جستجوی استاد راهنمای دانشگاهی

• Learning Sciences
• Technology-Enhanced Learning
• Collaborative Learning
• +۴ مورد دیگر

Professor Nancy W. Y. Law is a leading scholar in Learning Sciences and Technology at the University of Hong Kong's Faculty of Education . She has served as Founding Director of the Centre for Information Technology in Education (CITE) (1998-2013) and currently holds the Chair of Learning Sciences and Technology . Her career spans over 25 years of international comparative studies, policy analysis, and large-scale educational innovation. Current roles: Associate Dean (Strategic Development), HKU Key affiliations: Sciences of Learning Strategic Research Theme (co-convener) Research Expertise Her work focuses on technology-enhanced learning , digital literacy , knowledge building communities , and multilevel educational change . She pioneered network models of teacher innovation and socio-technical frameworks for sustainable pedagogical transformation, particularly in STEM education and Cyberbullying prevention . Scientific Leadership With over 25 major research grants (2000-2023) exceeding HK$50 million, she leads projects like: Learning and Assessment for Digital Citizenship Jockey Club Self-directed Learning in STEM Design-Aware Learning Analytics A Fellow of the International Society of Learning Sciences and recipient of the HKSAR Humanities and Social Sciences Prestigious Fellowship , she has supervised numerous international comparative studies including SITES 2006, ICILS 2013, and longitudinal digital citizenship research (2019-2021).

• Foundations of Probability
• Randomness
• Graph Theory
• +۸ مورد دیگر

Lourens Waldorp is an Associate Professor at the University of Amsterdam within the Faculty of Social and Behavioural Sciences , specifically the Department of Psychological Methods . His research focuses on network theory, causal inference, and statistical modeling in psychology and neuroscience. University of Amsterdam IAS Fellow (2024) His research interests include: Network psychometrics Causal inference in psychological models High-dimensional statistical methods Dynamical systems in psychopathology Graph theory applications Signal processing for biophysical data The trends in his recent publications center on causal modeling, network analysis of psychopathology, and statistical techniques for time-series data. He has developed methods for perturbation graphs, moderated network models, and dynamic intervention frameworks. Notable scientific awards : IAS Fellowship for 6 months (2024) He advises PhD students like Kyra Evers and collaborates with researchers across disciplines, including J. Haslbeck , D. Borsboom , and O. Ryan . His work intersects with network theory and clinical psychology .

Katerina Marcoulidesمشاهده پروفایل

دانشیار

• Data Mining
• Structural Equation Modeling
• Longitudinal Data Analysis
• +۳ مورد دیگر

Dr. Katerina Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program at the University of Minnesota's Department of Psychology. She is affiliated with the Minnesota Population Center and serves as Co-Chair of the Structural Equation Modeling Special Interest Group (SEM SIG) for the American Educational Research Association. Her research focuses on advanced data mining and modeling techniques for complex longitudinal data, particularly applied to developmental processes in economically disadvantaged immigrant children. She holds a PhD in Quantitative Psychology from Arizona State University, an MA from UC Davis, and a BA from UC Santa Barbara. Education: PhD: Quantitative Psychology, Arizona State University MA: Quantitative Psychology, University of California, Davis BA: Psychology (minor in Education), University of California, Santa Barbara Research Interests: Dr. Marcoulides develops and applies statistical methods such as structural equation modeling (SEM), Bayesian synthesis, and data fusion to study developmental and educational processes. Her work emphasizes longitudinal data analysis, item response theory, and multilevel modeling. Recent projects include NIH-funded research on parenting, marginalization, and well-being during the pandemic. Awards: APS Rising Star Award (2021) NIH Grant Award Teaching & Collaboration: She teaches courses on SEM, multilevel modeling, and data analysis at the University of Minnesota. Previously at the University of Florida, she contributed to workshops on educational data mining and served as an APA Advanced Training Institute presenter. Her interdisciplinary collaborations span population studies, health inequities, and workforce research. Labs & Groups: She leads the Data Analytics and Visualization Lab and actively participates in the Minnesota Population Center, integrating computational and statistical innovations with real-world applications.

