جستجوی استاد راهنمای دانشگاهی

Jouni Kuhaمشاهده پروفایل

استاد

• Social Statistics
• Latent Variable Modeling
• Survey Methodology
• +۴ مورد دیگر

Jouni Kuha is a Professor of Social Statistics and MSc Social Statistics Programme Director at the London School of Economics and Political Science (LSE), Department of Statistics. His expertise lies in latent variable modeling, survey data analysis, measurement error, and missing data, with applications in social sciences. He collaborates on projects addressing education mobility, public attitudes toward policing, family support dynamics, and global medicine accessibility. He contributed to the UK General Election exit poll analysis and was elected a Fellow of the British Academy in 2021. Education: MSocSc (Statistics) from the University of Helsinki (1992), PhD (Social Statistics) from the University of Southampton (1996). Prior roles include postdoctoral research at Nuffield College, Oxford, and Assistant Professor at Pennsylvania State University. Teaching includes courses on applied regression analysis, multivariate analysis, and statistical modeling. Research interests span latent variable models, categorical data analysis, and methodological advancements in multilevel and latent class analysis. Recent work focuses on digital trace data biases, procedural justice in policing, and intergenerational welfare dynamics. His methodological contributions include R package implementations for multilevel latent class analysis. Scientific awards include Fellowship of the British Academy (2021). Key grants and collaborations involve cross-national studies on citizenship norms, crime victimization, and healthcare policy. He advises on statistical methodologies for social science applications and maintains active involvement in the LSE’s Department of Methodology.

• Social Psychology
• Intergroup Relations
• Gender Studies
• +۶ مورد دیگر

Andrew L. Stewart is an Associate Professor of Psychology at Clark University's Hiatt School of Psychology. His research focuses on intergroup relations across various dimensions including gender, race, ethnicity, nationality, sexual orientation, and class. He directs the Ideology and Intergroup Violence Laboratory and has been at Clark University since 2014. Stewart received his Ph.D. and M.A. in Social Psychology from the University of Connecticut (2014 and 2011) and B.S. degrees in Psychology and Mathematics from Colorado State University (2009). His academic journey reflects a strong foundation in both psychological theory and quantitative methods. Professor Stewart's research examines how widespread beliefs about social groups contribute to intergroup violence and discrimination, and how to change those beliefs to reduce such negative outcomes. His work particularly investigates how sexist and hegemonic masculinity norms contribute to violence against women, why people engage in collective action to reduce inequality, and how to implement effective sexual assault prevention programs. His methodological expertise includes advanced statistical techniques such as multilevel modeling, structural equation modeling, and analyses of dyadic and group data. His recent publications demonstrate a consistent focus on social dominance theory, intergroup relations, and collective action, with applications to gender inequality, racial justice, and political protest. Stewart's work bridges theoretical social psychology with practical applications for reducing discrimination and violence. Among his notable contributions are the development and evaluation of "The Men's Project," a sexual assault prevention program targeting college men, and research on international support for social movements like the Arab Spring uprisings. He has consulted with universities and non-profit organizations on men's programming and violence prevention training. As an educator, Stewart teaches courses including Advanced Social Psychology, Masculinities and Violence, Psychology of Prejudice, and Structural Equation Modeling. He also serves as an SPN Mentor, providing career guidance to underrepresented college students.

Fabio Nobileمشاهده پروفایل

استاد

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +۵ مورد دیگر

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

Jun Yanمشاهده پروفایل

استاد

• Survival Analysis
• Spatial Statistics
• Network Analytics
• +۲ مورد دیگر

Dr. Jun Yan is a Professor in the Department of Statistics at the University of Connecticut. His research spans network analytics, spatial extremes, survival analysis, and statistical computing with applications in public health, finance, and environmental science. His core research interests include: network modeling and analysis, spatial statistics for climate extremes, survival analysis methodologies, statistical computing frameworks, and applications in interdisciplinary domains including sports analytics. Dr. Yan has developed significant statistical methodologies for network analysis, climate change detection, financial modeling, and health analytics. His recent publications demonstrate innovation in modeling complex network structures, analyzing climate extremes, developing computational approaches for massive datasets, and creating specialized statistical methods for health and finance applications. He maintains active collaborations across disciplines and contributes to open-source statistical software. Honors include: Guggenheim Fellowship, multiple Fromm Foundation commissions, and Barlow Endowment recognition.

