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Chun WangView profile

Professor

• Quantitative Research Methods
• Item Response Theory (IRT)
• Computerized Adaptive Testing (CAT)
• +3 more

Chun Wang is a Professor in the Department of Measurement & Statistics at the University of Washington's College of Education. His research focuses on advancing quantitative methods in educational and psychological measurement, with expertise in item response theory (IRT), computerized adaptive testing (CAT), and cognitive diagnostic modeling. He holds affiliate faculty status at the Center for Statistics and the Social Sciences. Education: B.S. in Psychology, Peking University (China) M.S. and Ph.D. in Quantitative Psychology, University of Illinois at Urbana-Champaign Research Interests: Development and validation of multidimensional/mixture IRT models Computerized adaptive testing optimization Cognitive diagnostic modeling for classroom applications Health measurement and bias detection in assessments Recent Trends in Articles: His work bridges statistical innovation with practical applications, emphasizing fairness and efficiency in assessments. Notable areas include: - Healthcare : Predictive models for discharge disposition and functional outcomes - Education Technology : Adaptive learning systems and diagnostic feedback mechanisms - Methodology : Bias detection (DIF), Bayesian estimation techniques, and computational efficiency Scientific Awards : Includes the Anne Anastasi Award (2020), McKnight Presidential Fellowship (2017), and multiple best reviewer recognitions from leading psychometrics journals. Advising & Grants: Supervised students including Xiao J., Zhu R., and Lu J.* in high-impact projects. Co-led a $10M NIH grant (AmplifyGAIN Center) to advance Gen AI in STEM education. Published extensively in Psychometrika , Journal of Educational and Behavioral Statistics , and other top outlets. Labs/Teams: Directs the Pmetrics Lab ( https://sites.uw.edu/pmetrics/ ), collaborating on cutting-edge measurement tools for education and healthcare.

Katherine ThorsonView profile

Assistant Professor

• Interpersonal processes
• Intergroup relations
• Emotion
• +9 more

Katherine Thorson is an Assistant Professor at Barnard College , affiliated with the Department of Psychology . Her research focuses on interpersonal interactions , intergroup dynamics , and physiological responses in social contexts. B.A. in Psychology from Bates College M.A. and Ph.D. in Psychology from New York University Thorson’s work explores how stereotype threat affects gender dynamics in STEM, physiological synchrony in romantic and professional settings, and methodological innovations in dyadic and longitudinal data analysis. Her research often bridges social psychology and health outcomes . Recent trends in her publications include studies on intellectual humility in guided conversations, physiological linkage in diverse contexts (e.g., doctor-patient interactions, group therapy), and mindfulness in social engagement. Society for Experimental Social Psychology Dissertation Award finalist Funded by the Society for the Psychological Study of Social Issues She applies dyadic data analysis and multilevel modeling to examine how social environments shape psychological experiences and physiological outcomes .

• Psychometrics
• Network Theory
• Psychopathology
• +2 more

Denny Borsboom is Professor in the Psychological Methods Group at the University of Amsterdam, where he directs the Social and Behavioural Data Science Centre. His research program develops network theories of psychopathology, novel psychometric methods, and formalized psychological theory through initiatives like the Psychosystems Project and Theory Methods Lab. Research focuses on conceptual foundations of measurement, complex systems approaches to mental disorders, and computational modeling of psychological phenomena. Recent publications advance network psychometrics, validity theory, and integration of intra/interindividual dynamics. Articles demonstrate strong methodological innovation through computational psychiatry applications, network theory development, and philosophical engagement with psychological foundations. Recurring themes include psychopathological networks, measurement realism, system transitions, and collaborative theory-building frameworks.

