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Michael C RodriguezView profile

Professor

• Psychometrics
• Educational Measurement
• Test Accessibility
• +7 more

Michael C Rodriguez is a Professor in the Quantitative Methods in Education department at the University of Minnesota's College of Education and Human Development. He serves as Dean in the Administration (CEHD) and is a leading expert in psychometrics and educational assessment. PhD in Measurement and Quantitative Methods, Michigan State University (1999) MA in Educational Psychology, University of Minnesota (1995) His research focuses on psychometric properties of tests , optimizing test accessibility for diverse learners, and advancing applied measurement through collaborations with schools. Key areas include constructed-response vs. multiple-choice item design, SDG-aligned educational practices, and the intersection of developmental assets with student engagement . Recent publications examine automated evaluation tools , bullying impacts on foster youth, and test validity frameworks . His work aligns with the UN Sustainable Development Goal 4 (Quality Education) . He collaborates on grants from the U.S. Department of Education and Institute of Education Sciences , including projects like the Individual Growth and Development Indicators for Hmong preschoolers and Computer Adaptive Storybook Assessment (CASA).

• Team Dynamics
• Organizational Behavior
• Negotiation Theory
• +3 more

Stephen Humphrey is the Alvin H. Clemens Professor of Management and Organization at the Smeal College of Business, Pennsylvania State University. His research focuses on social relations at work, with a primary emphasis on teamwork dynamics, negotiation theory, and organizational behavior. He holds a PhD in Organizational Behavior and Human Resource Management from Michigan State University and a BS in Psychology from James Madison University. Professor Humphrey's research explores two core areas: (1) the 'bottom-up' formative design of teams (including composition, role allocation, and reward structures), and (2) the 'top-down' management of existing teams (focusing on temporal dynamics, structural adaptation, and conflict resolution). His work on team microdynamics examines multilevel, multi-period, and multi-theoretical aspects of teamwork. His recent publications demonstrate strong research trends in team dynamics, organizational reputation, negotiation pedagogy, and ethical decision-making. Articles frequently employ meta-analytic approaches and focus on contextual factors influencing team performance across diverse settings. The research consistently bridges theoretical frameworks with practical organizational applications. Professor Humphrey teaches negotiation skills across undergraduate, MBA, EMBA, and executive education programs. He has developed several negotiation simulations derived from real-world scenarios and teaches doctoral seminars on organizational research design. As faculty advisor, he mentors PhD and DBA students in research methodology and academic development.

Jennifer SomersView profile

Assistant Professor

• Developmental Psychology
• Clinical Psychology
• Psychophysiology
• +7 more

Jennifer Somers is an Assistant Professor in the Department of Psychological Sciences at Auburn University , directing the FLOWER Laboratory (Families: Leveraging Opportunities for Wellbeing and Resilience). Her research examines biobehavioral processes in parent-child interactions that influence depression and anxiety risks. Education: PhD in Clinical Psychology (Arizona State University, 2021) MA in Clinical Psychology (Arizona State University) BA in Psychology & English (Pomona College) Her work focuses on dyadic resilience , emotion regulation , and physiological synchrony in families. She has secured funding from NIMH, NSF, and private foundations. Recent publications analyze parent-child interactions , RSA synchrony , and transdiagnostic biomarkers spanning developmental psychology, clinical research, and behavioral science. Labs & Teams: FLOWER Lab investigates mechanisms to promote family resilience across the lifespan, integrating biological and environmental factors in mental health outcomes.

Valentijn M.T. de JongView profile

Assistant Professor

• Biostatistics
• Epidemiology
• Medical Research Methodology
• +3 more

Valentijn M.T. de Jong is an Assistant Professor at Utrecht University, specializing in methodological advancements in biostatistics and epidemiology. His research focuses on causal inference, missing data analysis, and meta-analytical techniques in medical studies. Research Trends: Recent publications highlight his expertise in statistical methods for handling missing data (e.g., Heckman selection models), causal inference in individual-participant data meta-analyses, and enhancing prediction model discrimination in healthcare research. His work spans disciplines like epidemiology, biostatistics, and health data science.

