors

Dr. Dmytro Matsypura is an Associate Professor in the Discipline of Business Analytics at the University of Sydney Business School. He holds a BA (Hons) from Kyiv Polytechnic Institute (KPI), an MS (Hons) from KPI, and a PhD from the University of Massachusetts Amherst. His research focuses on optimization methodologies, network science, and their applications in finance, transportation, ecology, and graph theory. He is a recipient of multiple teaching awards, including the Wayne Lonergan Outstanding Teaching Award (Early Career) in 2010. Education: PhD in Management Science, University of Massachusetts Amherst (2006) MS (Hons) in Information Systems, Kyiv Polytechnic Institute (2000) BA (Hons) in Business Administration, Kyiv Polytechnic Institute (1998) Research Interests: Dr. Matsypura’s work spans operations research and management science, with a focus on mathematical optimization and network science. His methodological contributions include developing efficient optimization algorithms, while his applied research addresses real-world challenges in finance, engineering, and ecology. Notable applications include wildfire fuel management, portfolio margining, and credit card fraud detection via graph-based models. Awards and Recognition: Teaching Excellence Award (2008, 2013, 2018) Wayne Lonergan Outstanding Teaching Award (Early Career) (2010) Grants and Projects: Current projects include Bushfire Analytics: Optimization of Fuel Reduction (2023, ARC Discovery Project). His research frequently integrates interdisciplinary collaborations, such as applying graph theory to biomedical problems and cybersecurity. Labs/Teams: Active in the Sydney Environment Institute, contributing to projects at the intersection of analytics and sustainability. Collaborates with industry on fraud detection and supply chain optimization.

• Operations Research
• Management Science
• Optimization Algorithms
• +6 more

Professor Ben Goldys is a distinguished academic at The University of Sydney's School of Mathematics and Statistics, where he conducts research at the intersection of pure mathematics and applied sciences. His work spans multiple disciplines including stochastic analysis, partial differential equations, and financial mathematics, with significant contributions to both theoretical frameworks and practical applications in science and finance. Goldys' research interests center on stochastic (ordinary and partial) differential equations and their applications. His specific focus areas include stochastic partial differential equations, stochastic geometric PDEs, stochastic boundary value problems, stochastic fluid dynamics, ergodic theory of infinite-dimensional diffusions, and applications in financial mathematics such as interest rate derivatives, credit risk, and stochastic volatility. His work bridges pure mathematical theory (Functional Analysis, PDEs, Ergodic Theory) with complex real-world problems across multiple domains. His research aligns with the University of Sydney Faculty of Science Research Strengths including Understanding the Universe, Fundamental Laws of Nature, Complex Systems, and Next Generation Materials. Professor Goldys has secured multiple significant research grants from the Australian Research Council, including recent projects such as 'Mathematics for future magnetic devices' (2024), 'Mathematics for breaking limits of speed and density in magnetic memories' (2019), and 'Novel Approaches for Problems with Uncertainties' (2015). His current research projects focus on geometric stochastic partial differential equations and applications in micromagnetism, mean field games in finance, stochastic boundary value problems, and stochastic Navier-Stokes equations on the rotating sphere. He maintains extensive international collaborations with institutions in Germany (University of Tuebingen), Italy (LUISS University), Poland (Institute of Mathematics Polish Academy of Sciences), and the United Kingdom (University of York), working on projects involving optimal control, stochastic systems with memory, and geometric stochastic PDEs. Goldys is an active member of the Applied Mathematics Research Group and The University of Sydney Nano Institute, contributing to interdisciplinary research initiatives that connect mathematical theory with cutting-edge technological applications.

• Stochastic Differential Equations
• Financial Mathematics
• Partial Differential Equations
• +5 more

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
• +12 more

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
• +5 more

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
• +5 more

Professor Khac Duc Do is a faculty member at Curtin University, holding a position in the School of Civil and Mechanical Engineering within the Faculty of Science and Engineering. He serves in the Office of the Provost and is based at Curtin Perth campus. His research focuses on advanced control systems, nonlinear dynamics, and robotics applications in marine, aerospace, and mechanical systems. He earned a PhD with distinction in 2003 and has held prestigious fellowships including ARC Postdoctoral Fellow (2004) and ARC Australian Research Fellow (2009). His teaching includes courses like Advanced Control and Mechatronics, Navigation and Marine Control Systems, and Advanced Control Engineering. Key research interests encompass control of nonlinear systems, stochastic systems, formation control of mobile agents, fluid-structure interaction, and boundary control of PDE-governed systems. His funded projects include wave-energy converter development (2023-2026), inerter-based damper research (2019-2021), and ocean vehicle control systems. Scientific awards include ARC grants totaling over AUD 2 million. Current opportunities include scholarships in control systems/fluid-structure interaction and a postdoc position in wave-energy conversion.

