Asko Tolvanen is a Professor at the University of Jyväskylä's Faculty of Education and Psychology, specializing in advanced quantitative methodologies like structural equation modeling, multilevel modeling, and mixture modeling. His research focuses on educational and developmental psychology, with particular attention to student-athlete well-being, academic skill progression, and mental health dynamics. He leads multiple research groups including InterLearn (psychological aspects of learning), STAIRWAY (educational transitions), and Winning in the Long Run (sports psychology). Key projects include DETECT (disinformation detection education), AGE-X (aging and physical activity), and SportEX (elite sports gender equality). Publications emphasize longitudinal studies on burnout, motivation, and intervention efficacy in educational and sports contexts. Active in cross-cultural comparisons (e.g., teacher education emotional intelligence studies) and health-related research (e.g., asthma's psychosocial impact). His work bridges statistical rigor with practical applications in education, sports, and public health, supported by collaborative teams and national/international funding.
Dr. Polemnia (Nia) Amazeen is a Professor in the Department of Psychology at Arizona State University (ASU), with cross-disciplinary affiliations including the Biosocial Complexity Initiative, Center for Social Dynamics and Complexity (CSDC), and Institute for Social Science Research. She also serves as a Senior Global Futures Scientist within ASU's Global Futures Scientists and Scholars program. Education: Ph.D. in Center for the Ecological Study of Perception and Action, University of Connecticut, 1996 Postdoctoral Training, Vrije Universiteit - Amsterdam Her research centers on identifying universal mathematical principles of dynamical similitude across diverse behavioral systems. Specializing in dynamical systems and complex systems theory, she develops analytical frameworks for real-time detection of behavioral transitions and anomalies using multiscale data streams (neural, physiological, kinematic, communication). Her work bridges cognitive science, human factors, and artificial intelligence to decode complex human behavior through interdisciplinary lenses including kinesiology and psychology. Analysis of her 15 most recent publications (2002-2024) reveals consistent application of nonlinear dynamics to coordination phenomena across scales—from neural signatures in team cognition to motor-respiratory synchronization. Key trends include fractal/multifractal analysis of behavioral data, pink noise effects on state transitions, and computational modeling of memory foraging. Her work increasingly integrates real-time analytics for human-autonomy teaming applications. Scientific Awards: HFES Jerome H. Ely Award for Best Paper in Human Factors Amazeen has chaired or served on Ph.D. dissertation committees for 14 students including J. G. Holden, J. E. Butner, and A. Likens. Her research is funded by major federal agencies including NSF, ARL, DARPA, ONR, and USAMRMC, supporting investigations into team coordination dynamics and human-system interactions. She leads the Dynamics of Perception, Action, and Cognition (DPAC) research group, which develops analytical dashboards for translating complex behavioral data into actionable insights for military personnel, educators, and scientists through cross-disciplinary collaboration.
Brian Cooper is an Associate Professor in the Department of Management at Monash University's Faculty of Business and Economics. He is an expert in quantitative research methodology in management, with advanced skills in multivariate statistical techniques such as multilevel modeling and structural equation modeling. His research focuses on job attitudes, employee well-being, HRM, and employee voice. He teaches introductory and advanced research methods courses including MGX4000 and MGX4200 and is actively supervising PhD students. Research Interests: Quantitative Research Methodology Human Resource Management and Performance Employee Well-being at Work Job Attitudes and Job Security Employee Voice and Participation Psychosocial Health and Safety in Organizations His recent research outputs demonstrate a consistent focus on organizational behavior, workplace well-being, and the application of advanced statistical methods to survey data. Themes include resistance to change, ethical challenges in supply chains, and adaptability during crises like the pandemic. He frequently collaborates with leading scholars in HRM and organizational psychology. Scientific Awards: LEAD In Asia Conference: Best Paper Award (2018) Dr. Cooper has led and participated in numerous research projects, including those funded by external organizations such as WorkWell and the Australian Retailers Association. His projects often examine mental health in workplaces, OHS performance, and professional well-being in sectors like healthcare and architecture. He is actively involved in grant-funded research and interdisciplinary collaboration. Labs and Research Teams: While no formal lab is mentioned, Dr. Cooper is part of a robust research network at Monash, working closely with colleagues such as Helen De Cieri, Rodger Donohue, and Jane Wolfram Cox. He leads projects on psychosocial health and safety and contributes to studies on architectural work cultures and healthcare worker well-being.
