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Scott SissonView profile

Professor

• Computational Statistics
• Bayesian Statistics
• Big Data
• +4 more

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

Jiti GaoView profile

Professor

• Econometrics
• Non- and Semi-Parametric Econometrics
• Time-Series Analysis
• +3 more

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

• Bayesian methodology
• Variable selection and model averaging
• Nonparametric regression models
• +3 more

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.

Quoc Thong Le GiaView profile

Associate Professor

• Numerical Analysis
• Approximation Theory
• Partial Differential Equations
• +5 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).

Alan HuangView profile

Senior Lecturer

• Biostatistics
• Mathematical Sciences
• Probability theory
• +6 more

Dr. Alan Huang is a Senior Lecturer at the School of Mathematics and Physics, University of Queensland. He holds a PhD in Statistics from the University of Chicago (McCormick Fellowship) and an Honours degree in Science (Advanced Mathematics) from the University of Sydney. His academic career includes lecturing roles at the University of Wisconsin-Madison and the University of Technology Sydney before joining UQ. Research Focus: Biostatistics, nonparametric methods, and statistical modeling for dispersed counts. Key Projects: Bayesian methods for agricultural data, trend analysis of pesticide concentrations in the Great Barrier Reef, spectral water quality analysis. Article Trends: His work spans generalized linear models, count data analysis, and environmental statistics, with recent emphasis on Conway-Maxwell-Poisson regression and time-series modeling. Collaborations include environmental science applications. Awards: McCormick Fellowship (University of Chicago). Supervision: Currently advising PhD research on count data methods. Past supervision includes topics in geotechnical uncertainty and rock mechanics. Collaborates with Queensland Department of Environment and Science on water quality projects.

Andy McLennanView profile

Professor

• Mathematical Economics
• Game Theory
• Algebraic Geometry
• +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.

• Cybersecurity and privacy
• Privacy protection
• AI and machine learning
• +4 more

Dr. Bo Liu is an Associate Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he serves as a core member and director of the AI Security and Privacy (AISP) Research Lab at the Australian Artificial Intelligence Institute (AAII). With expertise spanning cybersecurity, privacy protection, AI and machine learning, and wireless communications, Dr. Liu has established himself as a leading researcher in the field of AI security and privacy. Dr. Liu earned his PhD from the Department of Electronic Engineering at Shanghai Jiao Tong University in 2010. His academic journey at UTS has progressed from Senior Lecturer (November 2019-December 2022) to his current position as Associate Professor (January 2023-present). Dr. Liu's research focuses on the critical intersection of artificial intelligence and security, particularly addressing emerging threats in the age of advanced AI systems. His work spans multiple dimensions of security and privacy, including deepfake detection, privacy-preserving data synthesis, AI model security, and fair machine learning. He has pioneered approaches to detect AI-generated content, protect visual privacy through de-identification techniques, and address the complex relationship between algorithmic fairness and privacy preservation. His publication record demonstrates significant contributions across multiple cutting-edge research areas, with particular emphasis on detecting and mitigating threats from generative AI systems. His recent work reveals a strong focus on deepfake detection across multiple modalities (images, video, and audio), privacy-preserving techniques for sensitive data, and the security implications of emerging AI architectures like Retrieval-Augmented Generation systems. Dr. Liu has secured substantial research funding, including as Lead Chief Investigator on multiple ARC Discovery and Linkage Projects, totaling over $3.5 million AUD. His industry collaborations include partnerships with the NSW Department of Planning and the Reserve Bank of Australia, demonstrating the practical applicability of his research. As an academic leader, Dr. Liu serves as Associate Editor for IEEE Transactions on Broadcasting and actively contributes to the academic community through conference organization, peer review for top-tier venues, and assessment for ARC grant schemes. He also teaches courses including Penetration Testing, Ethical Hacking and Offensive Security, and supervises Masters and PhD students in cybersecurity and privacy research.

David SewellView profile

Senior Lecturer

• Attention
• Learning
• Memory
• +7 more

Dr. David Sewell is a Senior Lecturer and Deputy Head of School (Teaching & Learning) at the School of Psychology, The University of Queensland. His research focuses on attention, learning, memory, and decision-making, with a strong emphasis on formal mathematical models of human cognition. He is affiliated with the Centre for Perception and Cognitive Neuroscience within the Faculty of Health, Medicine and Behavioural Sciences. Education: Bachelor (Honours) of Arts and Doctor of Philosophy, both from the University of Western Australia. David's research explores the intersection of cognitive psychology and computational modeling. Key areas include perceptual decision-making, attentional mechanisms, and the application of diffusion models to understand cognitive processes. His work also extends to sustainability and collective self-regulation through cognitive frameworks. The 15 most recent articles highlight his contributions to modeling decision thresholds in memory prioritization, analyzing gaze cueing effects, and investigating neural correlates of confidence in multisensory decisions. Collaborative projects frequently involve interdisciplinary approaches, combining neuroscience, psychology, and computational methods. He has supervised multiple PhD candidates, serving as Principal or Associate Advisor, with research topics ranging from visual categorization to metacognition in children. Current and past funding includes ARC Discovery Projects on collective self-regulation and category learning constraints.

