جست‌وجوی استاد و پژوهشگر

Johnson Liمشاهده پروفایل

دانشیار

• Quantitative Psychology
• Psychological Measurement
• Statistical Methods
• +۴ مورد دیگر

Dr. Johnson Li serves as Associate Professor in the Department of Psychology, Faculty of Arts at the University of Manitoba, where he chairs the Quantitative Methods section of the Canadian Psychological Association. His academic journey spans institutions including the University of Alberta, Chinese University of Hong Kong, and University of Hong Kong. His educational background includes: PhD (Measurement, Evaluation, and Cognition) from University of Alberta M.Phil (Psychology) from Chinese University of Hong Kong BSSc (Psychology) from University of Hong Kong Dr. Li's research pioneers robust statistical methodologies for psychological measurement, focusing on effect size estimation, scale validation, and correction techniques for non-normal data and range restriction. His work bridges theoretical psychometrics with practical applications in educational and organizational settings, emphasizing reliability and validity in measurement models. Analysis of his publication record reveals consistent innovation in robust statistics, particularly through bootstrap methods and probability-based effect sizes. His research addresses critical methodological gaps in handling outliers, heteroscedasticity, and multivariate relationships across psychological subfields. His scientific recognition includes: Terry G. Falconer Memorial Emerging Researcher Award (2019) Department of Psychology Teaching Award (2016) Faculty of Arts Special Achievements Award (2016) Dr. Li actively mentors Master's and PhD students in data analysis and research methodology while teaching core quantitative courses including PSYC 7200/7210. His leadership extends to developing statistical resources through the Quantitative and Applied Statistics (LIQAS) lab. The LIQAS lab provides open-access R packages and computational tools for robust correlations, common-language effect sizes, and probability-based statistics, serving as a vital resource for methodological advancement in behavioral sciences.

• Income Distribution
• Labor Economics
• Education Economics
• +۴ مورد دیگر

Prof. Dr. Martin Biewen is a Full Professor of Statistics, Econometrics and Quantitative Methods at the University of Tübingen 's Faculty of Economics and Social Sciences. Since 2023, he serves as Scientific Director of the Institute for Applied Economic Research (IAW Tübingen) and is a member of the Cluster of Excellence - Machine Learning for Science. His research spans income distribution , labor economics , education economics , and microeconometrics , with recent work applying machine learning to inequality analysis. PhD, University of Heidelberg (2000) Habilitation, University of Mannheim (2005) His methodological innovations include bootstrap inference for inequality measurement and Stata implementations of decomposition techniques. He has served on multiple advisory boards including the German Federal Ministry of Labour and Social Affairs and the German Economic Association 's standing committees. Current grants include DFG Priority Programme 1764 and leadership roles in the LEAD Graduate School. Articles demonstrate expertise in minimum wage impacts , wealth inequality , and gender gaps in economic literacy . He has developed statistical software packages for Stata and R used in inequality analysis across 20+ journals.

Andrei Volodinمشاهده پروفایل

استاد

• Statistical Inference
• Limit Theorems
• Distributions on Abstract Spaces
• +۱۲ مورد دیگر

Andrei Volodin is a Professor in the Department of Mathematics and Statistics at the University of Regina, Canada. He serves as the Co-op Work/Study Coordinator and has an extensive publication record spanning probability theory, statistical inference, and applied statistics. His research focuses on limit theorems, bootstrap methods, and distributional analysis with applications to quality control and healthcare economics.

• Econometrics
• Applied Financial Economics
• Business Data Analytics
• +۹ مورد دیگر

Dr. Jen-Je Su is a Senior Lecturer in the Department of Accounting, Finance and Economics at Griffith University, affiliated with the Griffith Asia Institute and Griffith Institute for Tourism. He holds a PhD in Economics from Michigan State University and previously taught at Massey University, New Zealand. His research focuses on econometrics, applied financial economics, business analytics, economic development, and tourism economics. Key interests include cryptocurrency, asset pricing, energy economics, and sustainable development goals like affordable energy and economic growth. Dr. Su's research outputs span 77 articles, with recent works examining digital inequality impacts, stock market semibetas, tourism spillover effects, and renewable energy forecasting. He has secured grants from organizations like DFAT and the United Nations, focusing on Pacific digital economies, economic policy in developing nations, and tourism data analysis. He supervises doctoral students exploring topics like credit constraints, corporate governance risks, and tourism economics. His teaching and supervision emphasize empirical methods and policy-relevant research. Dr. Su collaborates with institutions like GHD Pty Ltd and Austrade, contributing to policy formulation and economic analysis. He is fluent in English and Mandarin, peer-reviewed in multiple journals, and active in interdisciplinary groups like the Griffith Asia Institute. His work bridges theoretical econometrics with applied policy solutions in Asia-Pacific contexts.

