جست‌وجوی استاد و پژوهشگر

Steven Andrew Culpepperمشاهده پروفایل

استاد

• Quantitative Methods in the Social Sciences
• Psychometrics
• Latent Class Models
• +۶ مورد دیگر

Steven Andrew Culpepper is a Professor of Statistics at the University of Illinois at Urbana-Champaign, holding additional appointments as Professor in the Beckman Institute for Advanced Science and Technology, Psychology, and Educational Psychology. He specializes in quantitative methods for social sciences, focusing on psychometric models, latent class analysis, and statistical computing. Education: PhD, Educational Psychology, University of Minnesota, 2006 BS, Economics, Bowling Green State University, 2001 Research interests include advanced statistical methodologies such as latent class models, high-stakes testing analysis, and applications of Bayesian computing in education and organizational research. His work emphasizes improving large-scale assessment systems through innovative modeling approaches. His publications consistently address latent structure modeling, cognitive diagnosis frameworks, and methodological advancements in educational and behavioral statistics. While no scientific awards are explicitly listed, his contributions to psychometric theory and statistical software development are notable. Steven has grants and consulting projects related to statistical methodologies but specific grant details are not provided in the texts. He has no listed advisees/PhD students in the provided information. He collaborates across disciplines through affiliations with the Beckman Institute and maintains active software development projects, including R packages like 'rrum' and 'pathmodelfit'.

Li Caiمشاهده پروفایل

استاد

• Psychometrics
• Latent Variable Models
• Item Response Theory
• +۳ مورد دیگر

Li Cai is a Professor and Director at the National Center for Research on Evaluation, Standards, and Student Testing (CRESST) within the Graduate School of Education and Information Studies at the University of California, Los Angeles (UCLA). His work focuses on quantitative methods in education, particularly psychometrics and statistical modeling. Ph.D. in Quantitative Psychology from the University of North Carolina – Chapel Hill Research and teaching interests center on psychometrics, latent variable models, item response theory, nonlinear mixed models, and statistical computation. His methodological work addresses advanced techniques for educational assessment and model evaluation. His representative publications include studies on covariance structure models, item response theory, bifactor analysis, and goodness-of-fit testing. These works often emphasize computational algorithms and practical applications in educational measurement. Li Cai is affiliated with CRESST at UCLA, a leading center dedicated to rigorous research, assessment design, and evaluation methodology across diverse educational contexts.

Prabir Burmanمشاهده پروفایل

استاد

• Nonparametric function estimation
• Model fitting/selection
• Image analysis
• +۷ مورد دیگر

Prabir Burman is a Professor in the Department of Statistics at the University of California, Davis, with a career spanning over three decades. His research focuses on nonparametric function estimation, model fitting/selection, image analysis, time series, and discrete data. Education: Ph.D. (1982) and Master of Statistics (1977) from University of California, Berkeley; Bachelor of Statistics (1976) from Indian Statistical Institute, Calcutta. His work bridges theoretical statistics and applied problems, including ecological studies (e.g., coyote parasites, mountain lion tracking), biomedical research (e.g., metabolic syndrome in bipolar patients), and time series forecasting. He has secured multiple NSF and NSA grants for projects on multivariate analysis, shape modeling, and covariance estimation. Recent publications highlight his expertise in predictive model fitting, stock return analysis, and stroke survivor studies. While not explicitly listing awards, his editorial roles (e.g., Journal of Multivariate Analysis) and collaborative grants underscore his academic leadership.

