Randomized Numerical Linear Algebra Professors and Supervisors in Australia

Luke Prendergastمشاهده پروفایل

استاد

La Trobe UniversityAustralia

Professor Luke Prendergast is the Deputy Dean of the School of Computing, Engineering & Mathematical Sciences (SCEMS) at La Trobe University (LTU) and holds a Professorship in the Department of Mathematics and Statistics. He previously served as Head of Department (2014–2020) and led LTU's Statistics Consulting Platform. His research focuses on robust statistics, meta-analysis, dimension reduction, and applied statistics, leading the DRAMA research group. Collaborations span fields like endocrinology, disability studies, and respiratory health. He actively contributes to research grants, including projects on Prader-Willi syndrome and exercise for disability populations. Professor Prendergast's recent work emphasizes statistical software development (e.g., the rquest package) and applications in biostatistics, such as metabolomics analysis and health intervention fidelity. His articles address topics like quantile-based hypothesis testing, geospatial accessibility for disability care, and motivational interviewing efficacy. Professional roles include NHMRC grant review panels, editorial boards for Nutrients and Respirology , and leadership in the Statistical Society of Australia (SSA Vic). His teaching includes courses in meta-analysis, linear models, and data-based critical thinking. Grants funded projects on exercise programs for cerebral palsy populations and community-university partnerships for disability inclusion. Luke's work bridges statistical theory with real-world health challenges, emphasizing robust methodologies and interdisciplinary collaboration.

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Josef Dickمشاهده پروفایل

استاد

University of New South WalesAustralia

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.

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Guoyin Liمشاهده پروفایل

استاد

University of New South WalesAustralia

Professor Guoyin Li is a faculty member at the School of Mathematics & Statistics , University of New South Wales (UNSW Sydney). He holds a Ph.D. from The Chinese University of Hong Kong (2007) and has been at UNSW since 2011, currently serving as Professor and Research Director. Research Interests His work spans optimization , variational analysis , and multilinear algebra , with applications in robust optimization , structural engineering , and machine learning . He specializes in nonconvex nonsmooth optimization , tensor eigenvalue problems , and exact semi-definite programming relaxations . Recent Publications His articles focus on robust optimization for structural design, nonlinear approximation techniques, and conic programming for uncertain data. Key journals include Foundations of Computational Mathematics , Mathematical Programming , and Computer Methods in Applied Mechanics and Engineering . Awards and Grants Fellow of the Australian Mathematical Society (2023) 2022 AustMS Medal 2024 Marguerite Frank Award ARC Discovery Grants (2021-2023, 2025-2027) ARC Research Hub Project (2017-2021) Professional Roles He serves on editorial boards of SIAM Journal on Optimization , Optimization Letters , and Journal of Optimization Theory and Applications , and has delivered plenary lectures at international conferences in Austria, Spain, and Canada.