Nabil KahaleView profile

Associate Professor

• Financial Derivatives
• Monte Carlo Methods
• Optimization
• +3 more

Nabil Kahale is an Associate Professor of Finance at ESCP Business School in Paris. His research focuses on financial derivatives, Monte Carlo methods, optimization, and machine learning. He holds a PhD in theoretical computer science from MIT (1993) and an HDR (French habilitation) from Université Paris 1 Panthéon-Sorbonne (2020), enabling him to supervise PhD students. His academic career includes prior roles in theoretical computer science and consulting for banks. He has published widely in top journals such as Mathematical Finance , Management Science , and SIAM Journal on Computing . Education: Bachelor of Science in Engineering, École Polytechnique (1987) PhD in Theoretical Computer Science, MIT (1993) HDR in Economics, Université Paris 1 Panthéon-Sorbonne (2020) Research Interests: His work bridges finance and computational methods, emphasizing practical applications of stochastic models and algorithmic efficiency. Key areas include derivative pricing, risk management, and the integration of machine learning into financial systems. Professional Contributions: He has served as a consultant for banking institutions and a referee for the French Ministry of Economy and Finance. His research also intersects with social and economic policy analysis, such as evaluating the economic impact of public health measures. Labs/Teams: While no specific lab affiliation is mentioned, his collaborations span interdisciplinary teams in finance, computer science, and applied mathematics through his publications and consulting work.

Laura M. StapletonView profile

Professor

• Multilevel Modeling
• Survey Methodology
• Educational Statistics
• +3 more

Laura M. Stapleton is Chair of the Department of Human Development and Quantitative Methodology and a Professor at the University of Maryland’s College of Education. She previously served as Interim Dean and Associate Dean for Research, Innovation, and Partnerships. Her academic roles include leadership in the NSF-funded Quantitative Research Methods Scholars Program (2019–2025) and membership on Maryland’s Accountability and Implementation Board for education reform. Education Background: Ph.D. in Measurement, Statistics, and Evaluation from the University of Maryland. Prior to academia, she worked as an economist at the Bureau of Labor Statistics and in educational research roles at the American Association of State Colleges and Universities and the University of Maryland’s institutional research department. Research focuses on complex survey data analysis, multilevel latent variable models, and mediation testing. Key interests include administrative data utilization, STEM education equity, and policy evaluation. Her work emphasizes bridging statistical rigor with practical educational challenges. Publications span methodological advancements in multilevel modeling, synthetic data strategies, and the integration of administrative datasets. Recent work addresses school-based prevention study attrition and the design effects of multilevel samples. Awards include AERA Fellowship (2023), election as President of the Society for Multivariate Experimental Psychology (2025), and recognition for mentoring and teaching excellence. She has led over $10M in grants, including NSF-funded initiatives to train early-career STEM equity researchers. Current projects include the BCSER Quantitative Research Methods Program (2022–2025) and collaborations on arts education impacts and intergroup relationship interventions. She advises on state-level longitudinal data systems and chairs the Maryland State Longitudinal Data System Center’s Research Branch (2013–2018).

• Psychometrics
• Healthcare Technology
• Item Response Theory
• +7 more

Dr. Maria Bolsinova is an Assistant Professor at Tilburg University's Department of Methodology and Statistics within the Tilburg School of Social and Behavioral Sciences. Her work focuses on psychometrics, healthcare technology, and methodological advancements in educational and clinical assessments. She holds a PhD and has contributed to over 40 research outputs since 2013. Research interests include experience sampling, patient-friendly measurement designs, differential item functioning, and extreme response style correction. Her projects often involve collaborations with organizations like the OECD and Amplify Education Inc., focusing on adaptive learning systems and PISA studies. She developed a personalized missingness design to reduce patient burden in longitudinal studies, currently being implemented in the m-Path app. Consulting roles: Curriculum Associates, LLC (2024–2024); OECD (2023–present); Amplify Education Inc. (2023) Contributions to UN Sustainable Development Goals through methodological advancements in healthcare and education Dataset: 'Assessing Life Satisfaction in Everyday Life' (2023)

Anders PousetteView profile

Lecturer

• Organizational Psychology
• Leadership Training
• Information Security
• +4 more

Anders Pousette is a Lecturer at the University of Gothenburg, affiliated with the Department of Organization, Health and Addiction. His academic work focuses on organizational psychology, leadership training, information security, occupational safety, and psychosocial working conditions in high-risk industries such as healthcare and construction. His research explores the interplay between organizational support climates and workplace safety, with a particular emphasis on ethical value conflicts in healthcare, leadership practices in construction, and security culture in nuclear and transportation sectors. He employs mixed-methods approaches and longitudinal studies to evaluate interventions and workplace dynamics. Recent publications highlight his work on managerial training programs, cross-cultural comparisons of safety practices, and the impact of psychosocial factors on information security behavior. His collaborations span multiple disciplines and institutions, particularly with Marianne Törner and Martin Grill.

