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Renjie Feng is a Research Fellow in Mathematics and AI at the School of Mathematics and Statistics and the Sydney Mathematical Research Institute , University of Sydney. His work bridges probability theory, statistics, and applications in machine learning, deep learning, and artificial intelligence. His research interests focus on probability theory and its applications to machine learning , random matrix theory , and statistical physics . He investigates extreme value problems, spectral properties of random matrices, and topological features of random fields over Riemannian manifolds. Recent publications highlight trends in random matrix theory (GUE, GOE, GSE), extreme gap problems , determinantal point processes , and Wiener chaos . Collaborative works with F. Götze, D. Yao, and R. Adler emphasize U-statistics , multivariate linear statistics , and random topology inspired by Poisson point process studies.

• Probability Theory
• Random Matrix Theory
• Machine Learning
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Scientia Professor Robert Kohn is a distinguished academic at the University of New South Wales, holding a position in the School of Economics within the UNSW Business School. With a career spanning several decades, Professor Kohn has established himself as a leading expert in statistical methodology and econometric modeling. His research has significantly contributed to Bayesian statistics and computational methods for complex data analysis. Professor Kohn's research focuses on advanced statistical methodologies including Bayesian methodology, variable selection and model averaging, nonparametric regression models, time series modeling, multivariate Gaussian and non-Gaussian regression, and Markov chain Monte Carlo simulation algorithms. His work bridges theoretical statistics with practical applications across economics, finance, and cognitive science. His research demonstrates a consistent trajectory toward developing more efficient computational methods for complex statistical models, with recent work emphasizing variational Bayesian methods, particle filtering techniques, and applications to time series analysis. Analysis of his recent publications (2022-2025) reveals a strong focus on advancing computational statistical methods, particularly in Bayesian inference for complex models. His work shows increasing integration of machine learning techniques with traditional statistical methods, especially in handling high-dimensional data and complex time series structures. Professor Kohn has made significant contributions to variational inference methods, particle-based computational techniques, and applications to financial time series and cognitive modeling. Professor Kohn has maintained an exceptionally productive research career with continuous publication output since the 1970s, demonstrating remarkable longevity and adaptability in his research focus as statistical methodologies have evolved. His work shows strong international collaboration, particularly with researchers in Australia, the United States, and Europe, reflecting his standing in the global statistical community.

• Bayesian methodology
• Variable selection and model averaging
• Nonparametric regression models
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Professor Jiti Gao is a Donald Cochrane Chair in Econometrics & Business Statistics at Monash University's Faculty of Business and Economics. He leads the Department of Econometrics and Business Statistics, specializing in non- and semi-parametric econometrics, time-series analysis, and panel data methodologies. His research focuses on developing statistical models for climate change, energy demand, and financial forecasting. Affiliations: Monash University, Impact Labs Grants: Multiple ARC Discovery Projects (e.g., 2020–2025 on climate-energy time series, 2017–2020 on econometric model building) Collaborations: CSIRO, Yale University, and international partners from China, Norway, and Singapore Research interests include climate econometrics, financial time series, and policy evaluation. Over 136 publications span econometric theory and applications, with recent work on nonlinear trending models and quantile regression. His grants emphasize methodological advancements in time series and panel data analysis. Awards: Not explicitly mentioned, but recognition includes Australian Professorial Fellow status and international research leadership roles. Advising/Grants: Primary Investigator on multiple ARC-funded projects, focusing on climate modeling and financial econometrics Labs/Teams: Part of Monash's Impact Labs and collaborates with global institutions on climate and econometric initiatives

• Econometrics
• Non- and Semi-Parametric Econometrics
• Time-Series Analysis
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Jan de Gier is a Professor at the School of Mathematics and Statistics, The University of Melbourne . He is also the Founding Director of MATRIX , Australia’s residential research institute in the mathematical sciences, and a former Deputy Director and Chief Investigator in the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) . Additionally, he co-founded the Australian and New Zealand Association for Mathematical Physics (ANZAMP) in 2011 and served as its inaugural Chair. His research focuses on solvable lattice models at the intersection of mathematical physics and statistical mechanics . Key areas include the application of quantum integrability , algebraic structures like the Yang-Baxter equation, Hecke algebras, and quantum groups, as well as analytical methods such as complex analysis and elliptic curves. His work bridges pure and applied mathematics through connections between enumerative combinatorics , representation theory , and real-world phenomena like traffic flow modeling via exclusion processes . The 15 most recent articles reflect his expertise in integrable systems , non-equilibrium statistical mechanics , and algebraic combinatorics . Topics span Macdonald polynomials , stochastic duality , quantum spin chains , and traffic modeling , with methodologies involving matrix product forms , exact solutions , and critical phenomena analysis. He has contributed to editorial efforts through the AustMS Gazette and MATRIX Annals, and has been involved in public science communication via opinion pieces on mathematics funding and applications. His work emphasizes the importance of fundamental research in driving technological innovation, as highlighted in media articles discussing pi calculation , zero-knowledge proofs , and mathematics education .

