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Glen McGeeView profile

Assistant Professor

• Biostatistics
• Epidemiology
• Environmental Health
• +3 more

Glen McGee is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a PhD in Biostatistics from Harvard University and a BScH in Mathematics from Queen's University. His research focuses on developing statistical tools for epidemiology, environmental health, and health policy, with a particular emphasis on environmental mixture analysis, cluster-correlated data modeling, and outcome-dependent sampling methodologies. Education : PhD in Biostatistics, Harvard University BScH in Mathematics, Queen's University Research Interests : McGee advances methodologies for analyzing complex environmental mixtures and their health impacts, including incorporation of biological knowledge into statistical frameworks. His work addresses challenges in multigenerational studies, informative cluster sizes, and measurement error correction in case-crossover designs. Key application areas include hospital profiling, exposure misclassification, and longitudinal health data analysis. Research Trends : His publications emphasize Bayesian methods for mixture modeling, innovative sampling strategies for clustered data, and causal inference techniques. Notable contributions include frameworks for integrating biological pathways into environmental health analyses and developing efficient sampling approaches for healthcare performance evaluation. Grants & Labs : McGee collaborates on projects involving CMS data applications and maintains GitHub repositories like hospODS for hospital profiling methodology implementation. His work bridges statistical theory with practical public health applications, particularly in environmental epidemiology and healthcare analytics.

• Microsimulations
• Longitudinal analyses
• Health and migration sociology
• +1 more

Christoph Frohn is a Lecturer and Research Assistant at the Institute of Sociology , University of Duisburg-Essen, with a focus on empirical social research, longitudinal analysis, and health/migration sociology. Education: Doctorate (2024), University of Duisburg-Essen Master of Arts in Sociology (2017), University of Duisburg-Essen Bachelor of Science in Sociology and Philosophy (2014), University of Trier His research integrates microsimulation techniques with health inequality analysis, particularly examining physical/mental health interplay, migrant health disparities, and generational differences in care expectations. He frequently employs autoregressive latent trajectory models and longitudinal data in his work. Publications and conference presentations (2019-2025) demonstrate expertise in dynamic microsimulation of social dynamics , with applications to migrant pay gaps, health stratification, and caregiving conflicts. Recent lectures at international conferences (e.g., Joint Statistical Meeting 2024) highlight methodological innovations in panel data analysis.

• Bayesian Statistics
• Expert Knowledge Elicitation
• Robust Analyses
• +3 more

Professor John Paul Gosling is an applied statistician affiliated with the Department of Mathematical Sciences at Durham University . With a background in industry, academia, and the civil service, his work bridges statistical theory and real-world applications, particularly in criminal justice, toxicology, and climate science. Education: MMath in Mathematics (University of York, 1998–2002), PhD in Statistics (University of Sheffield, 2002–2005) Current Role: Professor in Statistics, focusing on human interaction with statistical information His research interests include: Bayesian Statistics : Developing models for complex systems Expert Knowledge Elicitation : Structuring expert input for risk assessment Uncertainty Quantification : Analyzing and communicating uncertainty in data Machine Learning Integration : Combining statistical inference with AI for decision-making Key trends from his recent articles include: Application of Voronoi Tessellations in Bayesian regression and classification Development of emulation techniques for computationally intensive models Focus on legal and public health domains Integration of copula methods for dependent variables He actively supervises PhD students in Bayesian theory and machine learning interfaces and contributes to Statistics research at Durham University.

• High-Dimensional Statistics
• Portfolio Optimization
• Bayesian Analysis
• +3 more

Taras Bodnar is a Professor at the Department of Management and Engineering, Linköping University. His research focuses on high-dimensional statistical methodologies with applications in finance, portfolio optimization, and econometrics. He specializes in developing and analyzing advanced statistical models for asset allocation, risk management, and multivariate meta-analysis. Bodnar's work often involves Bayesian methods, shrinkage estimation techniques, and copula modeling to address challenges in financial data analysis. His recent contributions include the HDShOP package for portfolio selection and advancements in nonlinear shrinkage tests for large-dimensional covariance matrices. His research bridges theoretical statistics with practical financial applications, addressing issues such as dark uncertainty and efficient frontier estimation in high-dimensional settings. Key research interests include: High-Dimensional Portfolio Optimization Bayesian Analysis in Financial Contexts Covariance Matrix Estimation and Testing Uncertainty Quantification in Multivariate Analyses Statistical Software Development for Finance Recent publications (2024-2025) emphasize methodological innovations in portfolio selection, copula modeling, and robust statistical inference. His work has implications for both academic theory and practical investment strategies, particularly in managing large and complex financial datasets.

