Dr. Muirne Paap is an Associate Professor with Ius Promovendi at the Faculty of Behavioural and Social Sciences, University of Groningen. She specializes in psychometrics, with expertise in test theory, item response theory, and computerized adaptive testing. Her research focuses on enhancing clinical decision-making through advanced measurement methodologies. Education: PhD in Psychiatry from the University of Oslo (2011), MSc in Clinical Psychology and Psychometrics from the University of Groningen (2006, cum laude). Research Interests: Development of reliable clinical assessment tools Computerized adaptive testing (CAT) applications in healthcare Measurement of personality disorders and quality of life Notable Projects: SEALS Project (2024–2028): Methodology for progressive tests in education PersoniCAT Project (2019–2022): Adaptive diagnostic interviews for personality pathology Awards: FRIPRO Young Research Talents Grant (2018) Member of the Young Academy Groningen (2021) Teaching: Test theory, item response theory, and applied statistics at undergraduate and master's levels. Collaborations: Extensive international partnerships, including with Oslo University Hospital, Harvard Medical School, and the Netherlands Cancer Institute.
Dr. Yunxiao Chen is an Associate Professor in the Department of Statistics at the London School of Economics and Political Science (LSE), where he co-leads a psychometric lab with Professor Irini Moustaki. Previously, he was an Assistant Professor at Emory University (2016–2018) and earned his PhD in Statistics from Columbia University (2016). His research focuses on developing statistical and computational methods for social data science, addressing challenges in high-dimensional data analysis, latent variable models, and educational assessment. Education: PhD in Statistics, Columbia University, 2016 Research Interests: High-dimensional factor models (matrices, tensors, counting processes) Dynamic behavioral data analysis Sequential decision theory in personalized learning Statistical inference for large-scale item response data Applications in education, psychology, and marketing Publications: Recent work includes advancements in factor analysis, change-point detection, and DIF statistical inference Key journals: Journal of the American Statistical Association , Psychometrika , Journal of Machine Learning Research Awards: 2024 Psychometrics Society Best Reviewer Award 2022 Early Career Award 2018 NCME Loyd Dissertation Award Advising & Grants: Accepts PhD students in statistical methodology Funded by National Academy of Education/Spencer Fellowship (2018–2020) and IEA R&D grants (2022–2023) Labs & Teams: Runs LSE’s psychometric lab focused on educational measurement Collaborates with interdisciplinary teams on machine learning applications
Dr. Sanford R. Student is an Assistant Professor in the School of Education at the University of Delaware and a Resident Faculty member of the Data Science Institute. His research focuses on connecting psychometric methodologies with practical educational implications, particularly in academic growth measurement, large-scale science assessments, and instrument design. He holds a Ph.D. in Research and Evaluation Methodology from the University of Colorado Boulder (2023) and dual B.A.s in Philosophy and Computer Science from Brown University (2013). Dr. Student’s professional experience includes roles as a Research Associate at Lyons Assessment Consulting (2021–2023), Doctoral Researcher at the Center for Assessment Design, Research and Evaluation (2018–2023), and Lead Researcher for the American Bar Association’s Bar Exam study (2019–2020). Prior to academia, he worked as a Software Engineer at edX (2016–2018). His awards include selection for the 2020 AIR/NCES NAEP Data Training Workshop. Current research trends in his publications emphasize Bayesian methods, vertical scaling, and crosscutting concepts in science education. He actively contributes to educational policy discussions through work with state agencies and assessment developers. Dr. Student advises graduate students and collaborates on grants related to growth measurement and educational data systems. He is affiliated with the National Council on Measurement in Education and maintains a lab focusing on applied psychometric challenges in K-12 systems.