• Mathematics Education
• Quantitative Research Methods
• Educational Attitudes
• +۴ مورد دیگر

Dr. Maria Pampaka is a Professor at the University of Manchester, holding joint appointments in the Manchester Institute of Education and the Department of Social Statistics. Her research focuses on mathematics education, particularly learners’ attitudes and dispositions, advanced quantitative methods, and the application of complex systems theory. She has led projects on mathematics anxiety, gender equality measurement, and longitudinal survey analysis. Pampaka employs methodologies like Rasch modeling, missing data imputation, and agent-based simulations. She has collaborated internationally on studies ranging from regional gender gaps to tourist behavior analysis. Her work contributes to UN Sustainable Development Goals related to quality education and gender equality. Her research interests span mathematics education, quantitative research methods, and social statistics. Notable projects include a systematic review of mathematics anxiety and the development of the Extended Regional Gender Gaps Index (eRGGI). She has also explored applications of machine learning in tourism and healthcare, such as predicting revisit intentions and optimizing hospital resource allocation during the pandemic. Projects: Systematic review of mathematics anxiety, eRGGI development, analysis of sexual assault survivor support needs, and requirements discovery for smart driver assistive technologies. Key methodologies: Rasch modeling, multilevel modeling, thematic analysis, agent-based simulations, and machine learning. Collaborations: Cross-disciplinary work with social scientists, engineers, and healthcare professionals internationally. Pampaka’s work bridges education, statistics, and policy, aiming to inform equitable practices in learning and societal structures. She is engaged in impact initiatives like the Good Behavior Game evaluation and policy-informed gender equality research.

Leslie Greengardمشاهده پروفایل

استاد

• Integral Equation Methods
• Electromagnetics
• Acoustics
• +۵ مورد دیگر

Leslie Greengard is a Silver Professor of Mathematics and Computer Science at New York University's Courant Institute, part of the Faculty of Arts and Science. His research focuses on integral equation methods for electromagnetics, acoustics, plasma physics, and fluid dynamics, with recent work extending to kernel-based methods in statistical inference. He leads the Courant Mathematics and Computing Laboratory and contributes to novel electromagnetic simulation techniques. Education: Ph.D. and M.D. in Computer Science and Medicine from Yale University (1987) B.A. in Mathematics from Wesleyan University (1979) Research Interests: Greengard's group develops fast solvers for complex geometries in electromagnetics and fluid dynamics. Notable contributions include the Fast Multipole Method (FMM) for particle simulations and Quadrature by Expansion (QBX) for layer potential evaluations. His work bridges computational mathematics and applications in physics, engineering, and biomedicine. Recent Article Trends: Recent publications emphasize fast algorithms (e.g., FMM, adaptive Gauss transforms), biomedical applications (e.g., deep brain stimulation modeling), and cosmological simulations. His methods address challenges in high-dimensional data, multiscale problems, and real-time computation. Awards & Recognition: While no specific prizes are listed, his foundational work on FMM has had a transformative impact on computational science. Advising & Collaborations: Collaborations span academia and industry, with co-authors including V. Rokhlin, M. O’Neil, and others. No formal advisees are listed here, though his research involves postdoctoral fellows and graduate students. Labs & Teams: Active in the Courant Mathematics and Computing Laboratory, focusing on electromagnetic simulation and design methodologies.

