Jing Zhou is a Lecturer in Statistics at the University of East Anglia, affiliated with the School of Engineering, Mathematics and Physics. Their research focuses on high-dimensional statistical methodologies, including variable selection, false discovery rate control, and robust estimation techniques. Dr. Zhou actively contributes to peer review for journals like Statistics and Computing and Statistics , and has presented at conferences such as the 2024 IMS International Conference on Statistics and Data Science. Research interests emphasize advancing statistical methods for social science and big data applications, particularly leveraging model-X knockoffs and black-box model assessments. Recent work explores trade-offs in false discovery vs. true positive rates in logistic regression and nonparametric quantile regression via vine copulas. Collaborative efforts include interdisciplinary projects addressing replication crises through methodological improvements. No scientific awards are explicitly listed. Advising activity and grants information is not provided in the text. Zhou is engaged in academic activities including invited talks and editorial work, contributing to both theoretical and applied statistical research.
Yiguo Sun is a Professor of Economics and University Research Leadership Chair at the University of Guelph, Department of Economics and Finance. She specializes in econometrics, focusing on semi-/nonparametric methods for panel data, non-stationary time series, and spatial regression models. Her research addresses issues such as investment dynamics, social interactions, and threshold effects. She holds a B.Sc. and M.Sc. from Hebei Normal University, an M.A. from the University of Guelph, and a Ph.D. from the University of Toronto. Awards include the CBE Senior Research Fellow (2018-2021) and University Research Leadership Chair (2022-2025). Education: B.Sc. in Mathematics, Hebei Normal University (1993) M.Sc. in Mathematics, Hebei Normal University (1996) M.A. in Economics, University of Guelph (1997) Ph.D. in Economics, University of Toronto (2002) Research Interests: Dr. Sun’s work centers on advancing econometric methodologies, particularly in nonparametric and semiparametric frameworks. Key areas include: Threshold regression models and their applications in inflation dynamics and social interactions Spatial econometrics and panel data analysis Nonlinear estimation techniques addressing endogeneity and measurement error Economic growth and natural resource nexus Recent Contributions: Her 2024 paper on investment-uncertainty relationships introduced a novel estimator addressing endogeneity and measurement bias. The 2023 Social Threshold Regression advanced peer effect analysis through a spatial Durbin framework. Recent articles explore spatial spillovers in trade policies and Canadian inflation dynamics using threshold models. Awards: University Research Leadership Chair (2022-2025) CBE Senior Research Fellow in Spatial Econometrics (2018-2021) SSHRC Insight Grant (2022-2025) Grants & Advising: She leads SSHRC-funded projects on social networks and measurement errors in finance. Supervised students include Delong Li (investment dynamics), Chaoyi Chen (threshold estimation), and Hui Xiao (model averaging). Research teams focus on econometric theory and applied policy analysis. Labs/Teams: Active in the University of Guelph’s CBE research community, collaborating with Thanasis Stengos, Emir Malikov, and international scholars on spatial econometrics and nonlinear methods.
Ioannis Kasparis is an Associate Professor of Econometrics at the Department of Economics , University of Cyprus , within the School of Economics and Management . His research focuses on time series econometric theory, particularly nonstationarity, fractional processes, nonparametric methods, heavy-tailed processes, specification testing, and asymptotic statistical theory. Education : BSc in Economics (University of Reading, 1998) MSc in Econometrics and Mathematical Economics (London School of Economics, 2000) PhD in Econometrics (University of Southampton, 2004) His research spans nonstationary time series analysis, nonparametric regression, and specification testing. Recent publications address topics like time-varying parameter regressions , nonlinear predictive models , and fractional integration . Articles often involve collaborations with prominent econometricians such as P.C.B. Phillips. Teaching roles include Econometrics (ECON 303, BSc) , Statistics & Econometrics (ECON 603, MSc/PhD) , and Econometrics (OIK 663, MSc) , with consistently positive student evaluations highlighting clarity, engagement, and pedagogical effectiveness.