Meltem ACAR GÜVENDİR is an Associate Professor in the Department of Educational Sciences at Trakya University's Faculty of Education. She holds a PhD in Measurement and Evaluation in Education from Ankara University (2013) and has conducted postdoctoral research at the University of California, Los Angeles (UCLA). Her research focuses on large-scale educational assessment, including analyses of PISA, TIMSS, and OBBS datasets using hierarchical linear modeling. She specializes in value-added assessment, meta-analyses of study abroad impacts, and psychometric evaluation of educational policies. Academic roles include serving as Head of the Measurement and Evaluation Commission (since 2018) and Deputy Head of the Department of Educational Sciences (2016–2018). She co-edits the International Journal of Assessment Tools in Education and has contributed to over 60 peer-reviewed articles and book chapters. Her work explores student achievement predictors, test anxiety measurement, and international student integration. Key projects include a TÜBİTAK-funded study on scale development processes and participation in the EU-funded CA19103 initiative addressing LGBTI+ inequalities. She has supervised numerous research projects and delivered keynotes on educational measurement methodologies.
Scott Morris is a Professor and Nambury S. Raju Endowed Chair in Psychology at Illinois Institute of Technology (IIT), leading the Industrial/Organizational Psychology Program within the Lewis College of Science and Letters. He holds a Ph.D. from the University of Akron (1994) and a B.A. from the University of Northern Iowa (1987). A Fellow of the Society for Industrial and Organizational Psychology and the American Psychological Association, Morris is also an associate editor for the Journal of Applied Psychology . His research focuses on advanced statistical methods in personnel selection systems, including meta-analysis, adverse impact analysis, and computer adaptive testing. He explores bias in subjective hiring practices and develops psychometric models to ensure fairness in employment decisions. Morris’s work bridges quantitative methodology with real-world applications, emphasizing ethical and equitable employment practices. Key research areas include adverse impact measurement, meta-analytic techniques, differential item functioning, and the design of efficient testing systems. He co-authored Adverse Impact Analysis: Understanding Data, Statistics and Risk (2017), a foundational text in the field. Morris’s lab, the Personnel Selection & Analytics Lab , investigates topics like multidimensional adaptive testing and statistical methodologies for EEO compliance. Awards: Fellowships from SIOP and APA, recognition for contributions to organizational psychology and statistical rigor. Media Expertise: Regularly consulted on psychology, data science, and ethics in employment. Labs/Teams: Leads the IIT Personnel Selection & Analytics Lab, focusing on quantitative methods in HR and equity analysis.
Soyeon Ahn is a full-time Professor in the Department of Education and Psychological Studies at the University of Miami's School of Education and Human Development. Her academic profile demonstrates active research leadership in health communication, educational assessment, and psychometrics, with recent publications spanning 2024-2025. Contact details include email s.ahn@miami.edu, phone (305)284-2929, and ORCiD 0000-0003-2581-306X. Research interests focus on Health Belief Model applications in social media health campaigns, fairness evaluation of high-stakes educational exams, and eHealth interventions for obesity management. She investigates how content creator characteristics (e.g., race, occupation) affect user engagement with health messages, examines psychometric validation of assessment tools via Rasch modeling, and explores generative AI's pedagogical impact. Her work integrates intersectionality frameworks to address health disparities in clinical settings. Recent publications reveal strong interdisciplinary trends connecting public health, education, and technology. Key themes include methodological rigor in meta-analyses of occupational cancer studies, social media's role in vaccine behavior change, and AI literacy training for educators. Her findings consistently emphasize practical implications: optimizing health messaging through HBM constructs, addressing test bias in educational equity, and leveraging technology for behavior intervention. The 2024-2025 output shows increasing focus on real-world application of measurement theory.
