Generalized Linear Mixed Models (GLMMs) Professors and Supervisors

Alwell Julius OyetView profile

Professor

Memorial University of NewfoundlandCanada

Alwell Julius Oyet is a Professor of Statistics in the Department of Mathematics and Statistics at Memorial University of Newfoundland. He earned his B.Sc. (1986) and M.Sc. (1992) from the Federal University of Technology, Owerri, Nigeria, and his Ph.D. (1997) from the University of Alberta, Canada. His research focuses on longitudinal data analysis, spatial statistics, time series, statistical computing, multinomial data, and robust methods. He maintains active research collaborations in these areas. Oyet teaches undergraduate and graduate courses including Design of Experiments, Time Series Analysis, Multivariate Analysis, and Computational Statistics. He has extensive experience developing course materials and statistical computing resources.

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Aishwarya BhaskaranView profile

Lecturer

Macquarie UniversityAustralia

Aishwarya Bhaskaran is a Lecturer in Theoretical Statistics at the School of Mathematical and Physical Sciences, Macquarie University. She holds a PhD in Statistics from the University of Technology Sydney (2023), followed by a postdoctoral position at Macquarie University. Her research focuses on theoretical and methodological advancements in survival analysis, generalized linear mixed models (GLMMs), asymptotic theory, and variational approximation methods. She is particularly interested in Bayesian inference and enhancing scalability for large datasets. Her teaching includes courses on Generalized Linear Models (STAT7111/8111) and Statistical Inference for Data Science (STAT3191/6191). Research interests include bridging statistical theory with real-world applications. Notable contributions span asymptotic improvements for GLMMs, dispersion parameter extensions, and variational methods for complex models. Her work emphasizes computational efficiency and practical applicability. Recent collaborations highlight international networks in statistical methodology and applications. While no specific awards are named, her research has garnered citations across peer-reviewed journals. She actively participates in academic networks and contributes to advancing statistical theory and its computational implementation.

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Minh-Ngoc TranView profile

Associate Professor

University of SydneyAustralia

Dr. Minh-Ngoc Tran is an Associate Professor in Business Analytics at the University of Sydney Business School and an Associate Investigator in the ARC Centre of Excellence for Mathematical and Statistical Frontiers. His research develops Bayesian and machine learning methods for complex models with big data. His methodological innovations include quantum-enhanced variational Bayes, manifold optimization techniques, and subsampling MCMC algorithms. Current research focuses on time series modeling for financial markets, quantum computing applications in statistics, and variational inference for models with intractable likelihoods. Dr. Tran teaches quantitative methods, data mining, and predictive analytics. He currently supervises PhD students in deep learning for financial time series, explainable AI, and quantum machine learning. Recent research grants support quantum computation in business analytics and deep learning-based financial forecasting. His work has been recognized through awards including the University of Sydney Business School Emerging Scholar Research Fellowship and designation as Australia's top researcher in Probability and Statistics with Applications in 2021.

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Aishwarya BhaskaranView profile

Lecturer

Schloss Dagstuhl - Leibniz Center for InformaticsGermany

Aishwarya Bhaskaran is a Lecturer in Theoretical Statistics at Macquarie University's School of Mathematical and Physical Sciences. She holds a PhD in Statistics from the University of Technology Sydney (Award Date: 1 August 2023) and completed a postdoctoral position at Macquarie University. Her research focuses on theoretical and methodological advancements in survival analysis, generalised linear mixed models, asymptotic theory, and variational approximation methods. She emphasizes scalability for large datasets and bridging statistical theory with real-world applications. Education: PhD in Statistics, University of Technology Sydney (2023) Research Interests: Her work spans survival analysis, generalised linear mixed models, asymptotic theory, Bayesian inference (particularly variational approximation methods), and scalable statistical methodologies. She explores applications in biostatistics, computational statistics, and mathematical modeling. Teaching: She teaches STAT7111/8111: Generalized Linear Models (S2, 2024) and STAT3191/6191: Statistical Inference for Data Science (S1/S2, 2025) . Publications: Recent work includes advancements in asymptotic theory for mixed models, survival analysis methods, and scalable Bayesian variational techniques. Her 2024 papers highlight improvements in GLMM asymptotics and penalized likelihood approaches for censored data. Collaborations: Engages in collaborations across statistical theory and methodology, with a focus on interdisciplinary applications. Active in Australia’s academic statistical community.