Brett HayesView profile

Professor

• Reasoning
• Concept Learning
• Memory
• +8 more

Professor Brett Hayes is a distinguished cognitive psychologist at the University of New South Wales, serving in the School of Psychology. He is the founding Director of the Sydney Thinking and Reasoning (STAR) Laboratory, which he has led for over 15 years, securing more than $4 million in competitive research funding. Professor Hayes has previously held the position of Head of the School of Psychology and served as a member of the Australian Research Council (ARC) College of Experts. His research expertise spans reasoning, concept learning, memory, and developmental changes in these cognitive processes. Professor Hayes employs both experimental investigation and computational modeling in his work, with a particular focus on applying fundamental cognitive research to practical problems in forensic and clinical decision-making, early childhood education, and climate change science communication. His research has significant interdisciplinary applications across psychology, education, and environmental science. Professor Hayes has published extensively in top cognitive science journals, with his most recent work focusing on inductive reasoning, sampling assumptions, learning traps, and consensus perception. His research demonstrates consistent innovation in understanding how people process information, make decisions under uncertainty, and develop reasoning abilities across the lifespan. His scientific contributions include numerous journal articles, book chapters, and co-authored textbooks on developmental psychology. Professor Hayes has also contributed to teaching through courses such as PSYC3341 Developmental Psychology (which he chairs), PSYC3221 Cognitive Science, and PSYC2061 Developmental and Social Psychology. Professor Hayes maintains an active research program with ongoing collaborations across multiple institutions, as evidenced by his numerous co-authored publications. His laboratory continues to advance our understanding of human cognition through rigorous experimental work and theoretical development.

Mohamad KhaledView profile

Senior Lecturer

• Applied Health Economics
• Health Econometrics
• Econometrics
• +3 more

Dr. Mohamad Khaled is a Senior Lecturer in the School of Economics at The University of Queensland (UQ), affiliated with the Centre for Efficiency and Productivity Analysis within the Faculty of Business, Economics and Law. His research focuses on Applied Health Economics , Health Econometrics , and broader Econometrics methodologies. He specializes in analyzing health inequality, socioeconomic determinants of health, and statistical modeling techniques for economic data. His work addresses critical issues such as child malnutrition, health concentration curves, and income-related health disparities. Dr. Khaled has contributed to peer-reviewed journals including European Journal of Health Economics , Journal of Business and Economic Statistics , and Health Economics . His methodologies often involve advanced econometric frameworks and Bayesian approaches. He holds an academic position in a leading economics department and collaborates with institutions like Edward Elgar Publishing. His research emphasizes bridging theoretical econometric models with real-world health policy applications.

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

• Spatio-temporal Statistics
• Computational Statistics
• Environmental Informatics
• +3 more

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

Ruben Loaiza MayaView profile

Associate Professor

• Copula Modelling
• Bayesian Estimation Methods
• Time Series Analysis
• +4 more

Ruben Loaiza-Maya is an Associate Professor (Research) in the Department of Econometrics and Business Statistics at Monash University. He holds a PhD in Econometrics from the University of Melbourne and an undergraduate degree in Economics from Universidad Nacional de Colombia (Medellin). His research focuses on Copula Modelling, Bayesian Estimation Methods, Time Series Analysis, and Macroeconomic/Financial Forecasting. Key contributions include advancements in variational inference, state space models, and robust forecasting techniques under model misspecification. He leads the active project 'Variational Inference for Intractable and Misspecified State Space Models' (2023–2026), funded as a Primary Chief Investigator. His work contributes to UN Sustainable Development Goals through methodological advancements in economic and financial analysis. Recent research emphasizes scalable Bayesian methods, hybrid variational approaches, and efficient computational techniques for high-dimensional models. Publications span prestigious journals like the International Journal of Forecasting, Journal of Econometrics, and Journal of Business and Economic Statistics. Notable collaborations include studies on copula-based time series forecasting and robust approximate Bayesian computation. His work bridges theoretical econometrics with practical applications in risk management and macroeconomic policy.

Xiaotian ZhengView profile

Assistant Professor

• Spatial Statistics
• Spatio-Temporal Statistics
• Point Process Modeling
• +5 more

Xiaotian Zheng is an Assistant Professor of Statistics at the University of Georgia. Previously, they were a Postdoctoral Research Fellow with the Australian Research Council Special Research Initiative Securing Antarctica's Environmental Future at the University of Wollongong, working under Professor Noel Cressie and Associate Professor Andrew Zammit-Mangion. They earned their Ph.D. in Statistical Science from the University of California, Santa Cruz, advised by Professors Athanasios Kottas and Bruno Sansó. Their research focuses on developing statistical and machine learning methods for analyzing complex, dependent data, particularly in ecological and environmental contexts. Key areas include spatial/spatio-temporal statistics, probabilistic downscaling, data integration, transfer learning, and statistical deep learning. Xiaotian's publications reflect their work on mixture transition distribution models, nearest-neighbor mixture models, and geostatistical frameworks for discrete-valued processes. These contributions emphasize Bayesian inference, computational efficiency, and real-world applications in environmental science and biodiversity modeling.

Howard BondellView profile

Professor

• Statistical Data Science
• Model Selection
• Robust Estimation
• +5 more

Howard Bondell is a Professor of Statistical Data Science at the School of Mathematics and Statistics, University of Melbourne, since 2018. He serves as Head of School since 2021, Co-Director of the Melbourne Centre for Data Science, and holds an ARC Future Fellowship (2020-2024). Ph.D. in Statistics, Rutgers University (2005) Academic Career: North Carolina State University (2005-2018) His research focuses on model selection , robust estimation , regularisation , Bayesian methods , and uncertainty quantification in statistical and machine learning. His publications emphasize applications in regression analysis, quantile modeling, variable selection for high-dimensional data, and genetic data analysis. Scientific awards include: Fellow of the American Statistical Association (2017) ARC Future Fellow (2020-2024)