Anders Skrondalمشاهده پروفایل

استاد

• Psychometrics
• Statistics
• Biostatistics
• +۳ مورد دیگر

Anders Skrondal is a Professor II at the University of Oslo's Faculty of Educational Sciences, affiliated with the Centre for Educational Measurement (CEMO). He also serves as a Senior Scientist at CEFH (Research Council of Norway Centre of Excellence) at the Norwegian Institute of Public Health and Co-Principal Investigator at CREATE, another Norwegian Centre of Excellence. His academic journey includes roles as Head of the Biostatistics Group at the Norwegian Institute of Public Health and Professor of Statistics at the London School of Economics (LSE), where he directed the Methodology Institute. Skrondal's research focuses on psychometrics, statistics, biostatistics, and econometrics, with a major contribution being the development of the GLLAMM framework. He has authored 14 books and over 200 peer-reviewed papers, achieving an h-index of 63 and 30,000+ citations. His awards include the 1997 Psychometric Society Dissertation Prize and leadership roles in prestigious organizations like the Psychometric Society and Royal Statistical Society. Research Interests: Skrondal specializes in statistical methodologies including latent variable modeling, multilevel modeling, and missing data analysis. His work bridges theoretical advancements and practical applications in medicine, psychology, and social sciences. He is renowned for integrating latent variable and mixed model frameworks to address complex data structures. Recent trends in his publications emphasize methodological solutions for missing data, non-ignorable mechanisms, and psychometric model validation. His articles span statistical theory, medical applications, and educational measurement. Awards: President, Psychometric Society (2016–2017) Elected Member, International Statistical Institute Outstanding Academic Title for 'The Cambridge Dictionary of Statistics' (2011) Fulbright Professor at UC Berkeley (2013–2014) Advising & Grants: Skrondal has led major research initiatives such as CEFH and CREATE, funded by the Research Council of Norway. While no specific advisee list is provided, his collaborations span international institutions. His work on GLLAMM software is used in over 750 journals, reflecting widespread academic impact. Labs/Teams: Active in CEMO and CEFH, he contributes to interdisciplinary teams advancing educational measurement and public health research. His involvement in CREATE focuses on equality in education through statistical innovations.

Ian Brunton-Smithمشاهده پروفایل

استاد

• Criminology
• Advanced Quantitative Methods
• Survey Methodology
• +۴ مورد دیگر

Professor Ian Brunton-Smith is a leading academic in Criminology and Advanced Quantitative Methods at the University of Surrey, Department of Sociology. He holds the role of Professor of Criminology and Research Methods, with affiliations including the Surrey Centre for Criminology and editorial positions at journals such as Sociology and the Journal of the Royal Statistical Society Series A . His research focuses on improving crime measurement, multilevel modeling, Bayesian statistics, and the public understanding of science. He has led projects like the ADR UK Data First Evaluation Fellowship and Re-counting Crime, addressing issues like police under-counting and crime data reliability. He currently supervises PhD student Megan Georgiou and teaches on the MSc Social Research Methods program. His work bridges criminological theory and methodological innovation, with notable contributions to topics like hate crime impacts, prisoner reentry outcomes, and interviewer effects in surveys. He has served on the 2021 REF panel for Sociology and advisory groups for police funding formulas. Education: Holds a PhD in Sociology, with prior academic roles at the University of Warwick (Director of the Warwick Q-Step Centre). His research integrates quantitative rigor with real-world policy applications, particularly in crime prevention and data accuracy. Grants and Projects: Principal Investigator for projects such as 'Re-counting Crime: New Methods to Improve the Accuracy of Estimates of Crime' (ESRC, £299,261) and 'ADR UK Data First Evaluation Fellowship' (£177,202). Collaborations include work on synthetic crime data and carbon footprint analysis of crime prevention measures. Awards and Recognition: While no explicit awards are listed, his extensive editorial roles and leadership in high-impact research initiatives reflect his scholarly influence. Labs and Teams: Co-director of the Surrey Centre for Criminology and involved in cross-disciplinary teams analyzing crime data, survey methodology, and environmental criminology.

Atika Khuranaمشاهده پروفایل

دانشیار

• Developmental Psychology
• Prevention Science
• Adolescent Risk-Taking
• +۴ مورد دیگر

Atika Khurana is an Associate Professor in the Counseling Psychology and Human Services Department at the University of Oregon's College of Education, and a Research Scientist at the Prevention Science Institute. Her work focuses on developmental-ecological approaches to adolescent risk-taking behaviors. Postdoctoral training (2010-2012): Annenberg Public Policy Center, University of Pennsylvania PhD (2010): The Ohio State University (Major: Human Development and Family Science, Minor: Quantitative Psychology) Her research examines: Self-regulation development in adolescents Media/technology effects on health behaviors Personality-risk interactions (impulsivity, sensation seeking) Longitudinal modeling of behavioral trajectories Parenting behavior moderation Substance abuse prevention Recent publications analyze adolescent risk behaviors through the lens of media exposure, peer influences, and neurocognitive development, employing advanced statistical methods like latent variable modeling and multi-level analysis. Her work has been funded by NIDA, NICHD, and IES. NIDA Early Career Poster Award (2014) Distinguished Research Fellow, Annenberg Public Policy Center (2013) University of Oregon Faculty Research Award (2013) Dr. Khurana serves as Research Scientist at the Prevention Science Institute and will accept new PhD students in Counseling Psychology and Prevention Science in 2026-2027. Her methodological expertise includes missing data analysis, mediation models, and longitudinal research design.