Julie Bugg is a Professor of Psychological & Brain Sciences at Washington University in St. Louis, where she also serves as the Director of Graduate Studies. She is the principal investigator of the Cognitive Control & Aging Lab, which conducts research on cognitive control, aging, and memory. Her work is supported by funding from the National Institute on Aging (NIA). Education: PhD, Colorado State University MS, Colorado State University BA, Bloomsburg University of Pennsylvania Her research focuses on cognitive control mechanisms across the lifespan, particularly how aging affects attention, memory, and executive function. She investigates interference resolution in tasks like the Stroop and Eriksen flanker, distinguishing between list-level, contextual, and item-specific control. A major focus is how normal aging and early Alzheimer's disease differentially impact these processes. She also studies prospective memory —remembering future intentions—and the risks of commission errors, especially in older adults. Another active line explores cognitive training and physical exercise as interventions to preserve cognitive function in aging, including impacts on eHealth literacy. The published articles reveal a strong focus on cognitive control dynamics , with recurring themes in top-down regulation , conflict adaptation , and aging-related cognitive decline . Her work integrates behavioral paradigms with real-world applications, especially in improving health decision-making among older adults. Several studies use pupillometry to assess cognitive effort and awareness. Scientific Contributions: Developed methods to isolate levels of cognitive control Investigated mechanisms underlying commission errors in prospective memory Explored exercise as a neuroprotective factor in aging Examined eHealth literacy outcomes in cognitive training programs Julie Bugg actively mentors students, including undergraduates involved in her research. She has led NIA-funded projects on cognitive training and aging. Her lab investigates translational strategies to support cognitive health in older adults, with implications for public health and clinical intervention. Laboratory and Research Team: She leads the Cognitive Control & Aging Lab at Washington University, which conducts multi-faceted research on cognitive aging, memory, and executive function. The lab uses behavioral experiments, neuropsychological assessments, and physiological measures like pupillometry to explore cognitive mechanisms.
Daphna Harel (she/her) serves as Associate Professor of Applied Statistics and Director of the A3SR MS Program within the Department of Applied Statistics, Social Science, and Humanities at New York University's Steinhardt School of Culture, Education, and Human Development. She holds leadership roles including PI of the NYU QUEER data lab and membership on the steering committee for the DEPRESSD project. Education: PhD in Mathematics and Statistics from McGill University Harel's research focuses on measurement challenges in survey methodology, particularly for self-reported questionnaires in health and social sciences. Her work bridges theoretical statistics with practical applications in LGBTQIA+ data collection, differential item functioning, and patient-reported outcomes. She develops methodological frameworks for polytomous Item Response Theory and creates guidelines for statistical analysis of complex survey data. Her recent work emphasizes improving statistical practice for LGBTQIA+ populations and advancing queer data collection methods. Her publication portfolio demonstrates consistent focus on measurement validity across diverse health contexts, with recent work expanding into LGBTQIA+ data science. Her research shows increasing emphasis on methodological innovations for gender and sexuality measurement, with multiple publications presented at major conferences including AAPOR 2024 and LGBTQ+ Health Conference 2024. Research Leadership: PI of NYU QUEER data lab (founded Fall 2022) Steering committee member for DEPRESSD project Collaborator with Scleroderma Patient-centered Intervention Network Recipient of NIH R21 funding and NYU intramural grants Harel mentors a diverse team of graduate students through the Applied Statistics for Social Science Research program, focusing on inclusive research methods for marginalized populations. Her grant portfolio includes investigations into transgender voice training, LGBTQIA+ survey methodology, and healthcare access for gender-affirming services. Laboratory Leadership: As founder and PI of the NYU QUEER data lab, Harel leads research projects examining survey question design for gender/sexuality measurement, content moderation effects on hate speech, and accessibility of transgender voice training. The lab operates through collaboration between NYU Steinhardt and the NYU BITS lab, with research assistants drawn from the Applied Statistics MS program.
Jinming Zhang is a Professor at the University of Illinois at Urbana-Champaign , affiliated with the College of Education and the Educational Psychology department. He also holds appointments in Statistics and the Center for East Asian and Pacific Studies . His research focuses on advanced statistical methodologies for educational and psychological measurement. Research Interests: Dr. Zhang specializes in multidimensional item response theory (MIRT) , dimensionality assessment , large-scale assessments , generalizability theory , and test security . His work addresses critical challenges in psychometric modeling, including bias correction, item compromise detection, and standards alignment for English Language Learners (ELL). Notable Contributions: He developed the DETECT procedure for dimensionality analysis and pioneered real-time item monitoring systems for computerized adaptive testing (CAT) security. His empirical studies span applications to the National Assessment of Educational Progress (NAEP) and Law School Admission Test (LSAT) analysis.