Yinqiu Heمشاهده پروفایل

استادیار

• High-dimensional statistical inference
• Large-scale data analysis
• Mediation pathway analysis
• +۵ مورد دیگر

Yinqiu He is an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison. They hold affiliations with the School of Computer, Data & Information Sciences and the Data Science Institute at Columbia University (2021-2022 postdoc). Their research focuses on developing statistical methodologies for high-dimensional and complex data, with applications in genomics, metabolomics, and network analysis. Key areas include mediation pathway analysis, asymptotic theory for U-statistics, and functional connectivity modeling. Education includes a B.S. in Statistics from the University of Science and Technology of China (2016) and a Ph.D. in Statistics from the University of Michigan-Ann Arbor (2021), advised by Professors Gongjun Xu and Xuming He. They were awarded the ProQuest Distinguished Dissertation Award (2022) and received multiple travel grants from the Institute of Mathematical Statistics and ASA. Teaching includes core Ph.D. courses like STAT 849 (Regression Analysis) and applied courses like STAT 456 (Multivariate Statistics). Current mentoring includes MS student Yuhan Zheng (now pursuing UW-Madison Ph.D.) and Xiangyi Liao (Ph.D. in Educational Psychology). Research outputs include foundational work on adaptive U-statistics testing frameworks and scalable methods for large-scale genomic data analysis. Active in methodological contributions to biostatistics, their work bridges statistical theory and computational efficiency. Recent projects involve dynamic functional connectivity estimation from fMRI data and latent space modeling in heterogeneous networks. GitHub repository 'Adaptive-U-stats' hosts open-source implementations of high-dimensional testing algorithms developed in their research.

Steve Marronمشاهده پروفایل

استاد

• Statistics
• Data Science
• Machine Learning
• +۴ مورد دیگر

Steve Marron is the Amos Hawley Distinguished Professor of Statistics and Operations Research at the University of North Carolina at Chapel Hill (UNC-CH). He holds a joint appointment in the School of Data Science and Society and is a professor in the Department of Biostatistics at the Gillings School of Global Public Health. Additionally, he serves as an adjunct professor in the Department of Computer Science within the College of Arts & Sciences. His research focuses on statistics, data science, and machine learning, with a particular emphasis on integrating diverse data types such as genomics, imaging, and demographic data. Education: Marron earned an AA from Orange Coast College (1974), BS from University of California, Davis (1977), and PhD from UCLA (1982). He has held faculty positions at UNC-CH since 1982 and Cornell University (2001-2002). His honors include Fellowships from the Institute of Mathematical Statistics and American Statistical Association, and he is a top-cited mathematician (1991-2001). Research interests include object-oriented data analysis, high-dimensional data methods (HDLSS), visualization techniques like SiZer, and statistical methodology for imaging and genomics. Notable contributions include Distance-Weighted Discrimination (DWD), Principal Nested Spheres, and JIVE for data integration. His work has applications in cancer genomics, medical imaging, and bioinformatics. Awards and recognitions include the Amos Hawley Professorship, S. N. Roy Memorial Lectureship, and Saw Swee Hock Visiting Professorship. Marron has advised numerous students and collaborated on NIH-funded grants, including studies on cancer genomics and imaging. His lab focuses on developing statistical tools for complex, multi-source data analysis.

Yan Xiaمشاهده پروفایل

دانشیار

• Statistical Methods for Structural Equation Modeling
• Item Response Theory
• Categorical Data Analysis
• +۳ مورد دیگر

Dr. Yan Xia is an Associate Professor in the Department of Educational Psychology at the University of Illinois at Urbana-Champaign. He holds a Ph.D. in Educational Psychology and Learning Systems from Florida State University and completed a postdoctoral fellowship at Arizona State University's T. Denny Sanford School of Social and Family Dynamics. Key research areas: Statistical methods for structural equation modeling, item response theory, categorical/nonnormal/missing data analysis, and applications in special education, child development, organizational behavior, and sports psychology. Current appointments: Faculty, College of Education, University of Illinois (since 2025). Recent contributions: 2025 Monte Carlo simulation on developmental research generalizability, 2024 work on parallel analysis improvements, and meta-analysis of autism eye-tracking studies. His methodological work critically examines fit indices like RMSEA, CFI, and TLI in ordered categorical data contexts. Collaborations span autism research, educational interventions, and cross-cultural studies. Publications demonstrate expertise in DWLS/ULS estimation, factor retention, and missing data techniques. Scientific awards: Not explicitly mentioned in provided texts. Advising: Accepts new graduate students (2025). No student list provided.