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David I WartonView profile

Professor

University of New South WalesAustralia

Professor David I Warton is a leading ecological statistician at the School of Mathematics and Statistics , University of New South Wales (UNSW). He leads the Eco-Stats research group , which has secured over $9M in Australian Research Council (ARC) funding and focuses on developing model-based statistical methodologies to address ecological questions. Education: PhD in Ecological Statistics (Macquarie, 2003), MA in Mathematical Statistics (Sydney, 1999), BSc (Hons) in Ecology and Pure Mathematics (Sydney, 1997) His research spans allometry , multivariate abundance analysis , species distribution modelling , and error-in-variables regression , with applications ranging from biodiversity conservation to medical studies. His recent publications emphasize AI-driven ecological data analysis , phylogenetic GLMMs , and efficient algorithm development . Scientific Awards: ARC College of Experts (2025-27), Highly Cited Researcher (2019-22), Fellow of the Royal Society of NSW (2020), Christopher Heyde Medal (2014) He supervises PhD students working on machine learning for fish classification, spatio-temporal species distribution models, and latent variable approaches in ecology. As academic lead of Stats Central , he promotes statistical collaboration across disciplines, and serves as Associate Editor for leading journals.

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Alan WelshView profile

Professor

Australian National University (ANU)Australia

Alan Welsh is the E.J. Hannan Professor of Statistics at the Australian National University (ANU), affiliated with the Research School of Finance, Actuarial Studies & Statistics. He holds prestigious fellowships from the Australian Academy of Science, Institute of Mathematical Statistics, and American Statistical Association. His research focuses on statistical inference, mixed models, robustness, nonparametric methods, ecological monitoring, and survey analysis. Education: PhD in Statistics (ANU, 1984) and BSc (Hons) in Mathematical Statistics (University of Sydney, 1981). Research interests emphasize advanced statistical methodologies with applications in ecology, environmental science, and healthcare. He has authored over 176 publications and secured funding through grants like 'Analytics for the Australian Grains Industry' (2024–2027) and 'Statistical Machine Learning' (2019–2024). His work spans high-dimensional data analysis, copula-enhanced models for medical imaging (e.g., myopia screening via fundus images), and spatial-temporal modeling of environmental data. He has also contributed to statistical software, notably the 'rpql' and 'mplot' R packages. Awards: Moran Medal (1990), Pitman Medal (2012), E.J. Hannan Medal (2019), and Honorary Life Membership (International Biometric Society, 2018). Welsh has advised numerous research students and served as Editor-in-Chief of the Australian and New Zealand Journal of Statistics (2012–2015) and Co-Editor of Biometrics (2020–2022). His current projects address challenges in big data analysis, robust estimation, and model selection for complex datasets.

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Marina PtukhinaView profile

Associate Professor

Whitman CollegeUnited States

Marina Ptukhina is an Associate Professor of Statistics at Whitman College, where she joined the faculty in 2016. She has played a pivotal role in developing the Math-Stats combined major and the Data Science minor at the institution. Education: Ph.D. in Statistics, University of Nebraska-Lincoln M.S. in Mathematics, Texas Tech University Specialist degree in Management, National Technical University "Kharkiv Polytechnic Institute" Dr. Ptukhina's research spans statistical modeling, design and analysis of research studies, and their diverse applications. Her scholarly work bridges theoretical statistics with practical implementations across economics, biostatistics, and statistical education. She has made significant contributions through her research publications and co-authorship of the influential 2024 textbook "Generalized Linear Mixed Models: Modern Concepts, Methods and Applications" (2nd edition), which has become a key resource in the field. Her publication record demonstrates a clear progression toward increasingly sophisticated statistical methodologies, with her recent work focusing on Bayesian implementations of GLMMs, smoothing splines, correlated errors analysis, and precision planning for research studies. The 2024 textbook represents the culmination of this trajectory, offering comprehensive coverage of both theoretical foundations and practical applications. Teaching: MATH 128: Introduction to Statistics MATH/CS 215: Introduction to Data Science MATH 247: Statistics with Applications MATH 248: Statistical Modeling MATH 347: Design and Analysis of Research Studies MATH 349: Probability Theory MATH 438: Statistical Theory GENS 176: Making Powerful Arguments: Data, Decisions, and Equity Born and raised in Kharkiv, Ukraine, Dr. Ptukhina brings an international perspective to her academic work, enriching both her research approach and classroom instruction.

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Rodrigo LabouriauView profile

Associate Professor

Aarhus UniversityDenmark

Dr. Rodrigo Labouriau is an Associate Professor at the Department of Mathematics, Aarhus University, within the Faculty of Natural Sciences. He leads the Applied Statistics Laboratory (aStatLab), focusing on statistical modeling and consultancy for natural and technological sciences, particularly biology, agriculture, and environmental sciences. Research Focus : Generalized linear mixed models (GLMMs), graphical models, and their multivariate/semiparametric extensions for complex data analysis. Teaching : Courses like 'Study Design and Analysis' for Master’s students, and PhD-level courses in R programming, generalized linear models, and mixed models. Consultancy : Provides free statistical support to Faculty of Natural Sciences members through ad hoc projects, thesis supervision, and daily consultations. Research Trends : His work bridges statistical theory with applied solutions for environmental monitoring (e.g., ammonia emissions), agricultural productivity (e.g., intercropping systems), and biomedical challenges (e.g., malaria detection via DNA sensors). Projects often involve interdisciplinary collaborations in agricultural, environmental, and health sciences. Key Projects : NATEF (2020–2023), MAG (2019–2025), and others tackling soil mechanics, aviation de-icing, and bull fertility.