Eli Ben-Michaelمشاهده پروفایل

استادیار

• Causal Inference
• Policy Analysis
• Machine Learning Applications
• +۳ مورد دیگر

Eli Ben-Michael is an Assistant Professor jointly appointed in the Heinz College of Information Systems and Public Policy and the Department of Statistics & Data Science at Carnegie Mellon University. He is affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC), contributing to cutting-edge research at the intersection of statistics, policy analysis, and artificial intelligence. His educational background includes a PhD in Statistics from U.C. Berkeley and undergraduate studies at Columbia University where he earned a dual degree in Computer Science and Statistics. Prior to his current position, he completed a postdoctoral fellowship at Harvard University's Institute for Quantitative Social Science and Department of Statistics. Ben-Michael's research focuses on developing innovative statistical and computational methods for causal inference and policy evaluation, with particular emphasis on integrating machine learning techniques to address complex problems in public policy and social science. His work bridges theoretical statistics with practical applications in healthcare, criminal justice, education, and social policy. Current research directions include safe policy learning, sensitivity analysis for clustered data, and methodological innovations for the synthetic control method. His publication record shows a strong trajectory in top-tier journals including Journal of the American Statistical Association, Journal of the Royal Statistical Society, and Proceedings of ICML. Recent work demonstrates increasing focus on policy-relevant applications including abortion legislation impacts, pre-trial risk assessment, and healthcare disparities, while maintaining methodological rigor in causal inference frameworks. Ben-Michael has developed open-source software tools including augsynth and multical R packages, which implement his methodological contributions for synthetic controls and multilevel calibration weighting. These packages have been adopted by researchers in multiple disciplines for causal inference applications.

Roberta Fidaمشاهده پروفایل

استاد

• Workplace Ethics
• Organizational Behavior
• Workplace Aggression
• +۷ مورد دیگر

Professor Roberta Fida is the Head of the Leadership and Management Department at Aston Business School, part of Aston University in the UK. She holds a Professorship in Organisational Behaviour and Organisational Psychology, with prior roles including Professor of Work Psychology at the University of East Anglia and Chair of the Ethics Committee there. A Chartered Occupational Psychologist with the British Psychological Society, her research focuses on unethical workplace behaviors, moral disengagement, and workplace stress, particularly in healthcare contexts. She specializes in advanced data analytics like structural equation modeling and has led major grants including EDI initiatives and studies on workplace aggression policy mapping. Her work bridges academic research with practical applications to foster ethical and inclusive organizational cultures. Education: Laurea (5-year degree in Work and Organizational Psychology) from Sapienza University of Rome (2002) MA in Organizational Training (2004–2005) PhD in Psychology from Sapienza University (2006) Postgraduate Certificate in Higher Education Practice (UEA, 2017) Italian National Qualifications as Full Professor (2017) and Associate Professor (2013) Research Interests: Her core areas include moral disengagement, counterproductive workplace behaviors, stress resilience, healthcare leadership, and EDI. She has published over 60 articles and is listed in Stanford's Top 2% Scientists globally. Awards & Grants: NIHR Work & Health Development Award (2023–2024) for EDI Smart-Dash (£95,855) INAIL grants totaling over €1.1 million for psychosocial risk studies and work-related stress analyses ESRC funding for workplace wellbeing research (£659,540) Research Council of Canada grant for nurse leadership studies ($202,852) Teaching & Supervision: She has taught across undergraduate to postgraduate levels, including modules on organizational psychology, multivariate analysis, and conflict management. She supervises PhD students and has led course design for programs focusing on organizational wellbeing and research methods.

Hiroyuki Isekiمشاهده پروفایل

دانشیار

• Transportation Equity
• Land Use Planning
• Transit-Oriented Development
• +۲ مورد دیگر

Hiroyuki Iseki is Associate Professor of Urban Studies and Planning at University of Maryland's School of Architecture, Planning and Preservation, and Research Affiliate with the National Center for Smart Growth. His work examines interactions between transportation, land use, equity, and environmental sustainability. Research focuses on: Transit-oriented development impacts on firm location Equity in public transit finance Climate policy implementation in urban planning Active transportation infrastructure analysis Recent publications analyze post-pandemic transit demand shifts, campus multimodal conflicts, and bicycle accessibility modeling. Methodological strengths include spatial econometrics, GIS analysis, and longitudinal data approaches. Work increasingly addresses climate adaptation in transportation planning and EV charging grid impacts. Research contributes to urban policy through WMATA collaborations and Japanese municipal climate planning studies. Current PhD program directorship advances urban planning analytics training.