Radhika Raghunathan, PhD, MSPH is an Assistant Research Professor at the Bloomberg School of Public Health at Johns Hopkins University, with departmental affiliations in Mental Health. Her research examines multilevel and longitudinal processes influencing children's social, emotional, and behavioral well-being, and antecedents of mental health disorders. Her educational background includes: PhD from Johns Hopkins Bloomberg School of Public Health (2020) MSPH from Johns Hopkins Bloomberg School of Public Health (2016) BS from University of Michigan (2014) As a public health and developmental scientist, Dr. Raghunathan investigates the role of self-regulation on children's socio-emotional development, school-related outcomes, and developmental antecedents/etiology of mental health disorders. She has developed expertise in advanced statistical methods including latent variable and longitudinal modeling to understand developmental processes at individual and population levels. Her research agenda bridges developmental science with public health to inform prevention programs supporting children and families. Her recent publications demonstrate consistent focus on child mental health, socio-emotional development, and the impact of various factors on children's well-being. The research spans diverse methodologies including longitudinal studies, latent variable analysis, and structural equation modeling, examining developmental trajectories, effects of adversity, and mechanisms of self-regulation in children across different contexts including the pandemic era. Her notable awards include: 2019-2020 Donald A. Cornely Scholar, JHSPH 2018-2019 Bernard and Jane Guyer Scholarship Fund, JHSPH 2016-2020 Maternal and Child Health Training Grant, HRSA Dr. Raghunathan has secured significant research funding, including the Maternal and Child Health Training Grant from HRSA. Her work demonstrates strong interdisciplinary collaboration, particularly with the Johnson lab at Johns Hopkins, focusing on child development, mental health, and the application of advanced statistical methods to understand complex developmental processes. Her research output shows steady productivity with publications spanning 2016-2025, indicating her establishment as an independent researcher in developmental science and public health. Her research activities span multiple interdisciplinary collaborations, with a particular emphasis on understanding how psychophysiological, behavioral, family, and environmental factors interact to shape children's development and mental health outcomes across the lifespan.
Roy Stewart is a Methodologist at the Faculty of Medical Sciences, University of Groningen (UMCG), specializing in advanced statistical methodologies for medical research. With over 180 publications to his name, his work spans multiple medical disciplines including rheumatology, mental health, orthopedics, and transplantation medicine. His methodological expertise is frequently applied in collaborative research projects across the medical center. Dr. Stewart's research focuses on psychometrics, statistics, and probability, with specific expertise in Multilevel/Linear Structural Equation Modeling, LISREL/SEM, and Latent Class Analysis (LCA). His fingerprint analysis reveals significant contributions to Musculoskeletal Pain research, Rehabilitation Engineering, Quality of Life studies, and Symptom assessment. He also serves as a methodologist on the Medisch Ethisch Toetsingscommissie BEBO in Assen. Analysis of his recent publications shows a consistent pattern of methodological contributions to diverse medical fields. His work frequently involves designing and analyzing complex longitudinal studies and randomized controlled trials. He has particular expertise in psychometric scale development and validation, as evidenced by his work on the Knowledge and Attitudes to Mental Health Scales. His collaborations span multiple departments and international institutions, demonstrating the interdisciplinary nature of his methodological contributions. Dr. Stewart maintains an active research profile with numerous recent publications in high-impact journals across multiple medical specialties. His work consistently applies rigorous statistical methodology to address clinically relevant questions, serving as a bridge between advanced statistical techniques and practical medical research applications.