• Mathematical Physics
• Statistical Mechanics
• Solvable Lattice Models
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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
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Dr Alex Sherman is a Lecturer at UNSW Sydney in the School of Mathematics & Statistics . He previously held postdoctoral positions at the University of Sydney with Kevin Coulembier and at Ben-Gurion University of the Negev with Inna Entova-Aizenbud. His research focuses on representation theory and supergeometry , with applications to Lie superalgebras , modular representation theory , and tensor categories . He has published extensively on topics such as ghost distributions, Duflo-Serganova functors, and the geometry of spherical supervarieties. Email: alex.sherman@unsw.edu.au Location: Room 4111, The Red Centre, UNSW Sydney, NSW 2052 In 2025 , he will lecture the Linear Algebra stream of MATH1241. He organizes the UNSW Pure Maths Seminar and Algebra Seminar , and has co-organized courses on Kazhdan-Lusztig equivalences and tensor categories.

• Representation Theory
• Supergeometry
• Lie Superalgebras
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Piotr Koniusz is a Principal Research Scientist at Data61/CSIRO and an Honorary Associate Professor at the Australian National University (ANU), with an Adjunct role at UNSW. He holds a PhD in Computer Vision from the University of Surrey (2013) and a BSc from Warsaw University of Technology (2004). His research focuses on Foundation Models, Representation Learning, and Few-shot Learning, with contributions to Graph Neural Networks and Adversarial Robustness. Key roles include Program Chair for NeurIPS’25, Senior Area Chair for ICML’25 and ICLR’25, and Workshop Co-Chair for WWW’25. Awards include the Sang Uk Lee Best Student Paper (ACCV’22) and recognition as an Outstanding Area Chair (ICLR 2021–2023). Research interests span Vision-Language Models (VLMs), Generative Adversarial Networks (GANs), and Domain Adaptation. He supervises PhD students at ANU and collaborates with industry on projects like traffic forecasting and ecotoxicology prediction.

• Foundation Models
• Representation Learning
• Graph Learning
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Julie Clutterbuck is an Associate Professor in the School of Mathematics at Monash University. Her research focuses on geometric analysis, partial differential equations, and spectral theory. She has led major projects funded by the Australian Research Council, including studies on curvature flows, spectral estimates, and optimal shapes. Clutterbuck has been recognized with the Gavin Brown Prize (2014) for outstanding mathematical research. She contributes to academic activities as a conference organizer and speaker, including roles at the AMSI Winter School and the New Zealand Mathematical Society Colloquium. Her teaching commitments include advanced courses in metric spaces and multivariable calculus. Her research explores geometric evolution equations, eigenvalue problems, and capillary surfaces. Notable projects include analyzing the fundamental gap conjecture and studying ancient solutions to curvature flows. Collaborations span international institutions, reflecting her contributions to global mathematical research networks.

• Geometric Analysis
• Partial Differential Equations
• Spectral Theory
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Dr. Chathura Bandutunga is a Research Fellow at the Centre for Gravitational Astrophysics within the Research School of Physics at the Australian National University (ANU). His research focuses on advanced optical techniques for precision measurement, with significant contributions to gravitational wave detection technology, molecular spectroscopy, and space exploration instrumentation. Dr. Bandutunga's research expertise spans digital interferometry, fiber optic sensors, and precision optical measurement systems. His work has pioneered digitally enhanced interferometric techniques that have enabled new capabilities in molecular dispersion spectroscopy, gravitational wave detection, and optical frequency referencing. He has developed innovative methods for phase noise suppression, common-mode noise rejection, and thermal-noise-limited optical measurements that operate at the boundaries of physical possibility. His publication record demonstrates consistent innovation in optical measurement technology, with recent work advancing fiber optic gyroscopes, frequency comb technology, and applications for interstellar propulsion systems like the Breakthrough Starshot program. His research bridges fundamental optical physics with practical applications in both terrestrial scientific instrumentation and space-based technologies. Dr. Bandutunga is actively involved in the Centre for Gravitational Astrophysics at ANU, contributing to Australia's participation in international gravitational wave research collaborations. His technical leadership in precision optical measurement systems directly supports next-generation gravitational wave detectors and related technologies requiring unprecedented measurement stability.