Le-Yu Chen is a Research Fellow at the Institute of Economics, Academia Sinica . His research focuses on advanced econometric methods and their applications to complex economic problems. Research Interests: Microeconometrics, Econometric Theory, Applied Econometrics, Statistics Email: lychen@econ.sinica.edu.tw Chen has developed innovative methods in quantile regression, treatment effect estimation, and discrete choice modeling. His work combines theoretical rigor with computational implementation using tools like Gurobi optimization solver. Recent publications examine: Local conditional tail treatment effects Sparsity in quantile regression Dynamic programming models High-dimensional moment inequalities Nonparametric inference techniques
Thomas S Richardson serves as a Professor in the Department of Statistics at the University of Washington, where he maintains an active research profile in theoretical and applied statistics. His academic work is centered within the university's statistical research community with primary focus on methodological innovation. Richardson's research spans two core domains: Causal Inference and Multivariate Statistics. Within causal inference, he investigates foundational frameworks including potential outcomes, decision-theoretic approaches, and graphical causal models. His multivariate statistics work emphasizes complex dependency structures, parameter estimation for high-dimensional data, and novel modeling techniques for discrete and continuous variables. This dual focus drives methodological advancements applicable to biomedical, social, and computational sciences. Analysis of his recent preprints reveals strong concentration on causal identification problems, particularly instrumental variable bounds, individual treatment effect quantification, and graphical model parameterization. His work consistently bridges theoretical rigor with practical estimation challenges, often developing new mathematical frameworks for causal effect estimation under complex constraints. Key recurring themes include counterfactual reasoning, nonparametric bounds, and computational approaches to causal estimation. Richardson maintains active collaboration with prominent researchers including James M. Robins, evidenced by co-authored preprints. His email contact thomasr@uw.edu serves as the primary professional communication channel. No formal advising relationships, grants, or laboratory affiliations are documented in the available information.
Rui Miao serves as an Assistant Professor in the Department of Mathematical Sciences within the School of Natural Sciences and Mathematics at The University of Texas at Dallas. Previously, he worked as a Mathematical Statistician at the Office of Biostatistics Research at NIH/NHLBI and completed postdoctoral training under supervision of Dr. Annie Qu and Dr. Babak Shahbaba. His academic foundation includes a PhD in Statistics from The George Washington University under Dr. Xiaoke Zhang's mentorship. Education: PhD in Statistics, The George Washington University Dr. Miao's research program bridges advanced statistical methodology with critical healthcare applications. His work focuses on Reinforcement Learning for personalized treatment policies, Causal Inference methods addressing unmeasured confounding, Health AI applications, Functional Data Analysis techniques, and computational approaches to Few-shot Learning . His methodological innovations particularly address challenges in heterogeneous medical data and complex decision frameworks. Analyzing his publication trajectory reveals a strong emphasis on developing statistical frameworks for personalized medicine, with increasing interdisciplinary collaboration in cardiology, immunology, and neuroscience. His work spans theoretical statistics in journals like Annals of Statistics and Journal of the American Statistical Association to applied medical research in Science Advances and Journal of the American College of Cardiology . Scientific Recognition: 2021 ICSA Student Paper Award for work on wavelet-based independence testing Dr. Miao actively contributes to the academic community through invited talks at institutions including National Cancer Institute, Duke University, and NIH, presenting on reinforcement learning under heterogeneity and functional data analysis methods. His teaching portfolio at UT Dallas includes STAT 5304 Introduction to Human Health Research, building on previous teaching experience at The George Washington University covering statistical theory and applied courses.
Vadim Marmer is a Professor at the University of British Columbia (UBC) since 2005, affiliated with the Vancouver School of Economics . He earned his Ph.D. at Yale University. His research centers on Econometrics , with specific expertise in estimation and inference in auctions, weak identification, non-stationary time series, and network-dependent data analysis. Education : Ph.D., Yale University Institutional Affiliation : University of British Columbia Research Focus : Econometric theory, auction modeling, regime switching, and financial time series. His recent publications focus on stochastic cycles in macroeconomic data, treatment effect estimation in triangular models, and auction theory advancements. Collaborations include Jun Ma, Zhengfei Yu, and Artyom Shneyerov. Though no explicit awards are listed, his work appears in top journals like Journal of Econometrics and Quantitative Economics .
Edward H. Kennedy is an Associate Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences. He joined CMU in 2016 as an Assistant Professor after earning his PhD in Biostatistics from the University of Pennsylvania, alongside an MA in Statistics from the Wharton School, an MS in Biostatistics from the University of Michigan, and a BA in Mathematics from the University of Pennsylvania. His research focuses on causal inference, missing data problems, nonparametric methods, and machine learning, with applications in criminal justice, health services, medicine, and public policy. Key areas include incremental treatment effects, counterfactual analysis, and semiparametric sensitivity frameworks. He has developed methodologies for causal clustering, functional average treatment effects, and robust population size estimation. Edward's work bridges theoretical statistics and practical applications, addressing challenges in high-dimensional and complex data. His contributions span methodological advancements in instrumental variables, doubly robust estimation, and counterfactual fairness in algorithmic decision-making. His research also extends to policy-relevant domains such as healthcare resource allocation, criminal justice reform, and pandemic response analysis. Edward is actively involved in interdisciplinary collaborations, mentoring students in statistical theory and applied data science. His lab emphasizes rigorous methodological innovation while addressing real-world societal challenges through data-driven approaches.