Pedro Simões Coelho is a Full Professor and President of the Scientific Board at NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, where he also serves as an Integrated Researcher in the Information Management Research Center (MagIC). He is a senior expert for the European Commission in statistical methods and sampling techniques, and holds leadership roles in several national and European bodies, including the European Master of Official Statistics (EMOS) board and the Portuguese Health Technologies Commission. His research interests lie at the intersection of statistics, data analysis, and public policy. He specializes in sampling techniques, structural equation modeling, survey methodology, and data quality, with applications in health economics, business intelligence, and official statistics. His work bridges theoretical statistical innovation with real-world policy impact, particularly in health and administrative systems. His recent publications demonstrate a strong trend toward integrating artificial intelligence and advanced modeling techniques into public administration and health policy. Topics include AI-based detection of legislative burdens, economic burden assessments in dermatology, consumer segmentation via social media, and cardiovascular health optimization. These works reflect a multidisciplinary approach combining statistics, machine learning, and domain-specific knowledge to solve complex societal challenges. Senior Expert, European Commission (Statistical Methods & Sampling) Member, EMOS Board Member, Ischools Accreditation Committee Head of Information and Statistics, NOVA Clinical Research Unit (NOVA CRU) Former President, Portuguese Association for Classification and Data Analysis (CLAD) Former Member, Portuguese High Council for Statistics (CSE) Former President, Fiscal Board, CESD-Lisboa He has supervised numerous graduate and undergraduate courses and has been a consultant and trainer for major institutions including Eurostat, the Portuguese Statistical Office, and the Portuguese Central Bank. His work includes over 100 peer-reviewed publications and around 200 research projects resulting in more than 500 reports. He has delivered nearly 100 invited talks and conference presentations worldwide. He is actively involved in research labs and teams such as MagIC and NOVA CRU, where he leads initiatives in data-driven public health and statistical innovation. His ongoing projects focus on AI for policy assessment, sustainable health systems, and advanced statistical modeling for small area estimation and data integration.
Akihito Kamata is a Professor at Southern Methodist University (SMU) in the Department of Education Policy & Leadership (Simmons School of Education & Human Development) and Department of Psychology (Dedman College of Humanities and Sciences). He also directs the Quantitative Methods Lab (QML) at SMU's Center on Research and Evaluation (CORE). Academic Rank: Professor Education: Ph.D. in Measurement and Quantitative Methods from Michigan State University (1998) Dr. Kamata's research focuses on psychometrics and educational/psychological measurement, with expertise in item response theory , multilevel modeling , and structural equation modeling . His work includes model development for oral reading fluency (ORF) assessment data, supported by three IES grants from the U.S. Department of Education. Recent publications emphasize Bayesian approaches to ORF scoring, count data modeling , and longitudinal analysis of student development. He has contributed to advanced psychometrics through special issues and book chapters, including the Handbook of Advanced Multilevel Analysis (2011). Scientific Awards: Michigan State University Distinguished Alumni Award (2022) Students and Collaborators: Current Ph.D. Students: Patrick Lan, Nancy Le, Kuo Wang, Ji Li Former Ph.D. Students: Hao Ma Collaborators: Yusuf Kara (Senior Data Analyst), Xinya Liang (Associate Professor), Chalie Patarapichayatham (Research Assistant Professor), Cornelis Potgieter (Assistant Professor), Xin Qiao (Post-Doctoral Fellow)
Dr. Gianmaria Bottoni is a Research Fellow at City, University of London , affiliated with the ESS ERIC HQ. He has held prior roles as a Researcher and Teaching Assistant at the University of Salerno and Molise, and was a Visiting Researcher at the University of Utrecht's Department of Methodology and Statistics. He also serves as a Lecturer at the Summer School on Methods of Social Sciences , recognized by the Italian Sociological Association. University: City, University of London Academic Role: Research Fellow Additional Roles: Lecturer (Summer School), Visiting Researcher His research focuses on cross-national comparative studies , social cohesion , quality of life , and advanced quantitative methods including multilevel modeling, structural equation modeling (SEM), and multilevel SEM. His methodological expertise informs both his research and teaching, with a particular emphasis on survey design, digital data collection, and comparative analysis across cultural contexts. Recent publications highlight his work on cross-national web surveys (CRONOS-2), social cohesion frameworks (2024-2018), and digital addictions (2023). Articles span topics like e-campaigning metrics, streaming service consumer behavior, and multilevel measurement models, reflecting interdisciplinary applications of statistical tools in social sciences. Dr. Bottoni contributes to EU-funded projects, notably as a Work Package Leader for the H2020 SERISS initiative, and has developed open science infrastructures for cross-national surveys.