• Econometrics
• Applied Econometrics
• Labor Economics
• +۳ مورد دیگر

Yuanyuan Wan is an Associate Professor in the Department of Economics at the University of Toronto. She holds a Ph.D. from Pennsylvania State University (2011), an M.A. from Peking University (2005), and a B.A. from Beijing Normal University (2002). Her research focuses on econometrics and applied econometrics, with an emphasis on policy analysis, labor economics, and China-specific economic issues. Her work includes developing econometric methods for testing model assumptions, evaluating treatment effects, and analyzing policy impacts. Key research contributions include studies on retirement spillover effects, labor force participation trends in China, and statistical methods for regression discontinuity designs. Her publications frequently appear in top-tier journals like the Journal of Econometrics and Quantitative Economics. No scientific awards are listed in the provided information. While no student advisees are explicitly mentioned, her research collaborations include prominent economists such as Ismael Mourifié and Thomas Russell. She is affiliated with the Max Gluskin House office and maintains a personal website at sites.google.com/view/yuanyuanwan .

• Educational Measurement
• Meta-Analysis
• Statistical Methods
• +۳ مورد دیگر

Ariel M. Aloe is a Professor of Educational Measurement and Statistics and currently serves as the Interim Associate Dean for Research in the College of Education at the University of Iowa. He holds affiliations in the Department of Psychological and Quantitative Foundations and the Office of the Dean (Administration) . His research focuses on meta-analytic methods, statistical synthesis, and educational measurement, with a particular emphasis on improving evidence-based practices in education and psychology. Aloe earned his BA in Physical Education from the Universidad de Flores, followed by an MA in Educational Psychology from Loyola University Chicago. He completed his MS in Statistics and PhD in Measurement and Statistics at Florida State University. His professional memberships include the National Council on Measurement in Education (NCME), American Educational Research Association (AERA), American Statistical Association (ASA), and Society for Research Synthesis Methodology (SRSM). His work emphasizes advancing statistical methodologies for synthesizing research evidence, particularly in quasi-experimental designs and meta-analytic frameworks. Aloe has led grants such as the National Science Foundation-funded project on partial effect sizes , demonstrating expertise in bridging quantitative methods with applied educational research. His publications span topics like ADHD treatment efficacy, summer reading program outcomes, and measurement invariance in intersectional groups.

• Bayesian Econometrics
• Quantile Regression
• Machine Learning
• +۲ مورد دیگر

Mohammad Arshad Rahman is an Associate Professor in the Department of Economic Sciences at IIT Kanpur. His research spans Bayesian econometrics, quantile regression, machine learning, and applied econometrics. He has held positions at Zayed University and University of California, Irvine, and currently teaches econometrics and finance courses. Education: Ph.D. Economics, University of California, Irvine (2013) M.S. Statistics, UC Irvine (2011) M.A. Economics, UC Irvine (2009) M.A. Economics, Delhi School of Economics (2006) B.Sc. Economics Hons., St. Xavier's College, Kolkata (2004) Research focuses on developing Bayesian methods for econometric problems, with applications in energy economics, finance, and social policy. Research areas include: Bayesian inference techniques, quantile regression models, machine learning applications in economics, discrete choice modeling, and time series analysis. Awards and Honors: Social Science Merit Fellowship Multiple Summer Research Fellowships All India Post-Graduate Scholarship Analyst Accolade Award

Hang Zhouمشاهده پروفایل

پژوهشگر ارشد

• Functional and high-dimensional data analysis
• Complex-structured data analysis
• ODE/PDE modeling
• +۳ مورد دیگر

Hang Zhou is a Post-Doctoral Scholar at the University of California, Davis, specializing in advanced statistical and computational methodologies. His research focuses on functional and high-dimensional data analysis, complex-structured data including manifold and Wasserstein types, and ODE/PDE modeling. He is affiliated with the Mathematical Sciences Building at UC Davis. His research interests encompass functional data analysis, high-dimensional statistics, and the integration of differential equations into modeling frameworks. He explores complex-structured data such as manifold and Wasserstein geometries, advancing methodologies for analyzing distribution-valued processes and error detection in numerical data. Zhou’s recent work bridges machine learning and statistical theory, exemplified by studies on deep regression for repeated measurements and event-attended graph ODE frameworks. His research also emphasizes conformal inference and optimal transport representations, reflecting a focus on robust statistical methods and interdisciplinary applications in data science. As a postdoctoral researcher, Zhou has not yet listed advisees or grants in the provided information. His website ( https://hg-zh.github.io/ ) provides additional details on his ongoing projects and collaborations.