Daniel J. Bauerمشاهده پروفایل

استاد

• Quantitative Psychology
• Latent Variable Models
• Developmental Psychopathology
• +۶ مورد دیگر

Dr. Daniel J. Bauer is a Professor and Director of the Quantitative Psychology Program and L.L. Thurstone Psychometric Laboratory at the University of North Carolina at Chapel Hill. His research focuses on advancing quantitative modeling techniques for studying negative social behaviors, health outcomes, and psychopathology, with expertise in generalized and nonlinear latent variable models, including multilevel models, structural equation models, and mixture models. His work emphasizes methodological innovations such as Bayesian regularization, measurement invariance evaluation, and the integration of deep learning with psychometrics. Bauer advises doctoral students in quantitative psychology and collaborates with developmental psychology programs. He leads the Thurstone Laboratory, one of the oldest quantitative psychology training programs in the U.S., emphasizing rigorous methodological training and applications in behavioral sciences. Bauer’s research trends span computational advancements in latent variable analysis, regularization for bias detection, and dynamic modeling of developmental processes. His advising includes over a dozen doctoral students now in academia and industry roles. He contributes to labs and initiatives like the Center for Developmental Science, advancing interdisciplinary research in psychological measurement and intervention evaluation.

Guanglei Hongمشاهده پروفایل

استاد

• Causal Inference
• Social Policy Evaluation
• Child and Youth Development
• +۳ مورد دیگر

Guanglei Hong is a Professor at the University of Chicago, holding tenure in the Comparative Human Development Department and the Committee on Education. She chairs the University-wide Committee on Quantitative Methods in Social, Behavioral, and Health Sciences and the Committee on Education. Her research focuses on causal inference methodologies for evaluating educational and social policies, particularly mediation and moderation effects in multi-level longitudinal studies. She developed the RMPW and MMWS methods, widely used in causal mediation analysis. Hong holds a Master’s in Applied Statistics and a Ph.D. in Education from the University of Michigan. Education: Ph.D. in Education, University of Michigan, 2004 Master's in Applied Statistics, University of Michigan, 2002 Research Interests: Hong’s work centers on causal moderation and mediation, spillover effects, and sensitivity analysis in policy evaluation. She applies these methods to assess impacts of educational programs, contextual changes, and socioeconomic factors on child/youth development. Her monograph *Causality in a Social World* (2015) is a foundational text in the field. Awards: John Simon Guggenheim Fellowship (2021–2022) William T. Grant Scholar Award (2009–2014) NAE/Spencer Postdoctoral Fellowship (2006–2007) AERA Mary Catherine Ellwein Dissertation Award (2005) Teaching & Training: Hong teaches advanced quantitative methods courses, including causal inference and mediation analysis. She leads the NSF-funded SIARM for STEM institute, training researchers in computational methods for education research. Notable courses include *Advanced Topics in Causal Inference* and *Mediation, Moderation, and Spillover Effects*. Grants & Leadership: Hong leads major grants from NSF, IES, and private foundations. Her current projects include methodological advancements for multisite trials and sensitivity analysis in mediation. She has co-authored over 60 peer-reviewed articles and edited volumes, and serves on editorial boards of leading journals. Labs/Teams: Hong directs the Quantitative Methods Group at the University of Chicago, fostering interdisciplinary collaborations in causal inference and policy evaluation. Her work integrates statistical innovation with real-world applications in education and health sciences.

Angeline Lillardمشاهده پروفایل

استاد

• Developmental Psychology
• Montessori Education
• Child Social and Cognitive Development
• +۳ مورد دیگر

Dr. Angeline Lillard is Commonwealth Professor of Psychology and Director of the Montessori Science Program at the University of Virginia. She leads the Early Development Lab, focusing on children's social and cognitive development, particularly Montessori education's impact on learning and wellbeing. A Fellow of AAAS, APA, and APS, she earned her BA in English Literature from Smith College and PhD in Psychology from Stanford University. Research Interests: Dr. Lillard's work bridges Montessori pedagogy with developmental psychology, analyzing how play, educational environments, and culturally responsive teaching shape child outcomes. She explores standardized testing disparities, discipline equity, and the neurobiological underpinnings of pretend play. Key themes in her 15 most recent publications include Montessori's role in reducing educational inequality, the cognitive effects of fantasy in media, and the use of multilevel modeling to assess school discipline patterns. Her research spans preschool to adult wellbeing, emphasizing self-determination theory and longitudinal data. Scientific Recognition: Awarded the Nancy Staub Award for Puppetry Research (2024) Recognized for her book with the Cognitive Development Society Book Award (2006) James McKeen Cattell Sabbatical Fellow (2005-06) Albert Bandura Graduate Research Award (2016-17) Advising and Grants: Mentored 15+ graduate students including Lee LeBoeuf and Christina Carroll. Secured IES funding for a 600-child study on public Montessori preschools and Arnold Foundation support for kindergarten data collection.