• Optical Physics
• Gravitational Wave Detection
• Fiber Optics
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Christina Lee is an Associate Professor in English and Cultural Studies at Curtin University, affiliated with the School of Media, Creative Arts and Social Inquiry and the Faculty of Humanities. She earned her PhD in Communication Studies from Murdoch University. Her research focuses on cultural memory, screen tourism, spectral spaces, and representations of gender/race in popular culture. Current projects explore affective landscapes in screen tourism (e.g., Bridgerton , Harry Potter ), urban ghost cities, and nostalgia in youth cinema. She edits peer-reviewed books and contributes to The Conversation . Teaching: Leads undergraduate units like Engaging Narratives and supervises postgraduate students in cultural/media studies. Active in curriculum development and assessment reform. Research Interests: Combines cultural theory with film/media analysis, emphasizing how spaces and spectrality shape cultural narratives. Recent projects include Screen Tourism and Affective Landscapes (2023) and Haunted Futures (2023). Her work often bridges academic critique with creative practice, as seen in video-installation collaborations. Memberships: Association for Cultural Studies, Cultural Studies Association of Australasia, and Graduate Women Western Australia. Her work has been showcased at venues like the John Curtin Gallery and PICA (Perth Institute of Contemporary Arts). Grants/Labs: Leads Curtin University’s tourism futures research (e.g., 2032 Western Australia Vision). Collaborates with interdisciplinary teams on projects like sustainable mining paradoxes (2018) and Hong Kong’s educational reforms (2016).

• Cultural Memory
• Screen Tourism
• Spectral Spaces
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Andrea Collevecchio is a Professor in the School of Mathematics at Monash University, Australia, where he has been a faculty member since 2012. His research focuses on the intersection of Probability, Mathematical Physics, and Statistical Mechanics, with particular expertise in stochastic processes and theoretical modeling. He earned his PhD in Statistics from Purdue University in 2004, followed by postdoctoral positions in Italy and Germany. In 2006, he became Assistant Professor at Ca’Foscari University in Venice before joining Monash University. Collevecchio specializes in Reinforced Processes and Large Deviations, investigating complex systems through random walk models. His work bridges abstract probability theory with applications in statistical mechanics, examining phenomena like memory effects in stochastic processes and phase transitions in lattice systems. Recent research emphasizes hypercube structures, non-reversible dynamics, and reinforcement mechanisms. His 2021-2025 publications reveal a concentrated focus on hypercube random walks, with increasing exploration of non-reversible processes, vertex-reinforced dynamics, and bootstrap methods. These works consistently apply probabilistic frameworks to problems in mathematical physics, demonstrating strong connections between theoretical probability and physical modeling. Collevecchio has secured multiple research grants including ARC-funded projects on self-interacting random walks (2023-2026) and random walks with long memory (2018-2022). He contributes to interdisciplinary initiatives like the Smart Vehicles project for dementia support (2025-2027) and actively organizes academic events including the AIM Day series connecting mathematics, AI, and industry applications.

• Probability
• Mathematical Physics
• Statistical Mechanics
• +3 more

Dr. Emma Carberry is a Lecturer in the School of Mathematics and Statistics at the University of Sydney, within the Faculty of Science. Her research focuses on differential geometry and integrable systems, particularly harmonic maps, conformal surface theory, and constant mean curvature surfaces. She is a member of the Sydney Southeast Asia Centre. Her research interests include geometric applications of integrable systems, spectral curves, and the interplay between differential geometry and complex algebraic geometry. Notable contributions include studies on harmonic tori, Whitham deformations, and constant mean curvature surfaces in various spaces. Dr. Carberry has secured grants including the DVC Research/Brown Fellowship (2016) and DVC Research/Laffan Fellowship (2010). Her teaching includes advanced courses such as MATH3968 (Differential Geometry: Advanced), Honours Riemannian Geometry, and undergraduate modules like MATH1011 (Applications of Calculus). Her academic work spans over two decades, with publications in journals like the Journal of Geometry and Physics and the Journal of the London Mathematical Society. She collaborates internationally and maintains an active research profile in geometric analysis and integrable systems.

• Differential Geometry
• Integrable Systems
• Harmonic Maps
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Professor Martijn de Sterke is a Professor in the Department of Physics at the University of Sydney and a member of the Sydney Nano Institute. He holds a MEng in Applied Physics from Delft University of Technology (1982) and a PhD in Optics from the University of Rochester (1987). His postdoctoral work at the University of Toronto (1988–1990) preceded his faculty appointment at the University of Sydney, where he has contributed significantly to the field of nonlinear optics. His research focuses on nonlinear optics, photonic crystals, soliton dynamics, and plasmonic systems. Notable contributions include studies on soliton microcombs, metamaterial-enhanced optical effects, and relativistic lightsail propulsion concepts. He has pioneered work on pure-quartic solitons and their applications in fiber lasers, as well as investigations into Förster resonance energy transfer in engineered metamaterials. Educations: MEng in Applied Physics, Delft University of Technology (1982) PhD in Optics, University of Rochester (1987) His publications span over 300 works, including key contributions to Optics Express as Editor-in-Chief from 2007–2012. He has received prestigious awards such as the Pawsey Medal (1999), Esther Hoffman Beller Medal (2017), and Beatty Steel Medal (2024). Current research activities include ARC-funded projects on optical microcombs and dispersion-engineered solitons. His work bridges theoretical models and experimental implementations, with applications in ultrafast optics, nanophotonics, and space propulsion systems leveraging optical forces.

