Erniel Bayhon Barrios is a Professor at the Malaysia School of Business, Monash University. Formerly a professor at the University of the Philippines Diliman and a visiting scholar at Karlstad University (Sweden) and the Asian Development Bank Institute (Japan). He holds a PhD in Statistics (1990) from the University of the Philippines Diliman. His research focuses on computational statistics, nonparametric methods, data science, and computational econometrics, with applications in spatiotemporal modeling, time series, and financial markets. He has contributed to high-dimensional data analysis, volatility modeling, and robust statistical techniques. Key projects include the National Mental Health Survey and Well-Being (2019–2021) and building a data-driven organization for the Habib Group (2025–2026). He is an elected member of the International Statistical Institute (2012), associate editor of Communications in Statistical Applications and Methods , and served on the board of the International Association of Statistical Computing (2022–2025). He advises PhD students on topics like stochastic frontier models, data assignment in big data, and high-frequency time series analysis. His work aligns with UN Sustainable Development Goals related to education and economic growth.
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Wooyong Lee is a Lecturer in the Economics Discipline Group at the UTS Business School, University of Technology Sydney. He holds a PhD in Economics from the University of Chicago (2020), an MS in Statistics from the University of British Columbia (2014), and a BA in Economics and Statistics from Korea University (2012). His research focuses on econometrics and applied microeconomics, specializing in panel data methods, difference-in-differences frameworks, and dynamic models. He has developed methodologies addressing spillover effects in staggered DiD designs and partial identification in heterogeneous coefficient models. His work applies to real-world issues like lifecycle earnings dynamics and policy evaluation. Lee teaches econometrics at undergraduate and postgraduate levels and supervises research students. His publications appear in venues such as Statistical Inference for Stochastic Processes and peer-reviewed working papers. Research interests emphasize causal inference techniques, with contributions to handling unobserved heterogeneity and measurement errors in economic data. Ongoing work explores dynamic treatment choice models where treatment decisions respond to outcome shocks, challenging traditional parallel trends assumptions.
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.
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.
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
Xuan Liang is a Lecturer in Statistics at the Research School of Finance, Actuarial Studies and Statistics (RSFAS), Australian National University. With a PhD from Peking University and postdoctoral experience at Monash University, his research focuses on spatial statistics, nonparametric modeling, and environmental data analysis. Education: PhD in Statistics (Peking University, 2017), BSc in Statistics (Zhejiang University, 2012) His work addresses methodological challenges in spatial panel data analysis, network modeling, and air pollution quantification. He has developed novel techniques for meteorological confounder adjustment in air quality assessments and contributed to distributed data analysis methods. Recent research trends include: Advancing quasi-score matching for spatial econometric models Improving subbagging algorithms for big data Creating robust distributed data aggregation frameworks Refining spatial autoregressive panel data methodologies Scientific contributions include: ANU Vice-Chancellor’s Citation for Outstanding Contribution to Student Learning (Early Career), 2022 CBE Teaching Commendation for Outstanding Teaching, 2020 Co-development of the ggmatplot R package for matrix visualization Co-inventor of Chinese patent 201811183512.0 for air quality assessment He teaches advanced courses in time series analysis, regression modeling, and mathematical statistics at ANU, while maintaining active research collaborations in econometrics and environmental statistics.
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.
Christopher John O'Donnell is a distinguished Professor at the School of Economics, University of Queensland, Australia, where he holds a dual affiliation (50% each) with both the main School of Economics and the Centre for Efficiency and Productivity Analysis (CEPA). His research primarily focuses on efficiency and productivity analysis across various sectors including agriculture, fisheries, public services, and healthcare. As a leading scholar in his field, he has published extensively in top-tier economics and operations research journals and is recognized as being among the top 5% of authors globally according to multiple citation metrics. O'Donnell's research interests span several interconnected domains: efficiency analysis, productivity measurement, agricultural economics, econometrics, state-contingent production frontiers, and metafrontier analysis. His work often bridges theoretical methodology with practical applications, particularly in estimating efficiency and productivity changes under various constraints and uncertainties. He has developed innovative approaches for measuring productivity in public service providers, agricultural sectors, and healthcare institutions, with particular attention to how weather, climate change, and demand uncertainty affect performance metrics. His research output demonstrates consistent productivity, with publications spanning from the 1990s to the present, including significant contributions in the last five years. O'Donnell frequently collaborates with researchers internationally, particularly with scholars from Australia, Europe, and Asia, reflecting the global relevance of his work. His publications appear in leading journals such as the American Journal of Agricultural Economics, Journal of Productivity Analysis, European Journal of Operational Research, and Agricultural and Applied Economics journals. Ranked among top 5% authors by citation metrics (Number of Citations) Ranked among top 5% authors by citation metrics (Number of Citations, Discounted by Citation Age) Ranked among top 5% authors by citation metrics (Number of Citations, Weighted by Number of Authors) Ranked among top 5% authors by citation metrics (Number of Citations, Weighted by Number of Authors, Discounted by Citation Age) Ranked among top 5% authors by citation metrics (Euclidian citation score) O'Donnell has supervised numerous graduate students, as evidenced by his 'Record of graduates' noted in his RePEc profile. His research has been supported by various institutions, particularly focusing on agricultural productivity, public sector efficiency, and resource economics. He has contributed significantly to methodological developments in productivity measurement, including nonparametric approaches and metafrontier frameworks that allow for cross-technology comparisons. As a core member of the Centre for Efficiency and Productivity Analysis (CEPA) at the University of Queensland, O'Donnell contributes to one of the world's leading research centers in efficiency and productivity analysis. His work has practical applications for policymakers in agriculture, fisheries management, healthcare, and public service delivery, helping organizations measure and improve their performance in increasingly complex economic environments.