• Human Resource Management
• Organizational Behavior
• HR Analytics
• +۷ مورد دیگر

Dr. Hanbo Shim is an Assistant Professor in the Department of Management at The University of Texas at Arlington, College of Business. He holds a PhD in Industrial Relations and Human Resources from Rutgers University and a BA in Mathematics and Economics from the University of Illinois at Urbana-Champaign. His research and teaching focus on Human Resource Management, Organizational Behavior, and HR Analytics. PhD, Industrial Relations and Human Resources, Rutgers University (2022) MS, Industrial Relations and Human Resources, Rutgers University (2019) MA, Human Resource Management, Rutgers University (2016) BA, Mathematics and Economics, University of Illinois (2012) Dr. Shim's research explores the temporal dynamics of employee performance, compensation systems, emotional intelligence, and social networks in organizations. He uses longitudinal analysis, multilevel modeling, meta-analysis, and computer simulation to examine how individual and interpersonal factors influence organizational outcomes. His work bridges HR analytics with behavioral science. His recent publications span topics such as green HRM, pay policy dynamics, emotional intelligence, and HR analytics education. These works reflect a strong interdisciplinary approach integrating psychology, sustainability, data science, and strategic management. His research has been presented at leading conferences including the Academy of Management and European Reward Management Conference. Award highlights include: Ralph Alexander Best Dissertation Award (2024) Innovative Teaching Award, Academy of Management HR Division (2022) Best Student Convention Paper Award (2020) Best Doctoral Conference Paper Award, Samsung Economic Research Institute (2020) Dr. Shim actively advises students through capstone and honors projects and serves on PhD and graduate studies committees. He has collaborated with SL Corporation on HR analytics and developed open-source educational materials. His service includes editorial board membership for Compensation & Benefits Review and ad hoc reviewing for top journals. He is also engaged in professional development workshops and public speaking, including for the Society for Human Resource Management at UTA. His lab and research activities emphasize simulation-based methods and real-world HR analytics applications.

Christoph Stadtfeldمشاهده پروفایل

دانشیار

• Social Network Analysis
• Dynamics of Social Networks
• Statistical Methods for Network Data
• +۲ مورد دیگر

Christoph Stadtfeld is Associate Professor of Social Networks at ETH Zurich's Department of Humanities, Social and Political Sciences and co-director of the ETH Social Networks Lab. His research examines social network dynamics, focusing on tie formation processes, network effects on individuals, and advanced statistical methodologies for longitudinal network analysis. Education: PhD from Karlsruhe Institute of Technology (2011) Postdoctoral researcher and Marie-Curie fellow at University of Groningen, University of Lugano, and MIT Media Lab (2011-2014) His work bridges sociology, statistics, and computer science to address fundamental questions about how social structures evolve and influence behavior. Key interests include relational event modeling, co-evolution of networks and attributes, and applications in mental health, political polarization, and scientific collaboration. He develops innovative methods for analyzing dynamic networks using cutting-edge computational approaches. Recent publications reveal strong emphasis on methodological rigor in temporal network analysis, with significant contributions to relational event modeling and dynamic network actor frameworks. His work increasingly addresses societal challenges including political polarization, mental health impacts of social isolation, and innovation dynamics in healthcare. Scientific awards: Raymond Boudon Award of the European Academy of Sociology (2017) Freeman Award of the International Network for Social Network Analysis (2021) As co-director of the ETH Social Networks Lab, Stadtfeld leads interdisciplinary research teams developing novel network methodologies. His work has been supported by prestigious fellowships including Marie-Curie funding, and he actively mentors graduate students in network science methodology and applications across diverse domains. The ETH Social Networks Lab serves as a hub for advancing network theory and methodology, with ongoing projects examining student networks during crises, scientific collaboration dynamics, and innovation ecosystems through the lens of network science.

Robert Frenchمشاهده پروفایل

پژوهشگر ارشد

• Educational Psychology
• Public Health
• Health Inequalities
• +۳ مورد دیگر

Dr. Robert French is a Senior Research Fellow at Cardiff University's School of Social Sciences. His work focuses on the intersection of health, education, and social policy, with a particular emphasis on data linkage methodologies to address disparities in healthcare and educational outcomes. He leads projects such as the ADR UK-funded initiative to establish frameworks for linking child health and education datasets in England and Wales. Key research interests include educational attainment dynamics, health inequalities among LGBTQ+ populations, and the impact of chronic conditions like diabetes on academic performance. His recent work explores factors influencing Special Educational Needs identification in Wales and the long-term outcomes of Graves' disease therapies. French's publications span epidemiology, public health, and social sciences, with methodological expertise in multilevel modeling and data integration. He collaborates with institutions like NHS Digital, the Royal College of Paediatrics and Child Health, and universities across Europe to advance evidence-based policymaking. His current projects include analyzing prescribing costs for diabetes treatments in Wales and examining cardiovascular risks linked to Graves' disease. French is also involved in public engagement, including workshops with Diabetes UK to inform ethical data usage frameworks. He holds leadership roles in multidisciplinary research teams and has secured grants from ADR UK and Wellcome Trust. His office is located at the University Hospital of Wales, Cardiff, reflecting his clinical and academic collaborations.