Patricia Martinkova is an Associate Professor at the Faculty of Education, Charles University , a Senior Researcher leading the Department of Statistical Modelling at the Institute of Computer Science, Czech Academy of Sciences , and an Affiliate Associate Professor at the University of Washington (Statistics and Social Sciences). She is also the founder of the Computational Psychometrics Group and the Center for Educational Measurement and Psychometrics at Charles University. Her research focuses on advanced psychometric models and estimators for granular insights in education, psychology, and health, with emphasis on inter-rater reliability , differential item functioning (DIF) , and reproducible research via tools like ShinyItemAnalysis . She has developed software packages ( difNLR , SIAmodules , SIAtools ) and authored the book Computational Aspects of Psychometric Methods. With R (2023). Recent projects include the 2025–2027 EduCoDe (Czech Science Foundation) and 2024–2028 Digital Technologies and Wellbeing (EU-funded). She has received recognition as a Fulbright Alumna (2013–2015) and organized the IMPS 2024 conference (570+ participants). Teaching includes courses on Statistical Methods in Psychometrics and Item Response Theory , incorporating active learning and R-based tools.
Mark Lubell is a Professor of Environmental Science and Policy at the University of California, Davis, and Director of the Center for Environmental Policy and Behavior. His work focuses on cooperation problems and decision-making in environmental, agricultural, and public policy contexts, particularly addressing the Tragedy of the Commons. He applies social science theory to provide actionable policy recommendations for real-world governance challenges. Dr. Lubell’s research emphasizes polycentric governance systems, climate adaptation, and the intersection of environmental science with policy implementation. He explores how institutions and networks influence environmental decision-making, with a focus on California’s agricultural and water management systems. His recent work examines barriers to sustainable practices, collective action dynamics, and the role of knowledge in governance reform. Notable research areas include sea-level rise adaptation strategies in the San Francisco Bay Area, sustainable nitrogen management practices, and the governance of invasive species. Lubell’s studies often highlight the importance of institutional design and interdependence among actors in achieving environmental and social objectives. His academic contributions span over two decades, with recent publications addressing topics like climate change education equity, unintended consequences of policy communication tools, and the role of technical advisors in agricultural sustainability. Lubell’s work bridges theoretical frameworks with practical applications, aiming to enhance resilience in socio-ecological systems through improved governance structures.
Dr. Chia-Wen Chen is a Psychometrician at the Psychometrics Centre within the Cambridge Judge Business School , specializing in Executive Education. Originally from Taiwan, he holds a PhD in Psychometrics from the Education University of Hong Kong (2018), preceded by a master's degree in Psychology from National Chung Cheng University (2012). His career spans institutions in Taiwan, Hong Kong, Norway, and the UK, including postdoctoral research at the University of Oslo's Centre for Educational Measurement (2019-2023). PhD in Psychometrics (2018), Education University of Hong Kong MSc in Psychology (2012), National Chung Cheng University Postdoctoral Researcher (2019-2023), University of Oslo Chia-Wen's research focuses on Item Response Theory (IRT) models for forced-choice and compositional items , with applications in computerized adaptive testing (CAT) , differential item functioning (DIF) , and multilevel modeling . His work includes developing novel IRT methods for ranking items and Most-Least formats, addressing reliability/validity in educational scales like the Principal Instructional Management Rating Scale (PIMRS), and analyzing large-scale datasets (PISA) for cross-national educational insights. Recent publications demonstrate expertise in ipsative testing , online parameter estimation , and factor mixture modeling for educational diagnostics. He has also contributed to methodologies for CAT algorithms in educational evaluation and fault line analysis in school leadership teams.