José R. Zubizarretaمشاهده پروفایل

استاد

• Causal Inference
• Biostatistics
• Health Policy
• +۳ مورد دیگر

José R. Zubizarreta is a Professor in the Department of Health Care Policy at Harvard Medical School, a Professor in the Department of Biostatistics at Harvard School of Public Health, and a Faculty Affiliate in the Department of Statistics at Harvard University's Faculty of Arts and Sciences. His research focuses on developing statistical methods for causal inference, particularly in healthcare and public policy contexts. He has received multiple awards, including the Kenneth Rothman Award and the Tom Ten Have Memorial Award, and is a Fellow of the American Statistical Association. He holds editorial roles at prominent journals such as the Annals of Applied Statistics and the Journal of the American Statistical Association . His work includes co-editing the Handbook of Matching and Weighting Adjustments for Causal Inference , a seminal reference in the field. Zubizarreta’s research addresses challenges in observational studies, including methods for bias reduction, generalization of results, and applications in healthcare quality measurement and policy evaluation. Notable contributions include advancements in optimal matching techniques, balancing weights, and scalable methods for analyzing large observational datasets. His collaborative projects span interdisciplinary areas such as healthcare economics, epidemiology, and machine learning applications in causal inference. Zubizarreta actively contributes to software development, including R packages like mipmatch and designmatch , which are widely used in statistical research. His academic service includes roles in editorial boards and grant review panels, reflecting his leadership in advancing statistical methodology and its applications. Zubizarreta’s work bridges theoretical rigor with practical impact, addressing pressing questions in healthcare systems and public policy.

Manhal Aliمشاهده پروفایل

مدرس

• Organisational Economics
• Managerial Economics
• Health Economics
• +۳ مورد دیگر

Manhal Ali is a Lecturer in Human Resource Management at the Leeds University Business School, University of Leeds. His expertise spans Organisational Economics, Managerial Economics, Health Economics, Digital Economy, and Data Science. Previously, he held roles as a Lecturer in Business Analytics at Bournemouth University, Research Fellow at Leeds University Business School, and Post-Doctoral Research Officer at the Oxford Department for International Development, University of Oxford. He holds a PhD from the University of Manchester and an MSc from the University of Bristol. His research focuses on healthcare management, data-driven decision-making, and the application of advanced analytics to solve complex organizational challenges. Notable areas include nurse turnover dynamics, pandemic risk management in low-income countries, and the impact of management practices on healthcare outcomes. Ali's work frequently intersects quantitative methodologies like machine learning and regression analysis with real-world policy issues. His recent research highlights include analyzing workforce disruptions during the pandemic and developing predictive models for healthcare system efficiency. He is affiliated with the Centre for Employment Relations, Innovation and Change at the University of Leeds.

• Statistical Learning
• Molecular Medicine
• Bioinformatics
• +۳ مورد دیگر

Zhi Zhao is a Researcher at the University of Oslo's Faculty of Medicine, affiliated with the department of Statistical learning in molecular medicine. His work focuses on statistical and computational methods applied to molecular medicine, including omics data analysis, pharmacogenomics, and cancer genomics. Research Interests Statistical learning for multi-omics data integration Bayesian variable selection in drug sensitivity studies Biomarker discovery for immune-related diseases Survival analysis modeling in clinical applications Key Contributions Developed R packages like BayesSUR for multivariate Bayesian regression and EnrichIntersect for set enrichment analysis. His recent work includes studies on immune checkpoint inhibitor toxicity biomarkers and pan-cancer drug response prediction. Awards No scientific awards explicitly mentioned in the provided text. Labs/Teams Core member of the Statistical learning in molecular medicine research group.