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Elliot Carrمشاهده پروفایل

مدرس ارشد

Queensland University of TechnologyAustralia

Dr. Elliot Carr is a Senior Lecturer in the School of Mathematical Sciences at Queensland University of Technology (QUT), Faculty of Science. He holds a PhD in Mathematics from QUT and has been a faculty member since 2015, progressing from Lecturer to his current rank. His research and teaching focus on applied and computational mathematics, with strong interdisciplinary applications. Education: PhD in Mathematics, Queensland University of Technology, 2009–2012 Bachelor of Applied Science (Honours) in Mathematics, QUT, 2008 Bachelor of Mathematics, QUT, 2005–2007 Elliot Carr's research lies at the intersection of applied mathematics and real-world physical systems. His work centers on developing and analyzing mathematical models of advection, diffusion, and reaction processes, particularly in heterogeneous media. He employs both deterministic (PDE-based) and stochastic (random walk) frameworks, contributing to analytical solutions, multiscale modeling, surrogate models, and numerical methods such as finite volume and Newton-Krylov techniques. His research has been applied to diverse fields including groundwater contamination, drug delivery, heat transfer, and tumor spheroid modeling. The latest publications reflect a consistent focus on transport phenomena in complex geometries and heterogeneous environments. Key themes include dual-grid mapping for contaminant transport, analytical modeling of drug release from spherical capsules, thermal diffusivity in shell geometries, and stochastic models of biological systems. His methodological contributions span analytical, numerical, and statistical approaches, demonstrating versatility across applied mathematics. Scientific Awards and Recognitions: JH Michell Medal, ANZIAM (2022) ARC DECRA Fellowship (2015) QUT Outstanding Doctoral Thesis Award (2012) University Medal, QUT (2008) Dean’s Award for top graduate in both Honours and Bachelor programs Keynote and plenary speaker at major conferences including ANZIAM and Forum “Math-for-Industry” Dr. Carr actively supervises PhD and Masters students, with completed and ongoing projects on diffusive transport, tumor modeling, and sports analytics. He has secured competitive research funding, including an ARC Discovery Project on multiscale modeling. His teaching includes computational mathematics, linear algebra, and differential equations, with a focus on MATLAB-based implementation. He is a member of the Australian Mathematical Society (AustMS) and ANZIAM. Research Labs and Teams: While not explicitly tied to a named lab, Carr is part of the broader Applied Modelling and Computation research environment at QUT. He collaborates extensively with researchers such as Ian Turner, Matthew Simpson, and Chris Drovandi, contributing to interdisciplinary teams in mathematical biology, environmental modeling, and statistical computation.

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Rohan Hitchcockمشاهده پروفایل

پژوهشگر

University of MelbourneAustralia

Rohan Hitchcock is a Researcher in the Department of Electrical and Electronic Engineering, focusing on interdisciplinary research at the intersection of machine learning and computational physics. His work emphasizes developing physically informed ML architectures to accelerate particle-based models, as reflected in his thesis titled 'Beyond the Black Box.' He is supervised by Prof Jonathan Manton. His research interests span machine learning, computational physics, statistical mechanics, and algebraic topology. Recent articles highlight contributions to stochastic processes, neural network physics simulators, and theoretical frameworks in category theory. Rohan's publications reflect a blend of applied and theoretical work, including studies on stochastic gradient Langevin dynamics (SGLD), entropy in thermodynamic systems, and advanced mathematical constructs like Landau-Ginzburg models and matrix factorizations.

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Thanh Tranمشاهده پروفایل

استاد

University of New South WalesAustralia

Computational Mathematics
Numerical Methods for Partial Differential Equations (PDEs)
Stochastic Differential Equations
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Professor Thanh Tran is a faculty member at the School of Mathematics & Statistics, University of New South Wales (UNSW Sydney). His research focuses on computational mathematics, particularly numerical methods for deterministic and stochastic partial differential equations (PDEs), with applications in physical sciences, engineering, and magnetic materials. He has secured multiple competitive research grants, including ARC Discovery Projects and international collaborations. Education: PhD from UNSW Sydney Research Grants: ARC Discovery Projects (2012-2026), UNSW Goldstar Awards (2011, 2015), Go8-Germany Joint Research Scheme (2013) Teaching: Courses include Stochastic Differential Equations (MATH3361/5361), Mathematics for Actuarial Studies (MATH1251), and Applied Analysis (MATH3051) His work on numerical methods spans preconditioning techniques, error estimation, adaptivity, and finite element-boundary element coupling. Recent publications emphasize stochastic PDEs and nonlinear evolution equations, with applications in magnetohydrodynamics and chemotaxis models. Scientific awards highlight his contributions to interdisciplinary mathematics, particularly through collaborative projects on magnetic memory materials and random boundary problems. He actively supervises PhD and Honours students in computational mathematics, focusing on Maxwell-Landau-Lifshitz-Gilbert equations and shape calculus.

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Nan Yeمشاهده پروفایل

مدرس ارشد

University of QueenslandAustralia

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

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Ali Eshraghمشاهده پروفایل

مدرس ارشد

University of Newcastle Australia (UON)Australia

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.