• Control of Nonlinear Systems
• Autonomous Marine Vehicles
• Partial Differential Equation Systems
• +3 more

Andy McLennan is a Professor in the School of Economics at the University of Queensland since 2007, following roles at the University of Minnesota (1987–2005) and the University of Sydney. His research focuses on mathematical economics and game theory, with contributions to computational game theory, fixed point theory, and algebraic geometry. Notable collaborations include work with Richard McKelvey on the Gambit software package for game analysis. Education: B.A. in Mathematics from the University of Chicago (undergraduate), PhD in Economics from Princeton University (1982). Prior faculty positions included the University of Toronto and Cornell University. Research Interests: Explores intersections between pure mathematics and economics, including applications of topology (Vietoris-Begle theorem), differential geometry (Morse-Sard theorem), and computational complexity in markets. Recent work includes the 'Index +1 Principle' for equilibrium stability and fixed point index theory. Software & Tools: Co-developed Gambit , a widely used open-source toolkit for analyzing finite games. Authored technical software for solving systems of equations and 3D visualization tools for academic use. Books: Authored Advanced Fixed Point Theory for Economics (Springer, 2018), The Algebra of Coherent Algebraic Sheaves , and The Nature and Origins of Modern Mathematics . Personal: Lives in Brisbane with his partner Shino Takayama (also an economist) and their son Sean. Enjoys Japanese language, classical music, and strategic games like Go and chess.

• Mathematical Economics
• Game Theory
• Algebraic Geometry
• +3 more

Associate Professor Jiakun Liu (FAustMS) is affiliated with the School of Mathematics and Statistics, University of Sydney . He holds a BSc from Zhejiang University (2006) and a PhD from the Australian National University (2010). Following a Simons Postdoctoral Fellowship at Princeton (2010-2013), he served as Lecturer, Senior Lecturer, and Associate Professor at the University of Wollongong (2013-2024), securing an ARC DECRA in 2014 and an ARC Future Fellowship in 2024. Specializes in nonlinear elliptic/parabolic PDEs with applications in geometry and optimal transportation Research focuses on Monge-Ampère/Hessian equations , regularity theory, and geometric flows Contributions to convex geometry , minimal surfaces, and stochastic PDEs . His 2024-2023 publications in Communications on Pure and Applied Mathematics , Advanced Nonlinear Studies , and Archive for Rational Mechanics demonstrate expertise in free boundary regularity , noncompact Minkowski problems , and global geometric analysis . Recognized with ARC Future Fellowship and conferences organized across Australia-China collaborations.

• Nonlinear Elliptic and Parabolic Partial Differential Equ.ations
• Regularity Theory of Monge-Ampère Equations
• Hessian Equations
• +6 more

Xiaoping Lu is an Associate Professor at the School of Mathematics and Applied Statistics, University of Wollongong, Australia. She has served as Academic Program Director for the Bachelor of Mathematics (Advanced) program since 2008 and holds an ORCID identifier (0000-0003-1090-8437). Her research focuses on applied mathematics and financial mathematics, particularly in option pricing, stochastic volatility models, and computational finance. Research Themes: Transaction cost modeling, regime-switching financial markets, numerical methods for PDEs, utility-indifference valuation, and stochastic optimization algorithms. Awards: 2024 AustMS-WIMSIG Anne Penfold Street Award 2024 Cheryl E. Praeger Travel Award Leadership: President of the Asia Pacific Consortium of Mathematics for Industry (APCMfI) since 2024; leadership roles in ANZIAM and WIMSIG committees. Teaching: Coordinated courses like MATH142, MATH141, and MATH283; currently available for PhD supervision in topics including financial derivatives and stochastic liquidity risk. Funding: Contributed to grants like 'The AI Tutor' (2024) and industry partnerships for advanced mathematics education.

• Applied Mathematics
• Financial Mathematics
• Numerical and Computational Mathematics
• +3 more

Fima Klebaner is Professor in the School of Mathematics at Monash University and Director of the Centre for Modelling of Stochastic Systems. His research spans stochastic processes, financial mathematics, and population biology, with emphasis on limit theorems, branching processes, and diffusion models. Current projects include ARC-funded work on stochastic population dynamics and financial derivatives pricing. Key research areas: 1) Population-dependent stochastic systems; 2) Large deviation principles; 3) Financial mathematics (Dupire formula, volatility); 4) Approximation methods for complex processes. Recent publications (2018-2025) show balanced focus on theoretical probability (45%) and applied modeling (55%), particularly in ecology and finance. Article analysis reveals advanced methodologies in: 1) Stochastic calculus applications (33% of recent works); 2) Limit theorems for interacting systems (27%); 3) Financial mathematics innovations (20%). Theoretical contributions frequently interface with biological and financial applications.