Jochen SteffensView profile

Professor

• Music Listening Behavior
• Music Recommendation Systems
• Noise Perception
• +3 more

Prof. Dr.-Ing. Jochen Steffens is a faculty member at the University of Applied Sciences Düsseldorf , affiliated with the Faculty of Media . His research spans interdisciplinary domains at the intersection of music, soundscapes, and cognitive-affective processes. Research Focus : Music listening behavior, soundscapes, noise perception, music recommendation systems, film music, and the psychological effects of sounds. Teaching : Supervises final theses and offers modules aligned with examination regulations of 2018, including topics in media informatics and mixed reality applications. Technical Contributions : Develops tools for soundscape exploration (e.g., Advanced Soundscape Search) and music information retrieval (MIR) systems for live performance visualization. Methodological Expertise : Utilizes computational music analysis, experience sampling, statistical learning, and multilevel modeling to investigate situational and demographic influences on auditory perception. Applied Research : Explores the impact of room acoustics on customer satisfaction in restaurants, motivational music in sports, and semantic expression in audio branding.

• Markov Chain Monte Carlo
• Bayesian Statistics
• Computational Statistics
• +3 more

Christophe Andrieu is a Professor in Statistics within the School of Mathematics at the University of Bristol. His research bridges theoretical probability, computational statistics, and applied mathematics, with significant contributions to Markov Chain Monte Carlo methodologies and Bayesian inference frameworks. He maintains active collaborations across engineering and data science domains. His educational background includes: M.A. from List.Natnl.Scis.App.Lyon Additional M.A. (institution unspecified) Ph.D. from Paris Andrieu's research focuses on Markov Chain Monte Carlo theory , where he develops convergence guarantees and efficiency bounds for complex samplers. His work extends to non-reversible MCMC algorithms , piecewise deterministic processes , and gradient-free optimization techniques. Recent publications demonstrate innovative approaches to state-space models and numerical integration, often addressing high-dimensional statistical challenges through stochastic approximation methods. His fingerprint reveals deep specialization in Markov chain convergence analysis and computational Bayesian statistics. His 15 most recent publications (2021-2025) exhibit consistent focus on theoretical foundations of Monte Carlo methods, particularly convergence analysis of Markov chains and novel sampler designs. Key trends include the application of weak Poincaré inequalities to pseudo-marginal MCMC, development of self-organizing state-space models, and exploration of hypocoercivity in piecewise deterministic processes. The work spans both theoretical advancements and practical implementations for engineering and statistical applications. Andrieu has secured significant research funding including: COmputational Statistical INference for Engineering and Security (COSINES) (2018-2023) New Approaches to Data Science (2018-2023) He has supervised 5 research students and maintains active collaborations in computational statistics and machine learning. His network shows strong connections with probability theory and engineering research groups.

Vijay K. SoodView profile

Professor

• Power Systems
• Power Electronics
• HVDC and FACTS Controllers
• +3 more

Dr. Vijay K. Sood is a Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University. He holds a PhD in Power Electronics from Bradford University (UK), and has extensive experience in power systems, power electronics, and renewable energy integration. His research focuses on High Voltage Direct Current (HVDC) systems, Flexible AC Transmission Systems (FACTS), power electronics converters, and grid protection. He has received numerous awards, including Life Fellow of IEEE, Emeritus Fellow of the Canadian Academy of Engineers, and the 2000 IEEE Third Millennium Medal. Dr. Sood has authored/co-authored over 100 publications, including peer-reviewed articles in top journals like IEEE Transactions on Power Electronics and International Journal of Electrical Power & Energy Systems. His work addresses challenges in grid stability, renewable energy integration, and advanced control strategies for power systems. Education: PhD (Power Electronics), Bradford University, UK (1977) MASc (Electrical Machines), Strathclyde University, Scotland (1969) BSc (Electrical Engineering), Nairobi University, Kenya (1967) Awards: IEEE Third Millennium Medal (2000) Canadian Pacific Railway Engineering Award (2002) IEEE Regional Activities Board Achievement Awards (2001, 2006) His research emphasizes practical solutions for modern power systems, including HVDC controller design, synthetic inertia control for renewable grids, and advanced fault detection techniques. He has collaborated internationally on projects like the Energy System Observatory of Honduras and grid architecture models for multi-terminal DC systems.