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Michael Mahoneyمشاهده پروفایل

استاد

University of QueenslandAustralia

Michael Mahoney is Professor of Statistics at the University of California, Berkeley, with additional affiliations at the International Computer Science Institute (ICSI) where he is Vice President and Director of the Big Data Group, the Lawrence Berkeley National Laboratory (LBNL) where he leads the Machine Learning and Analytics Group, and the EECS department's RISELab. He is also an Amazon Scholar. Education: While specific degrees are not listed in the provided text, his extensive record of teaching, research leadership, and publications indicates doctoral-level training in Statistics and Computer Science. Research Interests: Mahoney's work centers on the applied mathematics of data , spanning algorithmic and statistical foundations of big data, randomized numerical linear algebra (RandNLA), high-dimensional statistics, machine learning, and scientific machine learning. He develops theory, scalable implementations, and real-world applications in internet analytics, social networks, genetics, astronomy, and climate science. Recent software contributions include the RandBLAS and RandLAPACK libraries (standardizing RandNLA routines), Landscaper for visualizing deep-learning loss landscapes, and packages such as FreeAlg , DetKit , Imate , and LeaderBot . Awards & Honors: NeurIPS 2020 Best Paper Award (co-authored work on column subset selection) Director, NSF TRIPODS UC Berkeley FODA Institute Key contributor to the BALLISTIC project for next-generation BLAS/LAPACK Grants & Leadership: Principal Investigator, NSF TRIPODS FODA Institute (Foundations of Data Analysis) Group Lead, Machine Learning and Analytics, LBNL Vice President & Director, Big Data Group, ICSI Advising & Mentoring: Mahoney has an extensive network of current and former PhD students, postdocs, and visiting researchers, including placements at MIT, Stanford, Waterloo, Stevens, Tsinghua, and Georgia Tech. Current advisees include Shengaho Yang, Zhichao Wang, Hyunsuk Kim, and Pu Ren, among many others. Labs & Teams: He directs research efforts across UC Berkeley Statistics, ICSI’s Big Data Group, LBNL’s Machine Learning and Analytics Group, and the RISELab (formerly AMPLab), fostering cross-disciplinary collaboration between statistics, computer science, and domain sciences.

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William McLeanمشاهده پروفایل

دانشیار

University of New South WalesAustralia

Associate Professor William McLean is affiliated with the School of Mathematics & Statistics at the University of New South Wales (UNSW), where he contributes to computational mathematics and numerical analysis. He serves as the School Records System Manager and Chair of the Computing Committee. His research focuses on computational mathematics , integral equations , and nonlocal evolution equations , with a strong emphasis on time-fractional differential equations , discontinuous Galerkin methods , and numerical stability . He has published extensively in SIAM Journal on Numerical Analysis and other leading journals. Recent work includes advancements in stochastic time-fractional diffusion , α-robust numerical schemes , and Mittag-Leffler function computation , reflecting his expertise in fractional calculus and adaptive numerical methods . He also serves on the editorial boards of SIAM Journal on Numerical Analysis and Journal of Integral Equations and Applications . For teaching, he leads courses in Computational Mathematics (MATH3101/5305) and Numerical Linear Algebra (MATH3371/5371).

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Sergei Balakinمشاهده پروفایل

پژوهشگر ارشد

Monash UniversityAustralia

Sergei Balakin is a Research Fellow at Impact Labs, Monash University, with active research output spanning two decades from 2003 to 2025. His work bridges theoretical mathematics with practical applications in finance and computing systems. His research focuses on Applied Mathematics, Probability and Statistics, Financial Mathematics, Operator Theory, and Computer Science. Key contributions include combinatorial analysis of Markov sequences, stochastic modeling for computing systems, and dynamic optimization for energy storage trading, demonstrating strong interdisciplinary connections between pure mathematics and real-world problems. Publication trends show evolution from foundational operator equations (2008-2009) to combinatorial probability (2011) and stochastic computing models (2013), culminating in recent financial mathematics applications (2025). His work consistently applies advanced mathematical techniques to solve complex problems across multiple domains. Based at Impact Labs, Monash University, Balakin contributes to interdisciplinary research initiatives where mathematical modeling intersects with economic and computational challenges.