Alan Huangمشاهده پروفایل
مدرس ارشد
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.
Won-Ki Seoمشاهده پروفایل
مدرس ارشد
Dr Won-Ki Seo is a Senior Lecturer in the School of Economics at the University of Sydney. His research focuses on time series analysis, econometric theory, and functional data analysis. He holds a Ph.D. in Economics from the University of California, San Diego. Research Interests: Dr Seo's work centers on cointegration analysis in functional spaces, Hilbertian processes, and the application of advanced mathematical frameworks to econometric problems. His recent studies explore tail behavior of Lévy processes, functional principal component analysis, and nonlinear time series modeling. Recent work includes analyzing stopped Lévy processes with Markov modulation and developing methodologies for functional time series inference Key contributions to cointegration theory in Banach spaces and functional data econometrics Dr Seo has published extensively in top journals like Econometric Theory and Journal of Time Series Analysis . His research bridges theoretical econometrics and practical applications in financial and environmental economics. Contact: won-ki.seo@sydney.edu.au | Office: A02 Social Sciences Building
Tao Zouمشاهده پروفایل
دانشیار
Tao Zou is an Associate Professor at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans covariance regression modeling, network data analysis, and applications in financial and environmental statistics. He earned a Ph.D. in Statistics in 2016. Ph.D. in Statistics, 2016 Dr. Zou’s work pioneers covariance regression, where covariances are modeled as functions of covariiates. Key contributions include robust estimation techniques, spatio-temporal modeling, missing data imputation via semi-supervised learning, and distributed data aggregation. His methods address challenges in high-dimensional and non-Euclidean data analysis. Recent publications (2025–2023) explore quasi-score matching for spatial autoregressive models, regularization in network regression, functional principal component analysis for complex data, and environmental applications like PM2.5 pollution studies. These works emphasize robustness, scalability, and interdisciplinary relevance in economics, finance, and environmental science. Dr. Zou collaborates on projects like the 2023 Data Analysis App to Empower Assessment of Immunogenicity of Biologics (Co-Investigator). While his student supervision list isn’t explicitly provided, his methodological advancements influence big data and spatial statistics. He contributes to open-access software and continues expanding covariance regression for non-normal and functional data.
Xiaotian Zhengمشاهده پروفایل
استادیار
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 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)
Professor Spiridon Ivanov Penev is a leading academic in the School of Mathematics and Statistics at the University of New South Wales. He holds a PhD in Mathematical Statistics from Humboldt University (Berlin, Germany) and has been affiliated with UNSW since 1992, progressing from Lecturer to Professor in 2019. His research spans wavelet methods, saddlepoint approximations, structural equation models, and stochastic risk analysis. Education: PhD in Mathematical Statistics, Humboldt University Current Affiliation: Department of Statistics, School of Mathematics and Statistics, UNSW His work focuses on advanced nonparametric techniques, including wavelet-based signal recovery with adaptive sampling rates, and robust inference in structural equation models. He has developed bias-corrected reliability measures for psychometric applications and contributed to stochastic optimization problems in finance and engineering. Recent publications highlight his expertise in semiparametric regression, robust portfolio optimization, and marine engineering applications using machine learning. Key trends include the use of Bregman divergence for shape-preserving estimation and Markov chain methods for climate model weighting. Scientific Awards: DAAD award Elected member of the International Statistical Institute (ISI) He has supervised numerous grants as Chief Investigator, including Australian Research Council projects and industry collaborations. Administrative roles include membership in the School of Mathematics and Statistics Executive Committee. Teaching duties span advanced statistical inference, multivariate analysis, and data science applications.
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.