Gregory R. Hancockمشاهده پروفایل

استاد

• Structural Equation Modeling
• Latent Growth Models
• Latent Variable Analysis
• +۵ مورد دیگر

Gregory R. Hancock is a Professor and Program Director of Quantitative Methodology: Measurement and Statistics at the University of Maryland, College Park. He also serves as Director of the Center for Integrated Latent Variable Research (CILVR) and is an Affiliated Professor at the Center for Advanced Study of Language. Holding a Ph.D. from the University of Washington (1991), his research focuses on structural equation modeling, latent growth models, experimental design, and power analysis. He has co-edited influential volumes such as Structural Equation Modeling: A Second Course and contributed to Psychometrika , Multivariate Behavioral Research , and other top journals. His awards include the Jacob Cohen Award for Teaching (2011), Fellowships from the APA and APS, and multiple recognition for mentorship. Hancock has led over 200 workshops globally and served on editorial boards for major journals. His work emphasizes methodological rigor in quantitative research, with contributions to latent variable models, measurement invariance, and longitudinal methods. Key Contributions: SEM applications, growth curve modeling, and statistical pedagogy Labs/Teams: Center for Integrated Latent Variable Research (CILVR) Funding/Grants: Not explicitly listed, but implied through extensive workshop leadership and editorial commitments

Hans Baumgartnerمشاهده پروفایل

استاد

• Consumer Psychology
• Structural Equation Modeling
• Cross-cultural Measurement
• +۱ مورد دیگر

Hans Baumgartner is Smeal Chair Professor of Marketing at Pennsylvania State University, specializing in consumer psychology and research methodology. His work develops advanced measurement techniques for cross-cultural consumer research, including structural equation modeling approaches for scale validation and response bias correction. Professor Baumgartner's research examines pathological buying behaviors, cross-cultural response patterns, and pro-environmental decision-making. Recent publications focus on improving measurement validity in international marketing studies through methodological innovations addressing common method variance and careless responding. He has authored foundational texts on marketing measurement and serves on editorial boards of major marketing journals.

Brian Kellerمشاهده پروفایل

استادیار

• Missing Data Analysis
• Bayesian Statistics
• Statistical Computing
• +۳ مورد دیگر

Brian Keller is an Assistant Professor in the Department of Educational, School & Counseling Psychology at the University of Missouri's College of Education & Human Development. His research focuses on statistical methodologies, particularly missing data analysis, Bayesian statistics, structural equation modeling, and multilevel modeling. He is the developer of Blimp statistical software, which supports advanced statistical computing methods. Key areas of expertise include missing data imputation techniques, Bayesian computational methods, and the application of statistical models to educational and psychological data. His work emphasizes methodological innovation, software development, and the rigorous analysis of complex datasets. Recent research trends in his articles highlight advancements in latent variable modeling, nonlinear effects, and the integration of computational tools like Blimp. His contributions address challenges in multilevel models, missing data mechanisms, and model comparison criteria such as DIC and WAIC. No specific scientific awards are listed for Brian Keller in the provided information. His professional affiliations include the College of Education & Human Development and the Blimp Statistical Software initiative. He advises students in the department but no specific advisee names are provided. Research grants and funding details are not explicitly mentioned in the text. Blimp Statistical Software is a key collaborative project, reflecting his commitment to advancing statistical methods and software solutions for researchers.

Seongho Songمشاهده پروفایل

استاد

• Bayesian Statistics
• Biostatistics
• Statistical Modeling
• +۵ مورد دیگر

Seongho Song is a Professor and Undergraduate Program Director in the Department of Mathematical Sciences at the University of Cincinnati. He holds a Ph.D. in Statistics from the University of Connecticut (2005). His research focuses on Bayesian statistical methods, biostatistics, and computational biology, with applications in genetics, environmental science, and healthcare. Song has led or collaborated on grants totaling over $4.7M, including projects funded by DARPA, NIH, and the Korea Institute of Ocean Science and Technology. Education: Ph.D. in Statistics, University of Connecticut (2005) Positions: Assistant (2005-2011) → Associate (2011-2019) → Full Professor (2019–Present) Research interests include Bayesian hierarchical models, longitudinal data analysis, and bioinformatics. He has published over 30 peer-reviewed articles in journals like Biostatistics , Frontiers in Genetics , and Journal of Productivity Analysis . Recent work involves modeling circadian rhythms, lung disease progression, and oceanographic systems. Teaching responsibilities include graduate courses in Bayesian analysis, statistical computing, and linear models. He has served on editorial boards for Sankhya Series B and IEEE Transactions on Computational Biology , and chairs the EAC-ISBA Special Interest Group.