Yan Wang is an Associate Professor in the Department of Psychology at the College of Fine Arts, Humanities and Social Sciences, University of Massachusetts Lowell, specializing in advanced quantitative methods for psychological research. Her expertise bridges statistical methodology and applied behavioral sciences through innovative modeling techniques. Her educational trajectory demonstrates interdisciplinary rigor: Ph.D. in Measurement & Research (2018), University of South Florida, with supporting area in Educational Psychology; dissertation: "Covariates in factor mixture modeling: Investigating measurement invariance across unobserved groups" M.A. in Curriculum and Instruction (2013), Boston College Dual B.A. in English Literature and B.S. in Economics (2011), Xiamen University, China Dr. Wang's research centers on quantitative psychology with pioneering work in finite mixture modeling, structural equation modeling, multilevel modeling, and Bayesian analysis . Her methodological innovations address critical challenges in measurement invariance testing, latent variable modeling, and longitudinal data analysis, significantly advancing psychometric theory while enabling robust applications across educational, clinical, and social domains. This dual focus on methodological rigor and practical implementation defines her scholarly impact. Analysis of her 15 most recent publications reveals a distinctive pattern: she consistently develops and validates statistical techniques through Monte Carlo simulations before applying them to substantive problems in cognitive aging, traumatic brain injury outcomes, pediatric injury prevention, and cross-cultural psychopathology. Her signature approach combines simulation-based method evaluation with real-world applications, particularly in educational measurement and developmental psychology contexts. Her scholarly excellence has been recognized through prestigious awards: Teaching Excellence Award (2022) from University of Massachusetts Lowell Leslie C. Robbins Dean’s Excellence Award (2016) from University of South Florida Conference Presentation Travel Grant (2015) from SouthEast SAS Users Group University Graduate Fellowship Award (2013) from University of South Florida Dean’s Scholarship (2011) from Boston College National Scholarship (2008) from Xiamen University As Co-Investigator on the NIH-funded "Virtual Reality-based Rehabilitation for Children with Traumatic Brain Injuries" grant (Eunice Kennedy Shriver NICHD, 2020), she provides critical statistical leadership for clinical translation. Her collaborative publications with graduate students indicate active mentorship in quantitative methods, though formal advisee lists aren't documented. Dr. Wang maintains strong professional engagement through presentations at major conferences including AERA, APA, and Florida Educational Research Association, establishing herself as a leading voice in quantitative methodology development.