Grace Limمشاهده پروفایل

استادیار

• Organizational Behavior
• Diversity and Inclusion
• Social Class in Organizations
• +۵ مورد دیگر

Grace Lim is an Assistant Professor in the Department of Management at the Nanyang Business School, Nanyang Technological University, Singapore. Her research focuses on inclusion, diversity, and the empowerment of disadvantaged employees in organizations, particularly those from lower social class backgrounds and women. Education: Ph.D. in Business (Organizational Behavior and Human Resources), Singapore Management University B.Sc. in Psychology, National University of Singapore Her research employs a range of methodologies, including field surveys, experiments, archival data, and qualitative methods, to ensure robust and generalizable findings. She investigates how individuals can exercise agency to overcome structural disadvantages in the workplace, contributing to the fields of organizational behavior, human resources, and social psychology. Her recent publications reflect a strong trend in studying diversity, inclusion, and equity, with a focus on social class, gender, leadership, and employee well-being. The research spans interdisciplinary themes, integrating insights from psychology, management, and sociology to understand workplace dynamics. Scientific Contributions: Published in top-tier journals such as Organizational Behavior and Human Decision Processes and Academy of Management Annals Research on social class, gender, inclusion, and leadership has been influential in shaping organizational practices Dr. Lim is actively involved in advancing knowledge in organizational behavior and human resources. She has not supervised any listed students yet but continues to contribute through impactful research and academic engagement. She is a member of the Centre for Leadership and Cultural Intelligence at NTU, collaborating on initiatives related to cultural intelligence and inclusive leadership.

Christopher Han-Fai Setoمشاهده پروفایل

استادیار

• Criminology
• Hate Crime
• Violence
• +۵ مورد دیگر

Christopher Han-Fai Seto is an Assistant Professor of Sociology at Purdue University, having joined the Department of Sociology in 2023. His research sits at the intersection of criminology, public health, and the sociology of religion, with a strong computational and spatial-methods orientation. Education: Ph.D. in Criminology, Pennsylvania State University Research Interests: Dr. Seto’s substantive work revolves around hate crime, violence, health, and religion/morality , approached through ecological lenses. He leverages spatial data , large-scale network analysis , and big social & digital data to understand how community contexts shape deviance and well-being. Publication Profile: Between 2020 and 2025 he has published extensively in leading journals—including Justice Quarterly , Health & Place , Social Science & Medicine , British Journal of Criminology , Sociology of Religion , and Journal of Adolescent Health . The body of work reveals a consistent focus on anti-Asian hate incidents , Christian nationalism and gun policy , religious ecology of crime , and COVID-19 spillover effects . Contact & Web Presence: Office: BRNG 1156, Purdue University Email: setoc@purdue.edu Personal website: chseto.com

Jason Rightsمشاهده پروفایل

دانشیار

• Quantitative Methods
• Multilevel/Hierarchical Data Analysis
• Statistical Methods
• +۴ مورد دیگر

Dr. Jason Rights is an Associate Professor in the Department of Psychology within the Faculty of Arts at the University of British Columbia. His office is located in Kenny Room 2017 at 2136 West Mall, Vancouver, BC. He leads The Rights Lab, a quantitative methods research group dedicated to improving statistical practice in scientific research. His educational background includes: B.S. in Psychology and Mathematics from the University of North Carolina at Chapel Hill (2011) M.S. in Psychology (Quantitative Methods) from Vanderbilt University (2015) Ph.D. in Psychology (Quantitative Methods) from Vanderbilt University (2019) Dr. Rights' research focuses on addressing methodological complexities in multilevel/hierarchical data contexts where observations are nested (e.g., patients within clinicians, students within schools). His work spans several interconnected programs including developing R-squared measures for multilevel models, addressing issues with level-specific effects, exploring connections between multilevel and mixture models, and advancing latent variable model selection techniques. Analysis of his publication record reveals a consistent focus on methodological innovations in quantitative psychology, with particular emphasis on improving statistical techniques for hierarchical data structures. His work bridges theoretical statistical development with practical applications across psychology and related fields. Dr. Rights actively develops open-source software in R to implement his methodological contributions, making advanced statistical techniques accessible to researchers. The Rights Lab serves as the hub for his ongoing research program in quantitative methods development.