Dingjing Shiمشاهده پروفایل

استادیار

• Bayesian Statistics
• Longitudinal Data Analysis
• Network Modeling
• +۲ مورد دیگر

Dingjing Shi is an Assistant Professor in Psychology at Georgia Institute of Technology, specializing in quantitative methods. Her research integrates Bayesian statistics with psychological measurement, focusing on longitudinal data analysis, network modeling, and ecological momentary assessment using wearable technology. She employs advanced statistical approaches to address methodological challenges in behavioral research. Her publications demonstrate expertise in developing novel psychometric approaches, including dimensionality reduction techniques for psychological networks and Bayesian generalizability theory for mixed-format assessments. Research addresses practical challenges in mobile health data collection and causal inference with incomplete data.

Zhiyong Zhangمشاهده پروفایل

استاد

• Bayesian methods
• Network analysis
• Big data analysis
• +۴ مورد دیگر

Zhiyong (Johnny) Zhang is a Professor of Quantitative Psychology at the University of Notre Dame and serves as the Quantitative Area Director. He is the director of the Lab for Big Data Methodology and a Fellow at the Institute for Educational Initiatives. His primary research areas include Bayesian methods, network analysis, structural equation modeling, and statistical computing. Dr. Zhang holds a Ph.D. in Quantitative Psychology from the University of Virginia. His work focuses on developing advanced statistical methodologies and software for education, health, management, and psychology. He is the Editor of the Journal of Behavioral Data Science and an Associate Editor of Multivariate Behavioral Research. His research trends span Bayesian inference, neural network applications, mediation analysis, and methodological innovations in factor analysis and latent growth models. He has contributed to software tools like WebPower for statistical power analysis and RAMpath for structural equation modeling. Zhang has received prestigious awards, including Fellow status in the American Psychological Association and election to the Society of Multivariate Experimental Psychology. His advising and grants include mentoring students in quantitative methodologies and leading research on text mining, emotion recognition, and big data applications. The Lab for Big Data Methodology under his direction advances interdisciplinary statistical approaches.

• Health Promotion
• Wellness Assessment
• Program Evaluation
• +۷ مورد دیگر

Kimberly A. Smith, PhD, is an Assistant Professor in the Department of Family and Community Medicine at the University of Alabama at Birmingham (UAB) School of Medicine. She also serves as Director of Evaluation at the UAB Center for Clinical and Translational Science (CCTS). Her roles include developing program evaluation models focused on outcome measurement, implementation, and study design. She holds a PhD in Education from the University of Southern Mississippi, with concentrations in Research, Evaluation, Statistics, and Assessment, along with master’s degrees in Educational Research Studies and Human Performance (Exercise Physiology). Dr. Smith’s research focuses on advancing translational research and outcomes in healthcare delivery, particularly through collaborative programs addressing cardiovascular health disparities and public health initiatives. She leads teams in designing research agendas, capacity assessments, and evaluation plans to drive evidence-based practices. Her expertise includes advanced statistical methodologies, program evaluation, and quality improvement projects across disciplines. Her professional activities include membership in the NSCA’s Long-Term Athletic Development Special Interest Group Executive Council and roles in journal reviewing (e.g., Journal of Clinical and Translational Science ). She has secured grants from the Alabama Department of Public Health and NIH-funded CTSA programs, emphasizing rural health engagement and cardiovascular disease reduction. As an educator, Smith teaches courses like Research Methods and Applied Multivariate Statistics, mentoring graduate students in doctoral committees. Her teaching philosophy emphasizes active, goal-oriented environments to foster lifelong learning and community impact.