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Marco Tomamichelمشاهده پروفایل

مدرس ارشد

University of Technology SydneyAustralia

Marco Tomamichel is a Senior Lecturer and Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) fellow at the Centre for Quantum Software and Information, which is part of the Faculty of Engineering and Information Technology at the University of Technology Sydney. He received his Master of Science in Electrical Engineering and Information Technology from ETH Zurich, where he was awarded the prestigious ETH Medal, followed by a doctorate in Theoretical Physics from the same institution. Prior to joining the University of Technology Sydney, Dr. Tomamichel held research positions at the Centre for Quantum Technologies in Singapore and the University of Sydney. Dr. Tomamichel's research lies at the intersection of information theory, computer science, and quantum physics, with a particular focus on the mathematical foundations of quantum information theory. His work explores entropy and other information measures, and addresses theoretical questions in quantum communication and cryptography when resources are limited. His research examines quantum information processing with noisy and limited resources, cryptography in a quantum world, mathematical foundations of quantum information, and other applications of quantum information theory in physics, engineering, and computer science. His recent publications demonstrate a strong focus on quantum information theory, with particular emphasis on entropies, divergences, and their applications. His work spans fundamental theoretical developments in quantum information measures, quantum cryptography protocols, quantum communication theory, and connections to quantum thermodynamics and machine learning. The recurring theme across his publications is the rigorous mathematical treatment of quantum information processing tasks, often focusing on finite-resource scenarios that are relevant for near-term quantum technologies. Dr. Tomamichel has received significant recognition for his research, including: Australian Research Council Discovery Early Career Researcher Award (DECRA) ETH Medal for Master's degree Dr. Tomamichel is available for Masters Research and PhD student supervision. His funded research projects include "Securing the quantum internet with high-dimensional quantum systems" (ARC Discovery Projects), "Topological algebra, first-order logic, and computability" (Royal Society of New Zealand), and "Enhancing Communication using Small Quantum Devices" (ARC DECRA Scheme). Dr. Tomamichel is actively involved in the quantum information research community, serving as an editor for "Quantum - the open journal for quantum science" and participating in numerous conference organizing committees including TQC, QIP, and Quantum Cryptography conferences.

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Wenjie Niمشاهده پروفایل

پژوهشگر ارشد

University of New England AustraliaAustralia

Nonlinear Partial Differential Equations
Mathematical Biology
Reaction-Diffusion Systems
+۱ مورد دیگر

Wenjie Ni is a Research Fellow in the School of Science and Technology at the University of New England. He earned his Ph.D. in Mathematics from Harbin Institute of Technology in 2018, specializing in reaction-diffusion equations and free boundary problems. His research focuses on nonlinear partial differential equations with applications in mathematical biology, particularly examining propagation dynamics in ecological and epidemiological systems. He teaches Linear Algebra and Differential Equations and maintains memberships in professional organizations including the Australian Mathematical Society. His recent publications analyze spatial spread mechanisms in biological systems through advanced mathematical frameworks, with consistent focus on nonlocal diffusion phenomena and free boundary formulations across ecological and epidemic contexts.

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Paul Vrbikمشاهده پروفایل

مدرس ارشد

University of QueenslandAustralia

Dr Paul Vrbik is a Senior Lecturer at the School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology at The University of Queensland. He holds a Bachelor (Honours) in Mathematics from McMaster University, a Master of Science in Mathematics and Computer Science from Simon Fraser University, and a PhD in Computer Science from the University of Western Ontario. His research focuses on Cyber Security, Software Engineering, and broader areas including Mathematics (algorithms, stochastic processes) and Education in Computing (academic dishonesty, assessment methods). Notable contributions include novel proofs in linear algebra, studies on student perceptions of academic integrity, and algorithmic advancements in algebraic geometry. Recent work highlights include examining student understanding of academic dishonesty in computer science (2024), a systematic review of testing modalities in engineering education (2022), and contributions to determinant identities in mathematics (2023). His publications span journals like Mathematics Magazine and conferences such as SIGCSE and IEEE EDUCON. Dr Vrbik collaborates on interdisciplinary projects, blending theoretical computer science with practical educational research. His academic profile includes contributions to computational tools like the RegularChains library and advancements in symbolic computation algorithms.

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