Saman Muthukumarana is a Professor and Head of the Department of Statistics at the University of Manitoba. He joined the department in 2010 as an Assistant Professor, was promoted to Associate Professor in 2016, and became a full Professor in 2022. He holds a BSc (Honours Special) in Statistics from the University of Sri Jayewardenepura, an MSc from Simon Fraser University, and a PhD from Simon Fraser University under Dr. Tim Swartz, focusing on Bayesian methods and applications. His research emphasizes Bayesian methodologies for complex models, with applications in social networks, health studies, sports analytics, environmental science, and machine learning. He has secured over $8.4M in research funding from NSERC, Mitacs, CIHR, and other organizations. His work has been published in journals such as the Canadian Journal of Statistics, Machine Learning with Applications, and IEEE Open Journal of Instrumentation & Measurement. Dr. Muthukumarana’s research spans Bayesian computation, biostatistics, data science, and environmental statistics. He has contributed to anomaly detection in buildings, predictive modeling for public health (e.g., Long COVID), and ecological studies like salmon stock recruitment. His collaborative projects include developing statistical tools for microbiome analysis and improving machine learning approaches for imbalanced datasets. He also leads the Data Science Nexus, fostering interdisciplinary research. His grants and collaborations highlight his role in advancing statistical methodologies for real-world challenges, including health, energy efficiency, and ecological conservation. While no specific awards are listed, his extensive funding and publication record reflect his scholarly impact. He currently supervises graduate students and actively participates in academic leadership roles.
Shizhe Chen is an Assistant Professor in the Department of Statistics at the University of California, Davis. His research focuses on statistical methodology for high-dimensional data, point processes, network reconstruction, and machine learning applications in neuroscience and biostatistics. He holds a Ph.D. and teaches advanced statistical courses such as STA 290. His work emphasizes flexible modeling approaches for complex systems and nonparametric methods for independence testing. Key research interests include: High-dimensional statistical inference and graphical models Point process modeling with applications to neuroscience and social networks Development of assumption-light screening techniques for Hawkes processes Network reconstruction using ordinary differential equations Recent work explores temporal dynamics in hippocampal sequences and distribution-free statistical tests for modern data structures. His methods bridge theoretical advancements with practical applications in computational biology and econometrics.
Edoardo Zanelli is a Research Fellow and 5th-year PhD student in Economics at the University of Bologna's Department of Economics. He also serves as a Teaching Tutor and has held roles as a Teaching Assistant for courses like Mathematical Economics and Advanced Time Series Econometrics. His research focuses on econometric methodologies, particularly bootstrap inference, nonparametric techniques, and boundary parameter analysis. In Fall 2025, he will assume a postdoctoral position at Aarhus University's Aarhus Center for Econometrics (ACE). Education includes a BSc and MSc in Economics (both summa cum laude) from the Catholic University of Milan and University of Bologna respectively, followed by PhD studies at the University of Bologna under Prof. Giuseppe Cavaliere. Research interests include bootstrap methods for nonlinear models, bias correction in estimators, boundary parameter inference, instrumental variable (IV) specification testing, and Phillips Curve instability estimation.
Pujee Tuvaandorj is an Assistant Professor in the Department of Economics at York University, part of the Faculty of Liberal Arts & Professional Studies. His research focuses on econometric methods, particularly robust inference under weak identification and randomization techniques. He holds a Ph.D. from McGill University (2015), an M.A. from Hitotsubashi University (2009), and a B.A. from Kyoto University (2007). His work bridges theoretical econometrics and applied microeconometric models. Research interests span econometric theory, including permutation tests for linear models, instrumental variables analysis, and robust statistical methods. He also explores structural breaks, time series econometrics, and asymptotic theory. His recent work emphasizes methodological advancements in handling weak identification and serial dependence in econometric models. Teaching responsibilities include courses such as Introductory Statistics for Economists II , Financial Econometrics , and Econometric Theory . His publications in top journals like Quantitative Economics and Journal of Econometrics reflect his expertise in econometric inference and model development. No scientific awards are explicitly mentioned in the provided texts. His research portfolio includes contributions to regression discontinuity designs, generalized method of moments (GMM), and invariant tests. Current projects address digital adoption, cyber security, and labor market impacts on homelessness, leveraging Canadian administrative data.