Marley W. Watkinsمشاهده پروفایل

استاد پژوهشی

• Educational Psychology
• School Psychology
• Psychometrics
• +۵ مورد دیگر

Marley W. Watkins is a Research Professor in the Department of Educational Psychology at the School of Education, Baylor University. He has held previous leadership roles including Professor and Chairman at Baylor, Training Director and Professor at Arizona State University, and Professor-in-Charge of Graduate Programs in School Psychology at Pennsylvania State University. Dr. Watkins earned his Ph.D. in Educational Psychology and Measurements with a specialty in School Psychology from the University of Nebraska–Lincoln. His background includes 15 years as a practicing school psychologist before transitioning into academia. His primary research interests include psychometrics of assessment and diagnosis, individual differences, professional issues in school psychology, and computer applications in psychological research. He has made significant contributions to methodological training through accessible guides on exploratory factor analysis using major statistical platforms. Dr. Watkins has authored over 180 peer-reviewed journal articles and has been deeply involved in the scholarly community, serving as an editorial reviewer for more than 30 professional journals. His publications reveal a strong focus on statistical methodology, measurement validity, and data analysis techniques in educational and psychological contexts. Diplomate of the American Board of Professional Psychology Fellow of the School Psychology Division of the American Psychological Association Member of the Society for the Study of School Psychology Vietnam Service Medal Air Medal Distinguished Flying Cross Dr. Watkins has served as a mentor and academic leader in doctoral programs in school psychology, guiding graduate training at multiple institutions. He has not received explicit mention of grant funding in the text, but his extensive publication and editorial activity suggest sustained research productivity. He has also contributed to the field through authoring practical methodological textbooks. There is no mention of specific laboratories or research teams in the provided text, but his work appears to be centered on methodological innovation and psychometric rigor within educational psychology and school-based assessment.

Yuexiao Dongمشاهده پروفایل

دانشیار

• Sufficient Dimension Reduction
• High-dimensional Data Analysis
• Machine Learning
• +۳ مورد دیگر

Yuexiao Dong is an Associate Professor in the Department of Statistics, Operations, and Data Science at Temple University's Fox School of Business and Management. He holds a PhD from Pennsylvania State University (2009) and a Bachelor's in Mathematics from Tsinghua University. His research focuses on sufficient dimension reduction, high-dimensional data analysis, and machine learning. Dr. Dong has been funded by the NSF for his work on 'New Developments in Sufficient Dimension Reduction' and serves as a Gilliland Research Fellow. He is an Associate Editor for the Journal of Systems Science and Complexity since 2015. His research is published in top journals such as the Annals of Statistics and Biometrika . Key interests include developing methodologies for efficient data analysis, particularly in handling high-dimensional and non-Euclidean data. He teaches Quantitative Foundations for Data Science at the undergraduate level. Education: Bachelor’s in Mathematics, Tsinghua University PhD in Statistics, Pennsylvania State University Awards: Gilliland Research Fellow NSF Grant: New Developments in Sufficient Dimension Reduction Dr. Dong’s work bridges statistical theory and applications, emphasizing computational efficiency and real-world relevance. Recent projects include Bayesian model averaging, real-time dimension reduction, and expectile-based regression techniques.

Guangjian Zhangمشاهده پروفایل

دانشیار

• Psychometrics
• Factor Analysis
• Structural Equation Modeling
• +۳ مورد دیگر

Guangjian Zhang is an Associate Professor at the University of Notre Dame, affiliated with the Psychometrics and Dynamic Analysis (PDA) Lab. His primary research focuses on developing and improving quantitative methods in the behavioral and social sciences, particularly in factor analysis, structural equation modeling, time series analysis, and longitudinal data analysis. He holds a Ph.D. in Psychology from Ohio State University (2006) and an M.S. in Statistics from the same institution (2004), along with an M.Ed. in Psychology from Beijing University (1999) and a B. Med. from Tianjin Medical University (1994). Education: Ph.D. in Psychology, Ohio State University, 2006 M.S. in Statistics, Ohio State University, 2004 M.Ed. in Psychology, Beijing University, 1999 B. Med., Tianjin Medical University, 1994 His research interests emphasize enhancing measurement tools like questionnaires, with a focus on methodological advancements in psychometrics. His work spans dynamic factor analysis, multivariate time series, and computational efficiency in statistical methods. Notable contributions include the development of the EFAutilities package and bootstrap procedures for factor analysis validation. He teaches courses on statistics and advanced methodology in behavioral and social sciences. Dr. Zhang has published extensively on topics such as factor rotation, bootstrap standard errors, and longitudinal modeling. His research addresses challenges in psychological dynamics, clinical outcomes, and statistical methodologies, reflecting a commitment to bridging theoretical and applied quantitative psychology.