• Nonlinear Optics
• Photonic Crystals
• Plasmonics
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Dr. Xuhui Fan is a Lecturer in Artificial Intelligence at the School of Computing, Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (Australia) and a bachelor's degree in Mathematical Statistics from China. Prior to his current role, he worked as a project engineer at Data61 (formerly NICTA), a postdoc fellow at the University of New South Wales, and a lecturer at the University of Newcastle. His research focuses on Bayesian methods, federated learning, temporal point processes, and neural network architectures. He is affiliated with the Data Horizons Research Centre and the Frontier AI Research Centre at Macquarie University. Key research interests include developing interpretable AI models, advancing federated learning for privacy-sensitive applications, and applying Bayesian techniques to complex data analysis. His work bridges theoretical advancements in machine learning with practical applications in areas such as anomaly detection, generative models, and spatio-temporal data analysis. Dr. Fan’s publications span top-tier conferences like NeurIPS, ICML, and IJCAI, covering topics such as diffusion models, nonstationary processes, and scalable relational models. He has contributed to surveys on Bayesian federated learning and developed novel frameworks for dynamic customer segmentation and network sustainability. His research collaborations span institutions in Australia and internationally, reflecting his expertise in interdisciplinary AI applications. Current projects emphasize ethical AI practices, efficient uncertainty quantification, and scalable inference techniques for large-scale datasets.

• Artificial Intelligence
• Machine Learning
• Bayesian Methods
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Haotian Wu is a Senior Lecturer in the School of Mathematics and Statistics at The University of Sydney, where he maintains an active research program in geometric analysis. His academic appointments include membership in the Geometry, Topology and Analysis Research Group, and he has been involved in organizing significant mathematical events including the AMSI Summer School 2025 and the Symposium on Geometric Analysis and Non-linear PDEs. Dr. Wu's educational background includes a PhD in Mathematics from The University of Texas at Austin (2013) and dual Bachelor's degrees in Mathematics and Physics from Lafayette College (2007). His research aligns with the University's Faculty of Science Research Strengths in Understanding the Universe, Fundamental Laws of Nature, and Complex Systems. Wu's research focuses on geometric evolution equations, particularly Ricci flow and mean curvature flow. His work investigates singularity formation, stability properties, and asymptotic behavior in these flows, with significant contributions to understanding Type-II singularities. His research bridges pure mathematics and mathematical physics, with applications to general relativity and geometric analysis. He has developed novel analytical techniques for studying nonlinear partial differential equations arising in geometric contexts. Analysis of Wu's publication record shows a consistent trajectory of high-impact research in geometric analysis, with increasing focus on numerical methods for studying geometric flows in recent years. His work demonstrates strong international collaboration, particularly with researchers in the United States, including notable collaborations with Garfinkle, Isenberg, Knopf, and Zhang. The publications reveal a progression from foundational work on Ricci flow to increasingly sophisticated analyses of mean curvature flow and related geometric evolution equations. Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) 2018 for 'Singularity Analysis for Ricci Flow and Mean Curvature Flow' Faculty of Science Faculty Startup Scheme 2023 for 'Elliptic and parabolic problems in geometric analysis' Dr. Wu currently supervises research students including Alexander BEDNAREK (working on General Kahler Ricci Flow) and Tiernan CARTWRIGHT (working on Hölder regularity of solutions to degenerate complex Monge–Ampère equations). He has received multiple research grants supporting his work in geometric analysis. Wu has been actively involved in teaching undergraduate mathematics courses including MATH1021 Calculus of One Variable, MATH2061 Linear Mathematics and Vector Calculus, and specialized courses like MATH3968 Differential Geometry. As an active member of the mathematical community, Wu co-organizes significant research events including the AMSI Summer School 2025 and the International Conference on Nonlinear Partial Differential Equations honoring Professor Neil Trudinger's 80th birthday. His research group focuses on geometric analysis problems with connections to mathematical physics and differential geometry.

• Geometric Analysis
• Differential Geometry
• Partial Differential Equations
• +3 more