Stefano CAMPOSTRINIView profile

Professor

• Social Statistics
• Public Health
• Healthcare Systems
• +6 more

Stefano CAMPOSTRINI is a Full Professor in the Department of Economics at Ca' Foscari University of Venice, specializing in Social Statistics (STAT-03/B). He serves as a Member of the technical-scientific Committee of the Ca' Foscari Challenge School and the Department of Economics' Committee. His research activities are supported by affiliations with the Research Institute for Social Innovation and the Research Institute for Innovation Management. Professor CAMPOSTRINI's research spans the intersection of statistical methodology, public health, and social policy. His work demonstrates expertise in advanced statistical techniques including Bayesian modeling, spatial analysis, and complex survey methodology. His primary focus areas include healthcare systems analysis, social innovation, public administration, and the economic aspects of health policy. He frequently addresses issues related to comorbidity patterns, healthcare service accessibility, and the application of artificial intelligence in healthcare settings. His publication record from 2021-2025 reveals significant trends in healthcare innovation, with particular emphasis on virtual hospital systems, AI applications in medicine, sustainable healthcare practices, and the statistical analysis of social services like early childhood education. His methodological contributions include novel approaches to analyzing regional health disparities and developing web-based tools for disease prevalence estimation. His research often employs expert consensus methods like Delphi techniques to address complex healthcare organizational challenges. Professor CAMPOSTRINI maintains active involvement in research initiatives through the Research Institute for Social Innovation and the Research Institute for Innovation Management. His work bridges advanced statistical methodology with practical applications in healthcare policy and social service delivery, making significant contributions to evidence-based decision making in public health and social policy domains across Italy and European contexts.

Patrick J. CurranView profile

Professor

• Quantitative Methods
• Longitudinal Data Analysis
• Developmental Psychopathology
• +3 more

Patrick J. Curran is a Professor in the Department of Psychology at the University of North Carolina, affiliated with the L.L. Thurstone Psychometric Laboratory and the Quantitude initiative. His research focuses on quantitative methodologies, particularly longitudinal data analysis and structural equation modeling, applied to developmental psychopathology and adolescent substance use. He collaborates on statistical education through YouTube tutorials, a blog (Help Desk), and workshops, alongside a podcast with Greg Hancock. His work bridges methodological innovation with substantive research in psychology and public health. Key contributions include advancing latent curve models with structured residuals, integrative data analysis techniques, and the dissemination of statistical tools. His research spans disciplines such as developmental psychology, clinical psychology, and epidemiology, addressing topics like alcohol use trajectories, chronic depression mechanisms, and pandemic-related disparities in healthcare workers’ mental health. Lab Affiliations: L.L. Thurstone Psychometric Lab, Quantitude Collaborations: Dan Bauer (statistical education), Greg Hancock (podcast), and interdisciplinary teams in public health and clinical research His publications emphasize methodological rigor and practical application, often addressing gaps in longitudinal data analysis and measurement equivalence. Notable work includes the latent curve model with structured residuals (LCM-SR) and integrative data harmonization strategies for prevention science.

• Biostatistics
• Public Health
• Medical Imaging
• +3 more

Jingjing Zou is an Assistant Professor in Residence at the Herbert Wertheim School of Public Health & Human Longevity Science, University of California San Diego. Her research focuses on integrating statistical methodologies with medical imaging and public health challenges, particularly in cancer prevention, physical activity analysis, and radiomics. Education: PhD in Statistics, Columbia University MA in Statistics, Columbia University BS in Statistics, Peking University Research Interests: Dr. Zou's work emphasizes functional data analysis, accelerometer-based physical activity monitoring, and MRI techniques for cancer treatment evaluation. She develops statistical models to analyze longitudinal health data, with applications in cardiovascular disease prevention and oncology. Her studies often bridge clinical outcomes with advanced imaging biomarkers. Grants: NIH/NHLBI (Principal Investigator): Examining Longitudinal Changes in Accelerometer-Measured Physical Activity in Preventing Cardiovascular Disease (2024-2028) NIH R37CA249659 (Co-Investigator): Advanced diffusion MRI for cervical cancer treatment evaluation (2021-2026) NIH R01CA255780 (Co-Investigator): Whole-body radiomics for gynecologic cancers (2020-2024) Labs/Teams: Collaborates with interdisciplinary teams in radiology, oncology, and biostatistics, including co-authors like Loki Natarajan (UCSD) and Loren Mell (UCSD).

Lauren ZallaView profile

Researcher

• Social epidemiology
• Health disparities
• HIV research
• +5 more

Lauren Zalla, PhD, MS, is a Research Associate at the Bloomberg School of Public Health and School of Medicine at Johns Hopkins University. She holds a PhD in Epidemiology from UNC Chapel Hill (2022) and an MS in Public Health from Duke University (2015). Her work focuses on policy approaches to improve health outcomes and reduce inequities among people with HIV in the United States. PhD, University of North Carolina at Chapel Hill (2022) MS, Duke University (2015) Dr. Zalla examines social and structural determinants of health, racial disparities, and effectiveness of interventions like the Ending the HIV Epidemic Initiative and state AIDS Drug Assistance Programs. Her methodological contributions include improving disparity measurement, causal inference techniques, and sub-population estimation. Recent publications analyze spatio-temporal mortality patterns, antidepressant impacts on viral suppression, and integrase inhibitor associations with diabetes in HIV populations. Key awards include the 2025 Lilienfeld Postdoctoral Paper Prize Finalist and 2023 Delta Omega Honorary Society membership. 2025 Lilienfeld Postdoctoral Paper Prize Finalist 2023 Health Disparities Scholar 2023 Bernard G. Greenberg Award 2022 Tyroler Student Paper Prize Finalist Her research integrates spatial epidemiology, causal modeling, and policy evaluation to address systemic inequities in HIV care. Collaborations span clinical and population health domains with major journals including JAMA, American Journal of Epidemiology, and AIDS.