• Biomechanics
• Exercise Physiology
• Machine Learning
• +7 more

Dylan Kobsar is an Associate Professor in the Department of Kinesiology at McMaster University . His research focuses on making biomechanics and human movement analyses more accessible and real-world relevant through wearable inertial sensor technology. Primary Research Areas Biomechanics Exercise Physiology Machine Learning Rehabilitation Sciences His work has been funded by the Labarge Catalyst Grant in Mobility in Aging , Ontario Research Fund , and Early Researcher Awards Program . He develops machine learning models for clinical gait analysis and musculoskeletal disorder tracking. Recent Scientific Contributions Markerless motion capture validation Wearable sensor-based osteoarthritis monitoring Biomechanical asymmetry detection in athletes Accelerometer data for frailty assessment Pain phenotype analysis in arthritis Dylan teaches courses including: Advances in Biomechanics and Human Movement Analyses Scientific Computing and Wearable Technology Neural Control of Human Movement

Xiaofeng ZhuView profile

Professor

• Genetic Epidemiology
• Statistical Genetics
• Genetic Association Analysis
• +5 more

Xiaofeng Zhu, PhD is a Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine. He also serves as faculty at the Cleveland Institute for Computational Biology. His research focuses on developing statistical methods and computational tools for analyzing genetic and genomic data, particularly in the context of association analysis, admixture mapping, and rare variant identification. He leads the Continental Origins and Genetic Epidemiology Network (COGENT) consortium and has identified multiple genetic loci associated with blood pressure, sleep disorders, and Alzheimer's disease. Education: Ph.D. in Biostatistics, Epidemiology and Biostatistics, Case Western Reserve University (1998) M.S. in Statistics, University of Cincinnati (1994) M.S. and B.Sc. in Mathematics, Peking University (1989, 1986) Research Interests: Dr. Zhu's work emphasizes statistical genetics, including the development of methods to control population stratification, admixture mapping in admixed populations, and cross-phenotype association analyses. His lab integrates deep learning and bioinformatics to study complex traits such as cardiovascular disease, obesity, and sleep disorders. Recent efforts include analyzing whole-genome sequencing data to uncover rare variants influencing phenotypic variation. Publications Trends: His recent work spans Mendelian randomization methods (e.g., MRBEE), gene-sleep interaction studies, and multi-ancestry genomic analyses. Key themes include causal inference, pleiotropy analysis, and leveraging diverse genetic datasets to improve polygenic risk scores. Awards & Memberships: Fellow of the Royal Statistical Society Member, American Society of Human Genetics Member, International Genetic Epidemiology Society Advising & Grants: He has mentored 3 Master’s students, 9/10 PhD graduates, and 11/12 postdoctoral researchers. Notable mentees hold positions at Harvard, Mayo Clinic, and NIH. Active grants include studies on sleep health metrics, lipid loci discovery, and cardiovascular disease genetics. Labs & Teams: Leads COGENT consortium and collaborates with the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. His lab develops software tools for ancestry inference, rare variant analysis, and epigenetic association studies.