Mårten Schultzberg is a Researcher at the Department of Statistics at Uppsala University , Sweden. His work spans causal inference, experimental design, and applied statistics, with a focus on methodological advancements and interdisciplinary applications in energy economics and behavioral science. Research Interests : Causal inference in observational and experimental studies Optimal experimental design and rerandomization strategies Statistical methods for energy demand analysis and behavioral modeling Recent Publications highlight his contributions to balancing covariates in randomized experiments, causal mediation analysis, and energy policy evaluation. His work appears in journals such as Journal of Causal Inference , Statistical Papers , and Energy Research & Social Science . Contact: marten.schultzberg@statistik.uu.se
Nicholas C. Jacobson is an Associate Professor of Biomedical Data Science and Psychiatry at the Geisel School of Medicine, Dartmouth College. He serves as the Director of the Treatment Development & Evaluation Core within the Center for Technology and Behavioral Health (CTBH) and leads the AI and Mental Health: Innovation in Technology Guided Healthcare (AIM HIGH) Laboratory. His work bridges computational methods with clinical applications to transform mental healthcare through technology. Dr. Jacobson earned his PhD in Psychology from Pennsylvania State University in 2019, following an MSc in Psychology from the same institution in 2015. He completed his Postdoctoral and Clinical Fellowships in Psychology at Massachusetts General Hospital/Harvard Medical School in 2019. Dr. Jacobson's research focuses on harnessing artificial intelligence and passive sensor data from smartphones and wearable devices to develop scalable, personalized interventions for anxiety and depression. His work has three main pillars: (1) enhancing precision assessment of anxiety and depression using intensive longitudinal data, (2) conducting multimethod assessment utilizing passive sensor data from smartphones and wearable devices, and (3) providing scalable, personalized technology-based treatments utilizing smartphones. As a computational psychologist, he created the Differential Time-Varying Effect Model (DTVEM), an innovative statistical package in R that allows researchers to discover and model optimal lag times in intensive longitudinal data. His methodological expertise encompasses machine learning, structural equation modeling, multilevel modeling, time-series techniques, and dynamical systems modeling. His recent publications demonstrate a strong focus on digital phenotyping, machine learning applications in mental health, and personalized interventions. The research spans multiple domains including depression symptom networks, anxiety disorder assessment, eating disorder prevention, and the use of passive sensing to understand mental health conditions. A notable trend is the application of advanced computational methods to create more precise and personalized mental health assessments and interventions, with increasing emphasis on real-world implementation and accessibility. Principal Investigator of an R01 Award from the National Institute of Mental Health studying personalized deep learning models to predict rapid changes in major depressive disorder symptoms Secured over $6 million in funding as Principal Investigator and over $20 million as a co-Investigator Featured on NBC Nightly News and CBS Morning News for pioneering work in AI-powered mental health applications Dr. Jacobson has developed several impactful digital tools including Therabot, a generative AI therapy chatbot that demonstrated substantial reductions in symptoms of major depressive disorder, generalized anxiety disorder, and feeding and eating disorders in its first randomized controlled trial. He also developed Mood Triggers, a smartphone sensing platform that integrates ecological momentary assessment and intervention to help users identify and manage anxiety and depression triggers. His suite of smartphone applications has reached over 50,000 users in more than 100 countries. Dr. Jacobson is actively recruiting team members and encourages interested individuals to contact him through his personal website. He directs the AIM HIGH Laboratory, which focuses on advancing AI applications in mental healthcare. The lab develops innovative computational approaches to enhance mental health assessment and treatment through technology. Current projects include using passive sensor data to predict symptom changes, developing personalized just-in-time adaptive interventions, and creating quantitative tools that enable precision mental healthcare.
Ricardo Javier Principe Rubio is a Researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona East School of Engineering (EEBE) and the Department of Fluid Mechanics. He is a key member of the ANiComp (Numerical Analysis and Scientific Computing) and (MC)² (Computational Mechanics in Continuous Media) research groups. His work bridges high-performance computing, fluid dynamics, and numerical methods, with applications in fusion technology, environmental engineering, and nanomaterials. His research focuses on advanced computational techniques, including finite element methods, uncertainty quantification, and parallel algorithms for large-scale simulations. Recent projects involve anisotropic mesh adaptation, multilevel Monte Carlo methods, and stabilized formulations for multiphase flows. Principe actively contributes to UPC's scientific software ecosystem, notably through the FEMPAR framework for parallel finite element modeling. Awards include the Premi Extraordinari de doctorat 2010 for outstanding doctoral research. He leads/participates in competitive R&D projects funded by Catalan and EU programs, such as EXAscale Quantification of Uncertainties for Technology and Science Simulation (EXAQUAT). Collaborative networks span Barcelona Supercomputing Center and international consortia.
