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Erniel Bayhon BarriosView profile

Professor

• Computational Statistics
• Nonparametric Methods
• Data Science/Big Data Analytics
• +5 more

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.

• Applied Statistics
• Big Data
• Economic Models and Forecasting
• +2 more

Prof Terry O'Neill serves as Executive Dean at the Bond Business School , Bond University, while also holding the title of Professor and Director at the Centre for Data Analytics. His research spans applied statistics , big data analytics , and financial modeling , with over 80 publications (20% in A* journals). Grants: Lead investigator on five ARC Discovery/Linkage grants totaling $2.99M, including studies on retirement savings, climate change modeling, and financial crisis resilience. Research Trends: Focus on financial literacy , retirement economics , and myelopoiesis , bridging finance with computational biology through collaborations with his spouse Helen O'Neill. Academic Leadership: Established Bond University's University Centre in Actuarial and Financial Big Data Analytics to elevate institutional research prominence in data science.

Xuan LiangView profile

Lecturer

• Spatial Modelling
• Panel Data
• Nonparametric Statistics
• +3 more

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.

Seojeong LeeView profile

Associate Professor

• Econometrics
• Statistical Inference
• Generalized Method of Moments (GMM)
• +8 more

Associate Professor Seojeong Lee is a faculty member at the University of New South Wales (UNSW) Business School, School of Economics, specializing in advanced econometric theory. She joined UNSW in 2012 after completing her PhD at the University of Wisconsin-Madison and has established herself as a leading researcher in robust inference methods under complex data conditions. Her educational background includes: Ph.D. in Economics, University of Wisconsin-Madison (2008-2012) M.A. in Economics, Seoul National University (2006-2008) B.A. in Economics and Political Science (dual major), Seoul National University, summa cum laude (2000-2006, with military service 2002-2004) Professor Lee's research centers on developing theoretically rigorous methods for econometric inference, with primary focus on generalized method of moments (GMM), instrumental variables (IV), and two-stage least squares (2SLS) under model misspecification. Her work addresses critical challenges including invalid/many/weak instruments, heterogeneous treatment effects, and clustered sampling, contributing foundational advances to statistical inference in economics. Analysis of her recent publications reveals a strong trajectory in refining methods for many-instrument settings and misspecified models, with increasing emphasis on computational implementations (e.g., Stata packages) and applications to causal inference. Her work bridges theoretical econometrics with practical policy-relevant analysis. Her scientific achievements include: Australian Research Council DECRA Fellowship (2017-2019) UNSW Dean's Research Fellowship (2020-2022) Zellner Thesis Award Honorable Mention from American Statistical Association (2014) Multiple competitive UNSW research awards Professor Lee actively supervises PhD candidates Wei Tian and Fangzhou Yu, and has secured over AUD 700,000 in research funding including ARC Discovery Projects. She teaches undergraduate and postgraduate econometrics courses, integrating her research into pedagogy. Her ongoing work continues to push boundaries in robust econometric methodology for modern data challenges.

Andrea CollevecchioView profile

Professor

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

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.

• Data Envelopment Analysis
• Stochastic Frontier Analysis
• Productivity and Efficiency Analysis
• +4 more