• Educational Identity Development
• Adolescent Psychology
• School Transitions
• +3 more

Dr. Annabelle Christiaens serves as a Lecturer in the Department of Youth and Family at Utrecht University's Faculty of Social and Behavioural Sciences. She currently contributes to the "Pedagogy of Hope" project, where she investigates adolescents' attitudes toward climate and sustainability through the lens of eco-identity development. Her academic journey began with a Bachelor in Psychology at Tilburg University (2013-2017, with distinction), followed by a Research Master in Psychology: Individual Differences and Assessment (2017-2019, with honors). She completed her PhD at Utrecht University in 2024 with the dissertation "Navigating School Transitions: The Formation and Maintenance of Adolescents' Educational Identity," supervised by Dr. Stefanie Nelemans and Professor Dr. Susan Branje. Dr. Christiaens' research expertise centers on educational identity development during critical school transitions, with particular focus on how adolescents construct and maintain their educational identities. Her work employs sophisticated longitudinal methodologies and advanced statistical techniques including latent class/profile analysis, multilevel modeling, and structural equation modeling. She has made significant contributions to understanding identity processes through the development and validation of measurement tools like the Educational Identity Processes Scale (EIPS). Analysis of her publication record reveals a clear trajectory from foundational work on identity measurement and developmental patterns toward increasingly complex examinations of the interplay between identity formation, psychological needs, and environmental factors. Her recent work demonstrates growing attention to the role of basic psychological needs in identity construction and the reciprocal relationships between school environments and identity development. Dr. Christiaens maintains active engagement with the academic community through multiple service roles: Local Committee member for the ISRI conference 2024 Co-organizer of the CAS Studies Research Day Member of the CAS educational advisory committee Her commitment to methodological excellence is evident through her participation in numerous specialized training programs, including summer schools on multilevel analysis, SEM modeling in Mplus, and longitudinal analysis methods. Her current research on eco-identity represents an important expansion of her work into sustainability education, demonstrating the adaptability and relevance of identity frameworks to contemporary societal challenges.

Özlem İlk DağView profile

Professor

• Longitudinal Data Analysis
• Multilevel Modeling
• Interactive Graphics
• +5 more

Prof. Dr. Özlem İlk Dağ is a Professor of Statistics at the Middle East Technical University (METU), Ankara, Turkey. She holds the position of Department Head of Statistics and serves on advisory boards for TUBITAK and the Journal of Biostatistics. Her academic journey includes roles from Research Assistant (1997) to Professor (2018), with affiliations in Actuarial Sciences and Biostatistics. Education: Ph.D. in Statistics (Iowa State University, 2004), M.S. and B.S. from METU. Research focuses on longitudinal data analysis, multilevel modeling, Bayesian methods, and biostatistics. She authored three editions of R Yazilimina Giris and a foundational book on multivariate longitudinal data analysis. Key contributions include developing marginalized transition random effects models (MTREM) and R packages like 'mmm'. Awards include METU's 20-year service recognition and teaching excellence. She has supervised numerous research projects and co-authored over 50 peer-reviewed articles across biostatistics, genomics, and statistical computing. Her work bridges statistical theory and application, with notable contributions to gene expression clustering, maternal antibody studies in veterinary science, and computational methods for longitudinal data. She maintains active collaborations in genomics and biostatistics, and her lab focuses on integrating statistical computing with biomedical research.

• Multivariate Methods for Change Analysis
• Latent Class Models in Developmental Processes
• Machine Learning in Psychology
• +3 more

Kevin John Grimm is a Professor in the Department of Psychology at Arizona State University (ASU), serving as Director of Operations and Research within the College of Health Solutions. He holds a B.A. in Mathematics and Psychology from Gettysburg College (2000), and M.A. (2003) and Ph.D. (2006) in Psychology from the University of Virginia. Previously, he served as faculty at UC Davis before joining ASU in 2014. His research focuses on multivariate methods for analyzing developmental change, including nonlinear growth modeling, latent class analysis, and integrating machine learning with psychological data. Notable contributions include co-authoring the textbook *Growth Modeling: Structural Equation and Multilevel Modeling Approaches* (Guilford Press, 2017). Teaches courses like Structural Equation Modeling, Longitudinal Growth Modeling, and Machine Learning in Psychology at ASU. Active in professional service: Associate Editor of *Structural Equation Modeling: A Multidisciplinary Journal* since 2012. Recipient of NIH/NIDA grants for drug abuse/HIV prevention research and NICHD-funded studies on sleep health in children. His methodological work bridges quantitative innovation with substantive developmental research, emphasizing rigorous model specification and cross-disciplinary applications.