• Biostatistics
• Survival Analysis
• Pharmacoepidemiology
• +5 more

Michal Abrahamowicz, PhD is a Senior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) and Professor in the Department of Epidemiology, Biostatistics and Occupational Health at McGill University's Faculty of Medicine and Health Sciences. He is affiliated with the Cardiovascular Health Across the Lifespan Program and the Centre for Outcomes Research and Evaluation (CORE) at RI-MUHC. His research focuses on developing innovative statistical methodologies for clinical and biomedical data analysis, particularly in survival analysis. Key areas include modeling time-varying and cumulative effects of risk factors and treatments, and addressing biases in observational studies. He co-founded and co-chairs the international STRATOS initiative (www.stratos-initiative.org), involving over 100 statisticians from 18 countries, aimed at improving analyses of observational studies. From 2011-2019, he served as Principal Investigator of the CAN-AIM network, funded by the Canadian Institutes for Health Research, which brought together over 45 faculty members from 12 Canadian universities for drug safety and effectiveness research. His recent publications demonstrate a strong focus on methodological advances in biostatistics, particularly in survival analysis, causal inference, and flexible modeling techniques. His work spans applications in cardiovascular diseases, cancer epidemiology, pharmacoepidemiology, and arthritis research, with an increasing emphasis on practical implementation of statistical methods in clinical research. Abrahamowicz leads major collaborative research initiatives including large clinical trials and longitudinal population-based studies. His research methodology work directly informs applications in real-world clinical and epidemiological studies, creating a strong bridge between theoretical statistics and practical healthcare research.

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.

Daniela BeisserView profile

Professor

• Bioinformatics
• Computational Biology
• Microbial Ecology
• +6 more

Prof. Dr. Daniela Beisser is a Professor at the Department of Engineering and Natural Sciences (FB 8) of the Westphalian University of Applied Sciences in Recklinghausen, Germany. Her research focuses on bioinformatics and biostatistical methods for high-throughput 'omics data, applied to biomedicine and freshwater ecology. She previously held academic roles at the University of Duisburg-Essen (2017–2023) and University Hospital Essen. 2004–2008: B.Sc. in Molecular Biology with Bioinformatics focus, FH Gelsenkirchen 2006–2008: M.Sc. in Molecular Biology with Bioinformatics focus, FH Gelsenkirchen 2008–2011: Ph.D. in Bioinformatics, University of Würzburg Her research integrates computational approaches with experimental data to study molecular responses to environmental stressors in freshwater organisms, genome analyses in human and protists, and proteomic studies in plants. She also investigates eco-evolutionary theories in microorganisms and links biodiversity to ecosystem functions. Recent publications highlight her work on amplicon sequencing (Natrix2 pipeline), metatranscriptomic analysis of microbial communities, and machine learning frameworks for environmental data. She contributes to software tools like TaxMapper and BioNet for reproducible workflows. Best Poster Award, German Conference on Bioinformatics (2013) Travel scholarships: DAAD, DAAD PROMOS, German Symposium on Systems Biology E-fellows.net scholarship (2006–2008) She has supervised numerous PhD, Master’s, and Bachelor’s students on topics such as protist community dynamics , fungal degradation processes , and stressor recovery mechanisms . Her lab collaborates on the CRC 1439 'RESIST' project and develops tools for environmental DNA analysis.

Politis KonstantinosView profile

Professor

• Stochastic developments
• Risk and bankruptcy theory
• Actuarial mathematics
• +3 more

Professor Konstantinos Politis is a distinguished academic in the Department of Statistics and Actuarial Science at the University of Piraeus, where he has served since 2004, progressing from Assistant Professor to his current position as Professor (2025-present). His academic career spans prestigious institutions including the University of Manchester, University of Southampton, and the University of Cambridge where he earned his PhD. Education: B.A. in Mathematics, University of Athens (1989) MSc in Statistics, University of Sheffield, UK (1991) PhD in Statistics, University of Cambridge, UK (1997) Professor Politis specializes in stochastic processes with particular focus on ruin theory, risk analysis, and actuarial mathematics. His research examines complex probability models related to insurance risk, surplus processes, renewal theory, and failure rate analysis. He has made significant contributions to understanding the mathematical properties of insurance risk models, particularly in the Sparre Andersen framework and compound Poisson processes. His work bridges theoretical probability with practical applications in actuarial science and financial risk management. His extensive publication record demonstrates consistent contributions to leading journals in probability and actuarial science. The research trajectory shows evolution from foundational work on ruin theory and risk models toward more sophisticated analyses of renewal processes, failure rate functions, and stochastic bounds. His recent work focuses on precise mathematical characterizations of recurrence times, convolution properties, and monotonicity in stochastic systems. Professor Politis has co-authored significant textbooks including Introduction to Probability: Models and Applications (2019) and Introduction to Probability: Multivariate Models and Applications (2021) with Balakrishnan and Koutras, as well as his Greek-language work Introduction to the Theory of Collective Risk (2012, 2nd ed. 2016). In teaching, Professor Politis has contributed to both undergraduate and graduate education, offering courses including Probability I, Special Topics in Probability, Variance Analysis, Loss Distributions, Generalized Linear Models, and Risk Theory II. His academic journey reflects a deep commitment to advancing statistical theory while maintaining practical relevance to actuarial science and risk management.