Peter Miksza is an Associate Professor of Music Education at the Indiana University Jacobs School of Music and an affiliate member of the Indiana University Cognitive Science Program. He teaches courses in instrumental music education, the psychology of music, learning processes in music, measurement and assessment in music, and research methods. His research interests focus on: Self-regulated music learning Musical skill acquisition Music teacher preparation Music education policy Dr. Miksza co-authored the influential text 'Design and Analysis for Quantitative Research in Music Education' (2018) with Kenneth Elpus, which has been praised as 'a model of research thinking' and 'unique in its thoroughness' by reviewers. The book provides music education researchers with sophisticated quantitative methods previously uncommon in the field, covering everything from basic descriptive statistics to advanced techniques like structural equation modeling and multilevel analysis. His scholarly contributions help bridge the gap between rigorous research methodology and practical music education applications, enabling evidence-based decision making in music teaching and learning.
Jessica Gipson is Professor and Fred H. Bixby Chair of Population and Reproductive Health in the Department of Community Health Sciences at UCLA's Fielding School of Public Health, where she directs the UCLA Bixby Center to Advance Sexual & Reproductive Health Equity. Her educational background includes a PhD in Public Health (Johns Hopkins Bloomberg School), MPH (Tulane School of Public Health), and BS in Anthropology (UCLA). Dr. Gipson's research employs mixed-methods to investigate reproductive health in lower-income settings globally, with core focus on fertility preferences, contraceptive use, unintended pregnancy, and abortion. She examines how gender norms and socio-cultural contexts shape reproductive decision-making among couples across the Philippines, Malawi, Bangladesh, China, and the United States, emphasizing women's empowerment and health equity. Her publication trends reveal consistent leadership in global reproductive health research, particularly in methodological innovation for studying couple dynamics and culturally responsive interventions in resource-limited settings. Recent work emphasizes male involvement in reproductive health and context-specific solutions for maternal mortality reduction. Scientific recognitions include: Fred H. Bixby Chair of Population and Reproductive Health IJGO Editor Top Pick (2017) for research on male roles in abortion decision-making As Bixby Center Director, she leads initiatives translating research into policy, exemplified by her Tibetan Birth Center project in western China. This community-driven intervention addressed transportation, cultural, and linguistic barriers to maternal care through a three-tiered approach (county, community, individual), achieving 98% patient satisfaction and demonstrating how culturally competent care improves service utilization in marginalized populations.
Alison Ramage is a Reader in Applied and Industrial Mathematics at the University of Strathclyde's Department of Mathematics and Statistics. Her research focuses on numerical linear algebra, preconditioning techniques for partial differential equations, and applications in liquid crystal modeling, geotechnical engineering, and data assimilation. She holds prestigious fellowships from the Leverhulme Trust and EPSRC, and has led numerous grants and collaborations with industry partners like Hewlett-Packard and Oasys Ltd. Education: PhD in Preconditioned Conjugate Gradient Methods from the University of Bristol (1991), BSc from the University of St Andrews (1987). Research Interests : Numerical Linear Algebra, Scientific Computing, Preconditioning, Liquid Crystals, Data Assimilation, Computational Fluid Dynamics. Her work bridges theoretical mathematics with practical industrial applications, emphasizing efficient algorithms and iterative solvers. Grants and Awards : Multiple EPSRC grants, including a £43K Leverhulme Fellowship (2017) for data assimilation research. Active in editorial roles for SIAM journals and professional societies like SIAM and EMS. Academic Service : Long-standing roles in SIAM, including Board of Trustees and editorial boards. Organized international conferences and workshops on numerical analysis and applied mathematics. Labs/Teams : Co-developed the IFISS software package for incompressible flow simulations, collaborating with global researchers in numerical methods and data science.