• Multilevel Modeling
• Educational Psychology
• Quantitative Research Methods
• +۳ مورد دیگر

Wen Luo is a Professor and Associate Department Head for Research in the Department of Educational Psychology at Texas A&M University. His work focuses on advanced statistical methodologies, including multilevel modeling, meta-analysis, and quantitative research techniques in education and psychology. He holds editorial roles at Journal of School Psychology and Statewide Standard . Luo has advised six doctoral students and contributed to over 50 peer-reviewed articles since 2007. His research addresses topics such as resilience in caregiving, peer victimization trajectories, and methodological advancements in statistical analysis. He has secured grants from NSF, Kellogg Foundation, and CPRIT for projects on educational equity, school improvement, and cancer screening disparities. Research Interests: Advanced multilevel modeling techniques Meta-analysis methodologies Quantitative methods in education and psychology Resilience and mental health Disparities in healthcare and education Key Projects: PEER: Laboratory-Oriented Online Mechatronics Curriculum Development (NSF, 2020–2022) Equity Toolkit for School Discipline Bias Assessment (William T. Grant Foundation, 2019–2020) Strategies for School Improvement in Diverse Settings (W.K. Kellogg Foundation, 2017–2020) His most recent publications emphasize transparency in multilevel modeling reporting, mediation effects in cross-classified data, and applications in healthcare disparities research.

Kevin McDonnellمشاهده پروفایل

استاد آموزشی

• Computer Science Education
• Visualization
• Visual Analytics
• +۲ مورد دیگر

Kevin McDonnell is the CS Undergraduate Program Director and a Teaching Professor in the Department of Computer Science at Stony Brook University. He is affiliated with the College of Engineering and Applied Sciences and focuses on Computer Science Education, Visualization, Visual Analytics, Geometric Modeling, and Computer Graphics. He has received numerous awards, including the 2024 Provost's Outstanding Lecturer Award and multiple Excellence in Teaching Awards. His teaching portfolio includes courses such as CSE 101, CSE 114, CSE 215, CSE 220, CSE 564, and ISE 218. His research emphasizes data visualization, visual analytics frameworks, and computational methods in environmental and biomedical contexts. Recent work includes studies on peptide identification algorithms, microbial community dynamics, and geo-spatial data exploration. Awards: SUNY Chancellor's Award (2021), NSF GAANN Fellowship (1998), and honor society memberships (Phi Beta Kappa, Golden Key). Advising & Grants: No formal student advisees listed, but contributes extensively to curriculum development and accessibility initiatives. Labs/Teams: Engaged with interdisciplinary projects in visualization and computer graphics, though specific lab affiliations are not detailed.

Yinghan Chenمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Educational Measurement
• Cognitive Diagnosis Models
• +۳ مورد دیگر

Yinghan Chen is an Associate Professor in the Department of Mathematics and Statistics at the University of Nevada, Reno (UNR), within the College of Science. Their research focuses on Bayesian statistics, cognitive diagnosis models, and educational measurement, with applications in public health and behavioral science. Key areas include latent variable modeling, health disparities among Asian American populations, and statistical methods for educational interventions. Dr. Chen’s work bridges theoretical statistical methodologies with practical applications in education and healthcare. Recent publications explore topics such as Bayesian hierarchical modeling for cognitive diagnostic assessments, multimodal data integration, and health behavior analysis using frameworks like the transtheoretical model. Their research also addresses algorithm optimization for network motif detection and model reduction techniques in psychometrics. No scientific awards or grants are explicitly listed in the provided materials. They currently advise no formally listed students but contributes to academic advising within their department. The full description provides additional details on their affiliations, educational background, and research trajectory.