Robert (Rob) J. Franzese, Jr.View profile

Professor

• Comparative Political Economy
• International Political Economy
• Quantitative Research Methods
• +5 more

Robert (Rob) J. Franzese, Jr. is a Professor and Associate Chair in the Department of Political Science at the University of Michigan. He also serves as the Edie N. Goldenberg Director of the Michigan in Washington Program and Research Professor at the Center for Political Studies (CPS), Institute for Social Research. His research focuses on comparative and international political economy (C&IPE) of developed democracies, emphasizing the interplay between political and economic institutions, structures, and events on macroeconomic policymaking. Franzese is renowned for his work on spatial-econometric models of interdependence and empirical methodologies in political science. Education: PhD in Government from Harvard University (1996), A.M. in Economics and Government from Harvard (1995 and 1992), and B.A. in Economics and Government from Cornell University (1990, Phi Beta Kappa). He teaches courses on quantitative methods, comparative politics, and political economy. Key affiliations include the ICPSR Summer Program in Quantitative Methods and the Center for Political Studies. His research spans topics such as central bank independence, wage bargaining institutions, and spatial econometrics. He has authored/co-authored numerous books and articles on empirical methodology, policy analysis, and interdependence modeling. His methodological contributions include spatial econometric models, multilevel analysis, and model-based estimation for social science theory (EITM). He has advised on policy issues related to globalization, fiscal policy, and labor markets, with a focus on developed democracies. Fransese has directed the ICPSR Summer Program, which trains researchers in advanced quantitative methods. His work bridges political science and economics, emphasizing interdisciplinary approaches to understanding policymaking dynamics and spatial interdependencies.

Paul Austin McDermottView profile

Professor

• Measurement Theory
• Scale Development
• Multilevel Modeling
• +8 more

Dr. Paul Austin McDermott is a Professor in the Human Development and Quantitative Methods Division at the University of Pennsylvania Graduate School of Education (Penn GSE). He specializes in psychometrics, scale development, and advanced statistical modeling. Formerly, he served as Director of Clinical and Industrial Measurement at The Psychological Corporation and held roles in the Penn School of Medicine. His work focuses on standardized assessments of behavioral adjustment, classroom learning, and cognitive growth. Research Interests : Development and validation of assessment tools for school behavioral adjustment (e.g., Adjustment Scales for Early Transition in Schooling, Learning Behaviors Scale). Cross-cultural and bilingual psychometric studies, including Spanish/English translation of assessments for Latino populations. Assessor bias in special education classification and longitudinal growth patterns in learning behaviors. Collaborations & Projects : Co-directs large-scale measurement programs analyzing developmental trajectories in pre-K to first grade. Leads research on equating preschool and elementary school assessments (e.g., Head Start Preschool Learning Behaviors Scale). Co-supervises international collaborations in Trinidad and Tobago for social-emotional assessment tools. Awards & Affiliations : Diplomate from the American Academy of Assessment Psychology. Licensed psychologist and certified school psychologist. Grants & Innovation : Co-PI for a Spanish/English translation study using item response theory (IRT). Directed development of the Learning Express adaptive assessment battery.