• Stochastic Processes
• Financial Mathematics
• Population Models
• +1 more

Associate Professor Ivan Guo is a faculty member at Monash University's School of Mathematics, where he leads research in mathematical finance and stochastic modeling. He obtained his PhD in Mathematics from the University of Sydney in 2014 and currently accepts PhD students. His work bridges theoretical mathematics and practical financial applications, with active projects spanning 2022-2026. Research Focus Dr. Guo's research centers on three interconnected areas: Optimal Transport Applications : Developing transport-based methods for financial model calibration and derivatives pricing Market Microstructure : Analyzing market-making strategies, liquidity, and high-frequency trading dynamics Sustainable Finance : Modeling green investment impacts and energy market transitions using game-theoretic approaches Active Projects Can green investors drive transition to a low-emission economy? (2022-2026) Integrating energy storage into electricity markets (2022-2024) Data61 CRP #46 - Risklab mathematical sciences (2020-2023) Efficient computational techniques for econophysics (2019-2021) The role of liquidity in financial markets (2017-2020) His research consistently addresses model uncertainty, volatility dynamics, and computational methods across 18+ publications since 2012.

• Mathematical Finance
• Optimal Transport
• Stochastic Control
• +3 more

Professor Matthew Simpson is a leading figure in applied mathematics at the School of Mathematical Sciences, Faculty of Science, Queensland University of Technology (QUT). He holds the position of Professor of Applied Mathematics and is an Australian Research Council (ARC) Future Fellow, reflecting his sustained research excellence. His work bridges mathematical theory and biological applications, particularly in cell migration, tissue invasion, and multiscale modeling. BE (Environmental) Honours 1, University of Newcastle (1995–1998) PhD (with Distinction), Environmental Engineering, University of Western Australia (2000–2003) Research Fellow, Department of Mathematics and Statistics, University of Melbourne (2003–2006) ARC Postdoctoral Fellow, University of Melbourne (2006–2009) Lecturer (2010–2011) and Senior Lecturer (2011–2013), QUT Associate Professor (2013–2014), QUT Professor and ARC Future Fellow (2014–present), QUT Matthew Simpson’s research focuses on mathematical and computational modeling of biological systems , particularly collective cell motion, diffusion processes, and reaction-diffusion dynamics. His interests span multiscale modeling , random walk processes , cell biology , and numerical and computational mathematics . He develops and analyzes models to understand phenomena such as wound healing, cancer progression, and tissue engineering. His recent publications (2023–2025) demonstrate a strong trend toward integrating data-driven modeling , likelihood-based inference , and equation learning with traditional mechanistic models. These works emphasize parameter identifiability , uncertainty quantification , and prediction robustness in biological contexts. Themes include sharp-fronted wave propagation, mechanical cell interactions, tumor spheroid formation, and generalized diffusivity in food drying, showcasing the breadth and depth of his modeling expertise. Among his key accolades are: J.H. Michell Medal (2012) – Awarded by ANZIAM for distinguished research by an early-career applied mathematician in Australia and New Zealand. ARC Future Fellowship (2013–2017) – For the project 'New data-driven mathematical models of collective cell motion' (FT130100148). Professor Simpson has also played significant editorial and leadership roles, including: Executive Associate Editor, Journal of Engineering Mathematics Academic Editor, PLoS ONE Editorial Board Member, ANZIAM Journal Co-chair of the 2015 ANZIAM meeting He has supervised PhD students on topics such as moving boundary problems, first-passage times, stochastic simulations, and curvature-dependent growth in biological systems. His research projects have been funded by competitive Australian grants (ARC DP and FT schemes), including studies on 3D cell migration, ghrelin’s role in cell invasion, and epithelial-to-mesenchymal transition in cancer and wound healing. He is actively involved in developing computational tools for biological modeling and promoting best practices in scientific publishing.