Leung Tsangمشاهده پروفایل

استاد

• Remote Sensing
• Electromagnetic Scattering
• Wave Propagation in Random Media
• +۶ مورد دیگر

Leung Tsang is a Professor of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, holding the Robert J. Hiller Professorship of Engineering. He received his SB, SM, EE, and Ph.D. from MIT. His academic career includes positions at Texas A&M University (1980-1983), University of Washington (1983-2014) as Professor and Department Chair (2006-2011), and a visiting Professorship at City University of Hong Kong (2001-2004). He has served as Editor-in-Chief of IEEE Transactions on Geoscience and Remote Sensing and President of the Electromagnetics Academy and IEEE Geoscience and Remote Sensing Society. His research focuses on microwave remote sensing , multiple scattering theory , electromagnetic wave propagation in random media , photonic crystals , and signal integrity . His work spans theoretical advancements (e.g., radiative transfer theory, UV multi-level methods) and practical applications (e.g., snow parameter retrieval, ocean foam modeling, via coupling analysis). Key trends include Development of computational techniques for large-scale scattering problems Integration of neural networks for inversion in remote sensing Investigation of backscattering enhancement and polarimetric signatures Notable awards include Fellow of IEEE and Optical Society of America William T Pecora Award (2012) Van De Hulst Light Scattering Award (2018) Membership in the National Academy of Engineering (2020) His group utilizes high-performance computing resources, including the Flux HPC cluster at the University of Michigan and XSEDE allocations, with specialized nodes for electromagnetic modeling. Current students include Ruoxing Gao (electromagnetic theory), Firoz Borah (snow remote sensing), Jongwoo Jeong (forest scattering), and Zhenming Huang (snow modeling).

Wei Wuمشاهده پروفایل

استاد

• Clinical Psychology
• Quantitative Psychology
• Longitudinal Data Analysis
• +۳ مورد دیگر

Wei Wu is a Professor in the Department of Psychology at Indiana University, specializing in quantitative methodology for psychological research. His work bridges clinical applications with advanced statistical techniques, particularly in longitudinal study designs. His academic credentials include: Ph.D. in Quantitative Psychology from Arizona State University (2008) M.S. in Personality Psychology from East China Normal University (2003) B.Ed. in Special Education from East China Normal University (1997) Professor Wu's research focuses on developing and refining statistical methods for psychological inquiry, with emphasis on missing data handling, growth curve modeling, and model fit evaluation. His clinical psychology background informs applications in educational and developmental contexts, particularly regarding grade retention effects. Methodological rigor defines his contributions, advancing best practices for longitudinal data analysis in social sciences. Analysis of his publication record reveals consistent innovation in missing data techniques and experimental design optimization. His work demonstrates increasing sophistication in planned missing data approaches, with growing emphasis on computational efficiency and real-world applicability across psychology subfields. Recent publications integrate SEM and multilevel frameworks to solve complex modeling challenges. Professor Wu has secured substantial research funding through competitive grants: NSF grant ($422,900, 2011-2016) as PI for planned missing data designs in longitudinal research NIH grant ($1.7M, 2010-2014) as Statistical Consultant for resilience determinants study PCORI grant ($1.5M, 2013-2016) as Statistical Consultant for burnout impact research New Faculty General Fund ($8,000, 2010-2013) for model fit evaluation methods NICHD grant (2004-2008) as Research Assistant for grade retention study He actively mentors graduate researchers, with documented advisees including A. I. Carroll, P-Y. Chen, F. Jia, R. Kinai, F. Gu, P. R. Miller, and P. Pornprasermanit across multiple publications. While no formal lab structure is specified, his extensive grant collaborations indicate active participation in interdisciplinary research networks focused on methodological advancement.