Kai Du is a Lecturer at the School of Business within the Faculty of Business and Law at the University of Wollongong. He holds a PhD in Economics from the University of Adelaide (2014) and served as a UQ Development Fellow at the University of Queensland (2019–2022). His research focuses on productivity and efficiency analysis, particularly through data envelopment analysis and stochastic frontier analysis, addressing challenges in healthcare systems, sustainable economic growth, and banking sector efficiency. He is a Higher Education Academy (HEA) Fellow and member of WATTLE (Wollongong Academy for Tertiary Teaching and Learning Excellence), emphasizing inclusive education and contributing to the UOW Ally Network for LGBTIAQ+ support. Recent funding includes Targeted Researcher Support grants from the University of Wollongong (2023–2024). Key research interests include applying quantitative methods to assess efficiency in diverse sectors, such as agriculture, healthcare, and finance. His work bridges econometrics with real-world policy implications, particularly in emerging economies. Teaching expertise spans a decade, with courses emphasizing technical and analytical skills. Education: PhD in Economics, University of Adelaide, Australia (2014) Languages: English (Peer Review Competency), Chinese (Mandarin) (Peer Review Competency) SDGs: 9 (Industry, Innovation & Infrastructure), 11 (Sustainable Cities & Communities), 13 (Climate Action) Recent research outputs include advancements in panel data methodologies for efficiency measurement and analysis of climate change impacts on agricultural systems. His 2025 article in the European Journal of Operational Research proposes a likelihood-ratio test for technological differences in two-stage DEA, while 2024 work explores climate mitigation's role in agricultural efficiency in China.

• Econometrics
• Time Series Analysis
• Financial Econometrics
• +3 more

Ye Lu is a Senior Lecturer in the School of Economics at the University of Sydney. She holds a PhD in Economics from Indiana University Bloomington (2017). Her research focuses on econometric theory with applications to time series analysis, financial econometrics, and large-dimensional data. Key interests include continuous time modelling with high-frequency data, nonlinear factor models, and econometric methods for event-driven data. Education: PhD in Economics, Indiana University Bloomington (2017) Research emphasizes developing robust methodologies for data-rich environments, addressing challenges in traditional econometric approaches. Recent work includes bootstrap inference for Hawkes processes and zero-inflated GARX models for energy price spikes. Teaching responsibilities include courses like ECMT1020 (Introduction to Econometrics) and ECOS3904 (Applied Macroeconometrics). Her publications appear in top journals such as Journal of Econometrics and Energy Economics .

• Probability Theory
• Stochastic Processes
• Ergodic Theory
• +2 more

Meng Shi is a Teaching Professor at the School of Mathematics, Monash University. Their research focuses on stochastic processes, probability theory, and ergodic systems, with notable contributions to bootstrap random walks and limit theorems. Two key publications include work on ergodicity in bootstrap random walks (2022) and foundational studies on bootstrap random walks (2016). Research interests emphasize theoretical and applied aspects of stochastic analysis, with applications in mathematical modeling. No scientific awards or grants are explicitly listed. While no student advisees are mentioned, their work engages with collaborative networks in probability and stochastic processes.

Bin PengView profile

Professor

• Econometrics
• Panel Data Analysis
• Time Series Analysis
• +3 more

Bin Peng is a Professor in the Department of Econometrics and Business Statistics at Monash University. His research focuses on developing novel econometric models and methods, particularly in panel data analysis, time series econometrics, and climate data modeling. He holds a PhD in Econometrics from Monash University (2013) under Professors Giovanni Forchini and Don Poskitt, preceded by a BSc in Mathematics from Nanjing University (2007). His work addresses structural changes in factor models, time-varying parameters in vector error-correction frameworks, and productivity convergence in manufacturing sectors. Key contributions include nonparametric panel models for climate data and methodologies for handling interactive effects in panel data with general factors. Peng has received multiple Dean’s Awards, including the 2021 Early Career Research Excellence Award, 2023 Commendation for Excellence, and 2024 Researcher of the Year. He leads a 2021–2025 project on modeling time trends in panel data, funded by Monash University. His recent articles (2021–2025) emphasize methodological advancements in econometric theory, applied to climate science, economic growth, and macroeconomic policy.