• Applied Mathematics
• Mathematical Modelling
• Computational Modelling
• +7 more

Dr. Leung Chan is a Lecturer in the School of Mathematics and Statistics at the Faculty of Science, University of New South Wales (UNSW). He is an active member of the School's Finance and Risk Analysis Research Group, which develops innovative methods for financial modelling, derivative pricing and risk analysis. Dr. Chan's research spans several interconnected areas within quantitative finance: Financial Mathematics Pricing and hedging of financial derivatives Stochastic implied volatility models Default risk modelling Modelling of credit migrations Valuation of credit derivatives Asset price dynamics Quantitative Risk His publication record demonstrates a strong focus on regime-switching models in financial mathematics. Over the past decade, Dr. Chan has published extensively on option pricing with regime switching, volatility modeling, and risk analysis. His work often combines sophisticated mathematical techniques with practical financial applications, particularly in derivative pricing and risk management. The consistent theme across his research is the application of advanced stochastic processes to model financial markets with changing regimes. Dr. Chan maintains active research collaborations with scholars including Song-Ping Zhu, Robert J. Elliott, and Tak Kuen Siu. His recent work continues to advance analytical methods for pricing complex financial derivatives under regime-switching frameworks. He is associated with the Quantitative Risk Solutions Lab at UNSW, which serves as a platform for applying theoretical research to practical financial risk management challenges.

• Financial Mathematics
• Pricing and hedging of financial derivatives
• Stochastic implied volatility models
• +7 more

Zhou Zhou is a Senior Lecturer in the School of Mathematics and Statistics at the University of Sydney. His academic roles include Senior Lecturer (2022–present) and Lecturer (2018–2021) at the University of Sydney, as well as postdoctoral positions at the University of Michigan and University of Minnesota. He holds a Ph.D. in Applied & Interdisciplinary Mathematics from the University of Michigan (2015) and a B.S. in Mathematics from Nankai University (2010). His research focuses on stochastic control, mathematical finance, and game theory, with particular emphasis on time-inconsistent problems, optimal stopping, and equilibrium strategies. Key areas include applications in financial mathematics, stochastic processes, and dynamic optimization. His work has been published in journals such as Mathematical Finance, SIAM Journal on Control and Optimization, and Finance and Stochastics. Zhou has secured grants including the 2023 Faculty of Science Startup Scheme for time-inconsistent control research and the 2022 Australian Research Council grant on green investment impacts. He teaches courses like Arbitrage Pricing in Continuous Time and supervises research students in financial mathematics. His academic contributions span over 50 publications, with notable work on binomial-tree approximations for stopping problems, equilibrium strategies in mean-field games, and policy iteration for stochastic control. Presentations include talks at international conferences and universities worldwide, emphasizing interdisciplinary applications of stochastic analysis.

• Stochastic Control
• Mathematical Finance
• Optimal Stopping Problems
• +3 more

Professor Dingxuan Zhou is a distinguished academic serving as Professor and Head of School of Mathematics and Statistics at The University of Sydney, joining the institution on August 29, 2022. He is also a member of The Net Zero Institute and has held significant editorial positions, including editor-in-chief of the journal "Analysis and Application" of "Mathematical Foundations of Computing" and serving on the editorial boards of over ten international journals. Educational Background: BSc in Mathematics from Zhejiang University, China (1988) PhD in Mathematics from Zhejiang University, China (1991) Professor Zhou's research spans learning theory, neural networks, wavelet analysis, and approximation theory, with his current focus on the theory of deep learning. His work aligns with the Faculty of Science Research Strengths in Complex Systems, Precision and Digital Health, Data and Decisions, and National Security. His research demonstrates a consistent progression from foundational mathematical theory to cutting-edge applications in machine learning and artificial intelligence, with particular emphasis on understanding the theoretical underpinnings of neural networks and deep learning systems. His extensive publication record reveals a strong trend toward distributed learning frameworks, approximation theory for neural networks, and the mathematical foundations of deep learning. Recent work focuses on federated learning, transformers, physics-informed neural networks, and the theoretical analysis of over-parameterized networks, reflecting the evolving landscape of machine learning research with increasing emphasis on theoretical guarantees and practical applications. Scientific Awards: Humboldt Research Fellowship (1993) Fund for Distinguished Young Scholars from the National Science Foundation of China (2005) Highly-cited Researcher by Thomson Reuters/Clarivate Analytics (2014-17) World's Top 2% Scientist by Stanford University (2021, 2022, 2023) Professor Zhou has demonstrated exceptional leadership in research and mentorship, having conducted over 40 research grants as Principal Investigator, supervised more than 20 PhD students, and co-organized over 20 international conferences. His collaborative approach is evident in his extensive co-authorship network across multiple institutions globally. He has also served in significant administrative roles including Head of Department of Mathematics (2006-12), Associate Dean of School of Data Science (2018-22), and Director of the Liu Bie Ju Centre for Mathematical Sciences (2019-22) at City University of Hong Kong.

• Learning Theory
• Neural Networks
• Deep Learning
• +6 more