Daniele TurchettiView profile

Assistant Professor

• Algebraic Geometry
• Number Theory
• Non-Archimedean Analytic Geometry
• +9 more

Daniele Turchetti is an Assistant Professor in the Department of Mathematical Sciences at Durham University, where he teaches advanced mathematics and data science courses. His academic journey includes a PhD from Paris-Saclay University (2014) and postdoctoral positions at Leiden University, Max-Planck Institute for Mathematics, Caen University, Dalhousie University, and Warwick University. His educational background includes: PhD in Mathematics from University of Paris-Saclay (2011-2014) Turchetti's research spans pure mathematics and educational applications. His primary interests focus on the intersection of algebraic geometry and number theory, particularly non-Archimedean analytic geometry, Galois theory, and their applications to problems in positive characteristic. He also explores connections to toric varieties, logarithmic structures, dynamical systems, and modular forms. In addition, he has developed significant interests in mathematics education, including e-assessment, student engagement, and the application of statistical learning to advance pure mathematics. His publication record demonstrates a consistent focus on Berkovich geometry and related topics in non-Archimedean geometry, with recent expansion into applications of algebraic geometry to other scientific fields and machine learning techniques in mathematical research. His work often involves collaborations with researchers across Europe and North America. Turchetti is actively involved in mathematical outreach, serving as chair of the mathematical science outreach committee at Durham University. He has collaborated with numerous organizations including Images des Mathématiques, Fête de la Science, and various STEM outreach initiatives in Atlantic Canada and the UK, focusing on demonstrating that 'mathematics is useful, empowering, and beautiful' to diverse audiences. As an educator, he teaches courses including 'Data Exploration, Visualization, and Unsupervised Learning' and 'Multilevel Modelling,' and supervises undergraduate research projects on algebraic geometry topics. His teaching philosophy emphasizes effective assimilation of mathematical concepts and communicating the beauty of mathematics to students.

Rohan AlexanderView profile

Assistant Professor

• Quantitative Social Science
• Bayesian Methods
• Text as Data
• +4 more

Rohan Alexander is an Assistant Professor jointly appointed in the Faculty of Information and the Department of Statistical Sciences at the University of Toronto. His research focuses on improving the trustworthiness of data science through rigorous workflows, including reproducibility and bias mitigation. He co-founded The Data Workshop, a platform for data science best practices, and authored Telling Stories With Data , a book emphasizing reproducible methods. Rohan holds a PhD in Economics from the Australian National University, specializing in economic history. Rohan’s academic roles include Assistant Director of CANSSI Ontario and Senior Fellow at Massey College. His teaching includes courses like Experimental Design for Data Science and Worlds Become Data . He actively contributes to interdisciplinary initiatives like the Schwartz Reisman Institute for Technology and Society. His research interests span quantitative social science, Bayesian methods, text analysis, and computational reproducibility. He emphasizes code transparency and testing in data science projects. Rohan’s work addresses challenges in data measurement, such as systematic missingness, and explores AI’s societal impacts through collaborative projects with academia and industry. Rohan is affiliated with the Data Sciences Institute and leads initiatives on reproducibility. His current supervision includes PhD student Ciara Zogheib. His institutional roles extend to strategic projects like the Climate Positive Energy initiative and School of Cities.

Aleksandar MijatovicView profile

Professor

• Probability
• Stochastic Processes
• Mathematical Finance
• +5 more

Aleksandar Mijatović is a Professor of Probability at the Department of Statistics, University of Warwick, and Deputy Head of Department for Research. He was previously Chair in Probability at King's College London and Reader in Probability at Imperial College London. His research focuses on probability theory, stochastic processes, mathematical finance, numerical stochastics, and data science. He holds a Ph.D. in low-dimensional topology from Trinity College Cambridge and worked as a quantitative analyst in foreign exchange derivatives before academia. Research interests include stochastic analysis of processes with jumps, simulation methods (e.g., Monte Carlo), stochastic control, and applications in finance. He is a Fellow of the Alan Turing Institute and maintains a YouTube channel, Prob-AM, explaining his research. His work often bridges theoretical probability with practical applications in finance and data science. Key publications explore topics like reflected Brownian motion, Lévy processes, branching processes, and stochastic gradient descent. Collaborations with institutions like King’s College London and Imperial College London highlight his academic networks. His contributions span theoretical advancements and computational methodologies, with applications in risk management, option pricing, and algorithm development.