Benedikt EhingerView profile

Assistant Professor

• Computational Cognitive Science
• Visual Cognitive Science
• EEG Analysis
• +5 more

Benedikt Ehinger is a Tenure-Track Professor for Computational Cognitive Science at the Stuttgart Center for Simulation Science (SC SimTech) and the Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart. His research bridges cognitive neuroscience, computational modeling, and visualization techniques to understand visual perception and decision-making processes. Education 2018: PhD in Cognitive Science from University of Osnabrück with thesis "Predictions, Decisions and Learning in the visual sense" 2013: Master of Science in Cognitive Science from University of Osnabrück with thesis "Filling in Blind-Spots: A psychophysical and an EEG study" 2011: Bachelor of Science in Cognitive Science from University of Osnabrück with thesis "Electrophysiological Correlates of Category Learning" Research Interests Ehinger's research focuses on the intersection of visual cognitive science, computational modeling, and neuroimaging techniques. His work primarily investigates predictive coding mechanisms in visual perception, statistical learning in visual scenes, eye movement control, method development for combined EEG and eye-tracking analyses, visual completion phenomena like the blind spot, and category learning and neural plasticity. His approach combines behavioral experiments, EEG recordings, eye-tracking, and advanced statistical modeling to uncover the computational principles underlying human visual cognition. Publication Trends Ehinger's publication record shows a clear evolution from foundational work on visual perception and category learning toward methodological innovations in neuroimaging analysis. His early work focused on visual completion phenomena, category learning, and melanopsin modeling. More recently, he has pioneered techniques for analyzing combined EEG and eye-tracking data, developing toolboxes like "unfold" that address critical challenges in temporal overlap correction and regression-based analysis. His research demonstrates a consistent thread of applying computational approaches to understand visual cognition while simultaneously advancing the methodological toolkit of cognitive neuroscience. Scientific Contributions Development of the "unfold" toolbox for overlap correction and regression-based EEG analysis Creation of the EEGVIS toolbox for EEG visualization Establishment of comprehensive eye-tracking test batteries for validating mobile eye-tracking devices Innovative approaches to modeling fixation durations and eye movement patterns Research Environment Ehinger leads the Computational Cognitive Science group within the Institute for Visualization and Interactive Systems at the University of Stuttgart. His work is situated at the intersection of cognitive science, neuroscience, and computer science, collaborating with researchers across these disciplines. His lab utilizes behavioral experiments, EEG, eye-tracking, and computational modeling to investigate visual cognition, with emphasis on open science practices and methodological transparency.

• Macroeconomics
• Financial Regulation
• Economic Growth
• +3 more

Professor Stephen Wright is a faculty member in the Department of Economics at Birkbeck Business School, part of Birkbeck, University of London. His academic role includes teaching modules such as Macroeconomics (EMEC011S6) and Introduction to Economics (EMEC013S4). His research focuses on macroeconomics, financial regulation, economic growth, and econometric methods. He has contributed to understanding stock market predictability, the Indian economy's growth dynamics, and pension scheme sustainability. His work spans theoretical and applied economics, including studies on monetary policy, corporate finance, and economic indicators. His publications address topics like predictive models, state-level economic disparities in India, and the implications of financial regulation. Collaborations include analyses of GDP estimation and pension funding challenges. He has authored monographs on topics ranging from the UK's cost of capital to actuarial pension schemes. His research often bridges empirical analysis with policy relevance, particularly in macroeconomic stabilization and financial systems. Notable projects include reports on pension contribution rates and cost-of-capital methodologies for UK regulators. His work frequently appears in journals such as the Journal of Business and Economic Statistics and Economic and Political Weekly .