• Labor Economics
• Applied Microeconomics
• Economics of Education
• +۳ مورد دیگر

Prof. Jessica Pan serves as Vice Provost (Graduate Education) and Dean of the NUS Graduate School at the National University of Singapore (NUS). She holds a PhD and MBA from the University of Chicago Booth School of Business and a BA in Economics from the University of Chicago. Her research focuses on labor economics, applied microeconomics, and gender studies, with notable contributions to understanding gender gaps, education policy, and immigration. She has been recognized with awards including the NUS Young Researcher Award (2022) and election as a Fellow of the Econometric Society (2020). Prof. Pan is a Research Fellow at the Institute for the Study of Labor (IZA) and the Centre for Economic Policy Research (CEPR). She co-edits the Journal of Public Economics and has served on editorial boards of top journals. Her work bridges theoretical models with empirical data, addressing real-world issues like labor market disparities and family planning. Education: Bachelor’s in Economics, University of Chicago MBA and PhD, University of Chicago Booth School of Business Research Interests: Gender economics Immigration impacts Education policy Household decision-making Grants & Awards: Fellow, Econometric Society (2020) NUS Young Researcher Award (2022) Dean’s Chair (2019) FASS Award for Promising Researcher (2015) Labs/Teams: Engaged with the Department of Economics at NUS and collaborates internationally through IZA and CEPR.

Gene Hahnمشاهده پروفایل

استاد

• Bayesian Methods
• International Operations
• Offshoring
• +۷ مورد دیگر

Dr. Gene Hahn is a Professor in the Department of Information Systems and Operations Management at Salisbury University, housed within the Perdue School of Business. He specializes in Bayesian methods, international operations, offshoring, and decision-making processes. His research has been published in top-tier journals such as Journal of World Business , Journal of International Management , and European Journal of Operational Research . He authored the book *Bayesian Methods for Management and Business* and is a Certified Analytics Professional (INFORMS). Dr. Hahn founded Salisbury University's first Innovation Lab, focusing on 3D printing and Arduino-based product development. His recent work emphasizes Bayesian statistical models for overdispersed data and missing value imputation, with applications in international business and operations management. He has also contributed to understanding the cultural and economic factors influencing offshoring decisions. His research spans theoretical advancements in Bayesian techniques and practical applications in global supply chain management, risk analysis, and strategic decision-making. Dr. Hahn teaches courses in Operations Management and Business Statistics. His awards include recognition for his contributions to Bayesian methodologies in management research. Professional activities include editorial roles, conference presentations, and computational resource development for Bayesian analysis in WinBUGS.

Kristopher J. Preacherمشاهده پروفایل

استاد

• Structural Equation Modeling
• Multilevel Modeling
• Mediation and Moderation Analysis
• +۳ مورد دیگر

Kristopher J. Preacher is a Professor in the Quantitative Methods (QM) program at Vanderbilt University's Department of Psychology & Human Development. He specializes in bridging theoretical frameworks with multivariate statistical techniques for analyzing complex longitudinal and correlational data. Lois Autrey Betts Chair in Education & Human Development Associate Chair, Department of Psychology & Human Development Editorial Board Member: Behavior Research Methods, Organizational Research Methods His methodological research focuses on: Structural Equation Modeling (SEM) Multilevel Modeling (MLM) Mediation and Moderation Effects Model Fit Evaluation Factor Analysis Continuous Time Modeling Recent publications emphasize model selection strategies, nonlinear treatment effects in cluster trials, and challenges in power analysis for complex models. Key collaborations span nutrition science, educational interventions, and clinical psychology applications. Scientific contributions include: Developing open-source statistical tools (e.g., semTools) Advancing multilevel SEM frameworks Promoting best practices for variable centering He advises graduate students and teaches courses in: Applied Nonparametric Statistics Multilevel Modeling

Austin C. Smithمشاهده پروفایل

دانشیار

• Economics of Education
• Health Economics
• Labor Economics
• +۳ مورد دیگر

Austin C. Smith is an Associate Professor of Economics at Bates College, located in Lewiston, Maine. His research focuses on policy-relevant questions in health, education, and labor markets, with notable projects examining compensation structures in healthcare, occupational licensing impacts, and factors influencing student academic performance and career outcomes. He teaches courses in Econometrics, Labor Economics, and Research Methods. Smith’s work bridges empirical analysis with real-world policy implications. His recent studies include analyzing the effects of performance pay in community health centers, evaluating the long-term earnings impacts of Greek life participation, and investigating daylight saving time’s correlation with fatal vehicle crashes. His research has been featured in prominent journals like the Journal of Public Economics and American Economic Journal: Applied Economics , alongside media outlets such as The Atlantic and Wall Street Journal . Methodologically, Smith employs advanced empirical techniques, including regression discontinuity designs and large-scale data analysis. His interdisciplinary approach addresses societal challenges at the intersection of economics, public health, and education policy. Outside academia, he enjoys outdoor activities like hiking and kayaking with his family, reflecting his commitment to balancing rigorous scholarship with personal well-being.