Jianxi GaoView profile

Associate Professor

• Network Science
• Artificial Intelligence
• Control Theory
• +3 more

Jianxi Gao is an Associate Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). His research focuses on network science, particularly network resilience, robustness, and control, integrating network theory, control theory, statistical physics, and operations research. He also explores the intersection of network science and AI, including applications of AI to network analysis and vice versa. His work aims to understand, predict, and control the resilience of complex systems against cascading failures. Key research areas include network resilience in transportation systems, quantum networks, and biological systems, with applications to pandemic response and infrastructure optimization. Gao's contributions span theoretical frameworks and computational tools, such as the NuRsE MATLAB package for network resilience analysis. His GitHub repositories (e.g., NuRsE and NON) showcase his open-source contributions to network science and computational methods. His recent publications address topics like AI-driven network analysis, quantum network percolation, and pandemic-induced healthcare system stress. He actively collaborates on interdisciplinary projects, emphasizing real-world applications of network science principles.

Keke LaiView profile

Associate Professor

• Quantitative Psychology
• Structural Equation Modeling
• Missing Data Analysis
• +5 more

Keke Lai is an Associate Professor of Quantitative Psychology at the University of California, Merced. Previously, she held positions at the University of Houston and Arizona State University. She earned her Ph.D. in Quantitative Psychology from the University of Notre Dame (2012), and a B.A. in English from China (2005). Her research focuses on advanced statistical methodologies in psychology and education, including structural equation modeling (SEM), robust statistical methods, model evaluation, longitudinal/multilevel data analysis, missing data techniques, and Bayesian statistics. She addresses critical gaps in SEM methodology, such as accurate estimation under missing data, nonnormal distributions, and model misspecification. Her recent work emphasizes improving confidence intervals for fit indices (e.g., RMSEA, CFI), comparing nonnested models, and developing methods for standardized parameters in misspecified models. These contributions enhance the reliability of statistical analyses in psychological research. Lai’s publications appear in top journals like Structural Equation Modeling and Psychometrika . She advises graduate students, including X. Zhang. Her website is https://sites.google.com/view/laikeke .

Rajen ShahView profile

Professor

• Statistical Inference
• High-Dimensional Data Analysis
• Machine Learning
• +3 more

Professor Rajen Shah is a faculty member in the Statistical Laboratory at the University of Cambridge, part of the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics. His research focuses on statistical methodology, particularly in high-dimensional data analysis, machine learning, and robust statistical inference. He is known for contributions to areas such as change-point regression, inverse propensity score weighting, and efficient estimation techniques in complex models. Key research interests include developing novel methods for variable selection, robust hypothesis testing, and scalable algorithms for large-scale data. He has collaborated on interdisciplinary projects, such as functional genomics studies (e.g., screening conserved genes of unknown function). His work often emphasizes theoretical rigor alongside practical applications in fields like causal inference and computational statistics. Prof. Shah has published extensively in top-tier journals like The Annals of Statistics , Bernoulli , and Journal of the Royal Statistical Society Series B . His recent articles address challenges in cross-validation for change-point detection, rank-transformed subsampling, and sandwich boosting methods. He is actively involved in the academic community, contributing to the Cambridge Statistics Clinic and supervising research in statistical methodology. His research group is affiliated with the Statistical Laboratory at the Centre for Mathematical Sciences, Cambridge. The lab focuses on advancing statistical theory and applications, with a strong emphasis on high-dimensional and assumption-lean methods.

Antonio PeyracheView profile

Associate Professor

• Applied Economics
• Econometrics
• Productivity Analysis
• +3 more

Associate Professor Antonio Peyrache is a Deputy Head of School at the School of Economics, University of Queensland, within the Faculty of Business, Economics and Law. His research focuses on applied economics, econometrics, and productivity/efficiency analysis with particular emphasis on production systems, public sector efficiency, and systemic risk modeling. He leads the Centre for Efficiency and Productivity Analysis (CEPA), driving advancements in efficiency measurement methodologies. Key research interests include judicial system efficiency, banking systemic risk, and multilevel production networks. Recent work explores homothetic production technologies and optimal organizational structures for public institutions. Featured projects include analyzing productivity in Australian horticulture and European judicial systems. His expertise spans both theoretical contributions (e.g., decomposition frameworks) and applied policy analysis (e.g., healthcare delivery efficiency). Publications span journals like European Journal of Operational Research and Omega, with a focus on operational research techniques and their policy applications. He has directed projects funded by the Asian Productivity Organization and collaborated on EU-KLEMS growth accounting initiatives. Professional roles include editorial contributions and leadership in academic productivity analysis.