Dominik Wied is a Professor of Statistics and Econometrics at the Faculty of Management, Economics and Social Sciences at the University of Cologne. Previously, he held positions as Assistant Professor (2011–2016) and Visiting Professor (2015–2016) at TU Dortmund. His research focuses on Financial Econometrics , Structural Breaks , and Microeconometrics , with applications in portfolio management and risk assessment. Education: Diploma in Statistics (TU Dortmund, 2008), Ph.D. in Statistics (TU Dortmund, 2009). Affiliations: Institute for Statistics and Econometrics (University of Cologne), ECONtribute Cluster of Excellence (Markets & Public Policy). His recent publications emphasize conditional distribution modeling , structural break detection , and copula-based dependence analysis , particularly in financial and economic contexts. He has contributed to advanced statistical methodologies in time series and spatial modeling, including applications to banking risk assessment and health insurance data. Notable trends in his work include: Developing robust tests for nonlinear endogeneity corrections and quantile regression stability. Advancing factor copula models with exogenous covariates for financial dependence structures. Improving spatial dependence and time series cointegration monitoring techniques. Contact: dwied@uni-koeln.de | Phone: +49 221 470 4514
Erin Evelyn Gabriel is a Professor in the Section of Biostatistics within the Department of Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences. She holds a PhD in Biostatistics from the University of Washington awarded on August 10, 2012. She teaches Introductory Statistics and Data Analysis at both Masters and Doctoral levels. Her research focuses on biostatistical methods development with applications to infectious diseases, vaccination, cancer, and aging. Key research areas include: Causal inference methodologies Surrogate evaluation, particularly in the presence of interference Nonparametric causal bounds Designs and estimation methods for emulated and randomized clinical trials Evaluation of prediction-based decision rules Her publication record shows a strong trajectory in methodological biostatistics, with recent work (2022-2025) focusing on causal bounds, mediation analysis, age-period-cohort models, instrumental variables, and biases in randomized controlled trials. Her work appears in top-tier journals including the Journal of the American Statistical Association, Biostatistics, and Epidemiology. Dr. Gabriel has established collaborations across multiple institutions, as evidenced by her co-authorship patterns with researchers including Sachs, Sjölander, Follmann, and Ocampo. Her research impact is indicated by citations and downloads of her work, with some publications gaining attention on social media platforms and news outlets.
Grant Morgan is a Professor in the Department of Educational Psychology at Baylor University's School of Education, where he also serves as Associate Dean for Research and Outreach and Program Director for the Quantitative Methods graduate program. He holds a Ph.D. in Educational Research & Measurement from the University of South Carolina and has been a faculty member at Baylor since 2012. Educational Background: Ph.D. in Educational Research & Measurement, 2012, University of South Carolina, Columbia M.S. in Human Resources Management (Organization Performance track), 2005, Western Carolina University, Cullowhee B.S. in Psychology, 2003, Clemson University Dr. Morgan's research focuses on latent variable models , psychometrics , classification , and nonparametric statistics . He conducts methodological investigations using Monte Carlo simulations and applies advanced quantitative models in interdisciplinary contexts. His work emphasizes validity in psychological measurement and accurate estimation in latent variable frameworks. His recent publications reflect a strong trend in Bayesian factor analysis, latent class modeling, robust estimation for ordinal data, and the generation of nonnormal distributions using mixture models. These works span top-tier journals such as Psychological Methods , Structural Equation Modeling , and Language Assessment Quarterly , highlighting his contributions to both theoretical and applied psychometrics. Scientific Awards and Recognition: Three-time recipient of Distinguished Paper Awards from AERA-affiliated organizations Nominated for Cornelia Marschall Smith Professor of the Year Nominated for Division D Early Career Award Dr. Morgan is actively involved in academic leadership and service. He has served as Chair of the Structural Equation Modeling SIG at AERA, is a board member of a regional AERA-affiliated organization, and regularly serves as a panelist for the National Science Foundation and U.S. Department of Education. He advises on externally funded research projects totaling over $20 million and mentors graduate students in quantitative methods. He serves on the editorial board of the Journal of Psychoeducational Assessment and reviews for leading methodological journals including Structural Equation Modeling , Psychometrika , and Multivariate Behavioral Research . He leads the Quantitative Methods specialization, teaching courses such as Psychometric Theory, Item Response Theory, Latent Variable Models, and Nonparametric Statistics. His lab and research team focus on advancing methodological rigor in educational and psychological measurement through simulation studies and real-world applications.