Anton WestveldView profile

Senior Lecturer

• Bayesian Methodology
• Network Analysis
• Game Theoretic Data
• +3 more

Anton Westveld is a Senior Lecturer in the Department of Statistics at the Australian National University (ANU), within the Research School of Finance, Actuarial Studies & Statistics (RSFAS). He also serves as an Affiliate Associate Professor at Virginia Commonwealth University since August 2023. His research focuses on Bayesian methodology, network analysis, game theoretic data, and statistical causality, with notable contributions to ecological modeling and agent-based stochastic simulations. Westveld holds a Bachelor’s in Economics and Political Science from the University of Michigan (Ann Arbor), a Master’s in Applied Economics and Statistics from the same institution, and a PhD in Statistics from the University of Washington. His work has been published in prestigious journals like the Annals of Applied Statistics and Proceedings of the National Academy of Sciences . His research interests span Bayesian inference, relational data analysis, and causal modeling, with applications in ecological and health sciences. Recent work includes developing Bayesian methods for ecological drivers in marine viral communities and latent socioeconomic health indices for policy evaluation. Notable articles include analyses of menstrual disorder surveys using Gaussian copulas, ecological metagenomics studies, and Bayesian-optimized bootstrap techniques for uncertainty quantification. His interdisciplinary collaborations bridge statistics with environmental science, public health, and economics.

Shovanur HaqueView profile

Lecturer

• Bayesian Statistical Modelling
• Spatial Data Analysis
• Machine Learning
• +3 more

Dr. Shovanur Haque is an Associate Lecturer in the School of Mathematical Sciences and a Postdoctoral Research Fellow in the School of Public Health & Social Work at Queensland University of Technology (QUT). She holds a PhD in Statistics (2018) from QUT, focusing on developing the MaCSim method for evaluating record linkage accuracy. Her work spans academia and government, including roles at the Australian Bureau of Statistics and Queensland Department of Environment and Science. Currently, she leads a Centre for Data Science (CDS) project investigating spatial data aggregation effects on medical decision-making. Her research interests include Bayesian modeling, spatial data analysis, machine learning, and record linkage. Key research projects involve assessing Modifiable Areal Unit Problem (MAUP) impacts on epidemiological inference, modeling infectious disease transmission dynamics, and developing climate-driven early warning systems for public health. She contributes to interdisciplinary collaborations, combining statistical methods with health, environmental, and social sciences. Education: PhD in Statistics (QUT, 2018) Research Focus: Spatial epidemiology, Bayesian methods, record linkage, and MAUP analysis Affiliations: Centre for Data Science, Centre for Immunology and Infection Control Her recent publications (2021–2025) emphasize Bayesian structural time series modeling, spatial aggregation effects in health data, and cross-border disease surveillance networks. She actively engages in methodological advancements for public health analytics and data-driven policy decisions.

Steven RobertsView profile

Professor

• Applied Statistics
• Environmental Statistics
• Statistical Epidemiology
• +6 more

Professor Steven Roberts serves as Dean of the College of Business and Economics at the Australian National University (ANU). He previously held the role of Director at the Research School of Finance, Actuarial Studies & Statistics and has taught Actuarial Studies and Statistics courses within the university. Education: PhD in Statistics from Stanford University Masters in Statistics from Stanford University Research Interests : Steven specializes in Applied Statistics , with a focus on Lasso Regression for high-dimensional data analysis and Time Series Analysis in financial and environmental contexts. His work addresses Model Selection challenges and develops Re-sampling Methods for statistical robustness. Research Trends : His publications demonstrate interdisciplinary applications in Finance (portfolio theory, health insurance economics), Statistics (regression techniques, influence diagnostics), and Public Health (statistical epidemiology). Recent work explores Retirement Expenditure patterns and Actuarial Modelling . Collaborative Projects : Steven has contributed to key initiatives including: Min/Max Autocorrelation Factors in Time Series Studies of Air Pollution Health Effects (2014-2018) Retirement Savings Expenditure Modelling (2014) Bootstrap Methods for Evolutionary Kernel Time-Series Modelling (2009-2010) Air Pollution and Statistical Model Selection (2008-2011)

• Sample Survey Design and Analysis
• Robust Statistical Methods
• Graphical Methods in Statistics
• +4 more