Barbara FischerView profile

Lecturer

• Evolutionary Medicine
• Obstetric Selection
• Pelvic Evolution
• +4 more

Barbara Fischer is an Elise Richter Fellow and Lecturer at the University of Vienna, Department of Evolutionary Biology. Her research investigates the evolutionary biomechanics of human childbirth, combining geometric morphometrics, comparative anatomy, and evolutionary modeling to resolve the 'obstetric dilemma'. Key contributions include the cliff-edge model of obstetric selection explaining rising Caesarean rates, analyses of sexual dimorphism in hominin pelves, and methods for skeletal age estimation using pubic symphysis morphology. Comparative work extends to Darwin's finches' self-medication behaviors and stickleback adaptation plasticity. She develops statistical approaches for morphological integration studies and teaches biostatistics, mathematical biology, and evolutionary medicine. Recognized with an Elise Richter Fellowship and science communication awards.

• Psychometrics
• Statistical Modeling
• Bayesian Methods
• +3 more

Roy Levy is a Professor at Arizona State University (ASU), holding appointments in the Department of Psychology and the T. Denny Sanford School of Social & Family Dynamics. His research focuses on quantitative methodology, including psychometrics, statistical modeling, Bayesian approaches, item response theory, and structural equation modeling. He specializes in applications such as assessment design and simulation-based evaluations. Levy teaches courses on Bayesian modeling, statistical analyses, and measurement methodologies. His work emphasizes methodological advancements to address interpretational challenges in factor analysis, missing data, and model validation. He has authored influential publications and a textbook on Bayesian psychometric modeling. Notably, his research bridges theoretical frameworks with practical applications in education and digital learning environments. Levy collaborates widely and maintains an active presence in academic publishing, contributing to journals like Structural Equation Modeling and Educational Assessment . Education and career details are not explicitly listed, but his academic trajectory is evident through his publications and roles. His research interests extend to dynamic Bayesian networks, educational measurement theory, and the integration of evidence-centered design principles in assessments. He advocates for rigorous methodological practices to enhance the reliability and validity of psychometric tools. Levy’s advising and grants are not detailed here, but his extensive publication record and textbook indicate significant contributions to training future researchers. His work on psychometric isomorphism and Bayesian network modeling highlights innovations in linking assessment design with cognitive processes. He is a key figure in advancing quantitative methods for evaluating complex educational and psychological constructs.

Lily AnView profile

Assistant Professor

• Educational Measurement
• Program Evaluation
• Accountability Policies
• +3 more

Lily An serves as an Assistant Professor of Quantitative Methodology within the Educational Policy Studies department at Georgia State University's College of Education & Human Development. She completed her Ph.D. in Education Policy and Program Evaluation at Harvard University in 2025, where she was affiliated with the Center for Education Policy Research as a PIER Fellow. Her academic credentials include: Ph.D. in Education Policy and Program Evaluation, Harvard University (2025) Ed.M. in Education Policy and Management, Harvard Graduate School of Education B.A. in Statistics, Williams College Dr. An's research focuses on advancing educational measurement techniques, developing rigorous program evaluation frameworks, and analyzing school accountability policies. She employs causal inference methods—including quasi-experimental designs—and specializes in meta-analyses of educational interventions. Her work frequently addresses summer learning programs' impacts on student outcomes, with particular attention to sociodemographic disparities in enrichment access and mathematics achievement gaps among low-income populations. She integrates psychometric tools to assess standardized testing validity within policy contexts. Analysis of her publication record (2019-2025) reveals a consistent trajectory in methodological innovation for education policy research. She has pioneered applications of Gaussian process regression in multidimensional regression discontinuity designs to overcome limitations of traditional analytic approaches, while her extensive meta-analytic work establishes evidence bases for summer learning interventions across cognitive, non-cognitive, and behavioral domains. Her scholarship bridges educational measurement, quantitative methodology, and K-12 policy implementation. Her professional recognitions include: PIER (Partnering in Education Research) Fellow, Center for Education Policy Research Equity and Inclusion Fellow, Harvard Graduate School of Education Summer Institute for Computational Social Science participant Just Education Policy Institute for Developing Scholars participant Dr. An has collaborated with state education agencies including the Massachusetts Department of Elementary and Secondary Education during her tenure as a research analyst at Brown University. Her current work continues this practice-oriented focus through quantitative analysis of policy effects on student outcomes, though specific grant funding details and advising activities were not documented in available sources.