Dr. Yusuf Ransome serves as an Associate Professor of Public Health in the Social and Behavioral Sciences department at Yale School of Public Health. He is the Founder and Director of the Society, Connectedness and Health (SOCAH) Lab, where he leads innovative research at the intersection of social connectedness, contemplative practices, and digital technology to address complex public health challenges. His work focuses on creating sustainable pathways to health, wealth, and prosperity for historically underserved communities both in the United States and globally. Dr. Ransome's research integrates social connectedness, contemplative practices, and digital technology to address complex public health challenges. His current priority research areas include: building social connectedness to buffer mental health and improve community resilience; strengthening faith-based infrastructure to improve community health and economic well-being; developing interventions that integrate contemplative practices, belief systems, and artificial intelligence to strengthen well-being; and leveraging the science of strengths through portable technologies to optimize well-being. His methodologies combine advanced quantitative survey analysis, in-depth qualitative research through interviews and focus groups, geospatial data science applications, and artificial intelligence tools for public health solutions. His research has been featured in top-tier public health journals including the American Journal of Public Health and Social Science & Medicine. Dr. Ransome's commitment to accessibility ensures his findings reach beyond academia through community engagement events, digital storytelling initiatives, and practical well-being products that empower communities to thrive. His work demonstrates clear trends across social determinants of health, HIV prevention, mental health, and the role of religion and spirituality in public health, with increasing emphasis on commercial determinants of health and advanced methodological approaches. Public Voices Fellowship with Yale and The OpEd Project (2024) Office of Health Equity Research (OHER) Award (2023) Dr. Ransome brings extensive academic training to his work, including an Alonzo Smythe Yerby Postdoctoral Fellowship from Harvard T.H. Chan School of Public Health, a DrPH in Public Health from Columbia University Mailman School of Public Health, an MPH in Health Behavior & Health Education from the University of Michigan School of Public Health, and a BS in Business Management and Science from Brooklyn College. His work addresses critical social determinants of health through innovative research-to-practice frameworks, providing actionable insights for policymakers, community organizations, faith-based institutions, government entities, and healthcare systems. The SOCAH Lab, which he directs, employs a multidisciplinary team that applies advanced research methodologies to real-world public health challenges.
Thomas Suesse is a Senior Lecturer at the School of Mathematics and Applied Statistics, University of Wollongong, with a career spanning institutions in Germany, New Zealand, and Australia. His academic journey includes a M.Sc. in Mathematics from Friedrich-Schiller-University Jena (2003) and a PhD in Statistics from Victoria University of Wellington (2008). Education: M.Sc. in Mathematics (FSU Jena, 2003), PhD in Statistics (VUW, 2008) His research focuses on Statistics, particularly Categorical Data Analysis, Social Network Modeling, and Spatial Statistics. Recent work explores variational Bayes inference for spatial autoregressive models, environmental extreme value detection algorithms, and the impact of missing data in statistical models. His publications span topics in public health, environmental science, and educational methodology. Key trends in his 15 most recent articles (2021-2025) include advancements in spatial statistics, applications of social network analysis to finance, and educational research comparing laboratory teaching objectives. He has also contributed to understanding pandemic effects on child health and activity patterns. Scientific Awards: 2008 VUW PhD thesis submission award; 2005-2008 VUW Postgraduate Scholarship. Thomas co-supervises PhD students on topics like spatial autoregressive modeling with missing data and variance estimation in mixture models. He has secured internal grants for computational infrastructure upgrades and studies on child movement behaviors. His professional affiliations include the Statistical Society of Australia.
Francesca Pia Vantaggiato is a Senior Lecturer in Public Policy at King's College London, affiliated with the School of Politics & Economics and the Department of Political Economy. Her research focuses on collective action problems, bureaucratic politics, and environmental/energy policy within multilevel governance systems. She holds a PhD in Political Science from the University of East Anglia and previously worked at the Florence School of Regulation (European University Institute) and the Centre for Competition Policy. Research Interests: Environmental Policy and Climate Adaptation Energy Policy and Regulatory Networks Polycentric Governance and Policy Subsystems Bureaucratic Dynamics and EU Institutions Recent Work Themes: Her publications analyze governance challenges in climate adaptation (e.g., sea-level rise), EU regulatory networks, and the interplay between institutional structures and policy outcomes. Key topics include porous bureaucracies, functional differentiation in governance networks, and scale dependencies in climate policy. Labs/Teams: She contributes to interdisciplinary groups like the Quantitative Political Economy Research Group , King's Climate Research Hub , and Public Policy and Regulation Research Group .