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Luca MaestriniView profile

Lecturer

Australian National University (ANU)Australia

Dr. Luca Maestrini is a Lecturer of Statistics at the Research School of Finance, Actuarial Studies & Statistics at the Australian National University (ANU), Canberra. His research focuses on methodological and computational statistics, with expertise in variational approximations, generalized linear mixed models (GLMMs), and statistical theory. He previously held postdoctoral positions at ANU (2022–2024) under Prof. Francis Hui and Prof. Alan Welsh, and at the University of Technology Sydney (2018–2022) under Prof. Matt Wand. He completed his PhD in Statistics at the University of Padova, Italy (2015–2019), studying under Prof. Nicola Sartori and co-supervisors Prof. Alessandra Salvan and Prof. Matt Wand. He also served as a Research Fellow at the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research interests span computational statistics, Bayesian inference, and applications in forensic science, ecology, and finance. Notable collaborations include work on inverse problems, multivariate abundance data analysis, and stochastic variational inference for GARCH models. He currently chairs the Australian Capital Territory branch of the Statistical Society of Australia. Key research trends in his articles include advancements in variational inference frameworks, asymptotic improvements to GLMMs, and statistical methods for postmortem interval estimation using lipid degradation biomarkers. His work bridges theoretical developments with practical applications in diverse fields. He has advised students through ANU’s research programs and contributed to ACEMS initiatives. His lab focuses on computational methods and applied statistical modeling, emphasizing interdisciplinary collaboration.

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Charles J GeyerView profile

Professor

University of Minnesota Twin CitiesUnited States

Charles J Geyer is a Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. He has been an active researcher since at least 1988, with a sustained record of scholarly output in statistical theory and methodology. His research focuses on advanced statistical methods including maximum likelihood estimation, exponential families, Markov Chain Monte Carlo (MCMC), likelihood-free inference, and aster models. These methods are applied in interdisciplinary contexts such as evolutionary biology, genetics, and ecological modeling, particularly in life history analysis and phenotypic selection. His work bridges theoretical statistics with practical computational tools for complex data. The recent publications highlight a strong trend toward computationally efficient inference, especially in models where traditional maximum likelihood fails. He has contributed to the development of the R package glmm for generalized linear mixed models and has worked extensively on envelope methods and variance reduction techniques. His research outputs include numerous peer-reviewed articles, book chapters, and publicly shared datasets, reflecting a commitment to open science. Scientific contributions include: Development of MCMC methods for dependent data Foundational work on likelihood inference when MLE does not exist Integration of aster models with envelope methodology Applications in evolutionary and ecological statistics He has collaborated with researchers such as D. J. Eck, R. G. Shaw, and R. D. Cook. While formal advisee relationships are not listed, his collaborative work suggests mentorship and academic leadership. He has not received any explicitly mentioned awards in the provided text, but his sustained impact is evident through citations and methodological influence. His datasets are archived in the University of Minnesota Data Repository, supporting reproducible research.

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David AschView profile

Professor

University of PennsylvaniaUnited States

David Asch is the Senior Vice President for Strategic Initiatives at the University of Pennsylvania and holds the John Morgan Professorship at both the Perelman School of Medicine and the Wharton School. His academic roles span multiple disciplines, including Medicine , Medical Ethics and Health Policy , Health Care Management , and Operations, Information, and Decisions . Education : MD from Cornell University (1984), MBA from the Wharton School (1989), AB from Harvard University (1980) Dr. Asch’s research focuses on behavioral economics , exploring how clinicians and patients make medical choices , with implications for health care management and policy innovation . His work integrates technology assessment and decision support systems to improve health outcomes. His recent publications emphasize privacy in digital health , remote monitoring , machine learning applications , and equity in health care delivery , reflecting his commitment to translating evidence into practice through design thinking and innovation methodologies . Scientific Awards : Ken Shine Prize in Health Leadership (2022), Distinguished Investigator Award (2019), Elected Member, Institute of Medicine (2007), RWJF David E. Rogers Award (2018), and over 20 additional honors for teaching, research, and mentorship. Dr. Asch leads the Center for Health Care Innovation and has directed programs like the Leonard Davis Institute of Health Economics (1998-2012) and the Robert Wood Johnson Foundation Clinical Scholars Program (2013-2017). His teaching includes courses on health care innovation and thesis mentorship in health policy research.

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