Professor Raymond Chambers is a distinguished statistician currently serving as an Honorary Fellow at the School of Mathematics and Applied Statistics, Faculty of Engineering and Information Sciences, University of Wollongong (2022–present). His career spans leadership roles at the University of Southampton (2003–2006 as Director, Southampton Statistical Sciences Research Institute; 1995–2000 as Professor and Head of Department of Social Statistics; 1999–2000 as Leverhulme Professor) and the Australian National University (1989–1995 as Senior Lecturer in Statistics). He completed his PhD in Biostatistics at Johns Hopkins University (1979–1983) and has supervised 9 PhD students in areas like Small Area Estimation , Robust Inference , and Survey Methodology . His research focuses on Sample Survey Design and Analysis , Robust Statistical Methods , Statistical Modelling and Inference , and Analysis of Computer-Linked Data . Recent publications emphasize Small Area Estimation , Probabilistic Data Linkage , and Non-Linear Spatial Models , reflecting his expertise in integrating survey data with advanced statistical techniques. Professor Chambers has received multiple accolades, including Elected Member of the International Statistical Institute Elected Fellow of the American Statistical Association Elected Fellow of the Academy of Social Sciences in Australia Leadership roles as President of the International Association of Survey Statisticians (2011–2013) He has secured significant funding from Australian and international agencies, including Discovery Projects and Linkage International grants, supporting research in Small Group Analysis , Missing Data Handling , and Longitudinal Survey Methodology . His professional contributions include editorial roles at the Journal of the Royal Statistical Society and The Annals of Statistics .

• Machine Learning
• Cybersecurity
• Biometric Authentication
• +3 more

Dr Wencheng Yang is a Senior Lecturer in Computing at the School of Mathematics, Physics and Computing , University of Southern Queensland. He holds a PhD in Computing from UNSW, an MSc from Korea, and a BMgmt from Wuhan University of Technology. His research spans multiple domains including machine learning security , biometric authentication , privacy-preserving systems , and IoT applications . Education : BMgmt (Wuhan University of Technology), MSc (Korea), PhD (UNSW) Yang’s work emphasizes privacy and security in AI systems, particularly in biometric authentication (e.g., fingerprint, face, ECG) and IoT security. His recent publications focus on model inversion attacks , homomorphic encryption , and federated learning for healthcare applications. Key trends include secure face-swapping , Alzheimer’s disease prediction , and anti-forensic detection . His research has been cited 3378 times, with 1458 total downloads and 31 monthly views. While no explicit scientific awards are listed, his interdisciplinary work bridges computer science , healthcare , and cryptography . Current projects include privacy-preserving frameworks for implantable medical devices and military health systems .

• Constrained Statistical Inference
• GARCH Models
• Extreme Value Theory
• +4 more

Mervyn J. Silvapulle is a Professor in the Department of Econometrics and Business Statistics at Monash University. He holds a B.Sc.(Honours) in Mathematics and a Ph.D. in Statistics from the Australian National University (ANU), awarded in 1982. His research focuses on econometric and statistical methodology, with current interests in constrained statistical inference, GARCH models, and extreme value theory. Past research includes robust statistics, generalized linear models, ridge regression, and minimum distance methods of inference. Education: B.Sc. (Honours) in Mathematics, Ph.D. in Statistics (ANU). Editorial Roles: Editor (Theory and Methods) of the Australian and New Zealand Journal of Statistics, guest editor for special issues on constrained statistical inference, and associate editor for multiple journals, including the Journal of Statistical Planning and Inference and Sankhya. His research spans collaborations with institutions like the Australian Research Council (ARC), Universität St. Gallen, and the European Centre for Advanced Research in Economics and Statistics. He has led or co-investigated projects such as 'Robust methods for heteroscedastic regression models for time series' (2015–2022) and 'Non-parametric estimation of forecast distributions in non-Gaussian state space models' (2009–2012). Grants & Advising: Accepting PhD students since 1995, with a history of securing ARC-funded projects.