جست‌وجوی استاد و پژوهشگر

Els Goetghebeurمشاهده پروفایل

استاد

• Biostatistics
• Causal Inference
• Quality of Care
• +۲ مورد دیگر

Els Goetghebeur is a Full Professor at Ghent University's Department of Applied Mathematics, Computer Science and Statistics. She chairs the UGent Expert Center for Statistics and serves as Chief-Editor of Statistics in Medicine , while leading the causal inference group within the STRATOS initiative. Her research emphasizes causal inference, survival analysis, and quality of care assessment in biomedical contexts. University: Ghent University Department: Applied Mathematics, Computer Science and Statistics Leadership Roles: Chair of UGent Expert Center for Statistics, Chief-Editor of Statistics in Medicine, Lead of STRATOS Causal Inference Group Her work spans methodological developments in causal inference and survival analysis, applied to biomedical challenges like cancer outcomes, transplant medicine, and tuberculosis diagnostics. Recent publications focus on propensity score methods, latent class analysis, and patient-reported outcome modeling across diverse clinical trials. Key trends in her research include: Improving causal inference techniques in observational studies Advancing survival analysis methods for complex clinical data Standardizing statistical practices in biomedical research through editorial leadership Developing frameworks for handling missing data in patient-reported outcomes Contributing to multicenter clinical trial designs for prostate cancer and solid tumors

Eli Ben-Michaelمشاهده پروفایل

استادیار

• Causal Inference
• Statistical Methods
• Computational Methods
• +۵ مورد دیگر

Eli Ben-Michael is an Assistant Professor in the Department of Statistics & Data Science and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. He is also affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC). Previously, he was a Post-Doctoral Fellow at Harvard University in the Institute for Quantitative Social Science and the Department of Statistics. Ben-Michael's research focuses on developing statistical and computational methods to solve practical issues in public policy and social science research. His work brings together ideas from statistics, optimization, and machine learning to create methods for credible and robust causal inference and data-driven decision making. His research spans multiple domains including healthcare policy, criminal justice reform, reproductive health, and education. His scholarly work shows a strong emphasis on causal inference methodology, with particular attention to synthetic control methods, balancing weights, policy learning, and sensitivity analysis. His recent publications demonstrate applications across diverse fields including healthcare, economics, social policy, and criminal justice. Ben-Michael completed his PhD in Statistics from UC Berkeley and earned his undergraduate degree in Computer Science and Statistics from Columbia University. His technical expertise includes developing open-source software, with contributions to the R packages 'augsynth' and 'multical' for synthetic control methods and multilevel calibration.

Glen McGeeمشاهده پروفایل

استادیار

• Biostatistics
• Epidemiology
• Environmental Health
• +۳ مورد دیگر

Glen McGee is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a PhD in Biostatistics from Harvard University and a BScH in Mathematics from Queen's University. His research focuses on developing statistical tools for epidemiology, environmental health, and health policy, with a particular emphasis on environmental mixture analysis, cluster-correlated data modeling, and outcome-dependent sampling methodologies. Education : PhD in Biostatistics, Harvard University BScH in Mathematics, Queen's University Research Interests : McGee advances methodologies for analyzing complex environmental mixtures and their health impacts, including incorporation of biological knowledge into statistical frameworks. His work addresses challenges in multigenerational studies, informative cluster sizes, and measurement error correction in case-crossover designs. Key application areas include hospital profiling, exposure misclassification, and longitudinal health data analysis. Research Trends : His publications emphasize Bayesian methods for mixture modeling, innovative sampling strategies for clustered data, and causal inference techniques. Notable contributions include frameworks for integrating biological pathways into environmental health analyses and developing efficient sampling approaches for healthcare performance evaluation. Grants & Labs : McGee collaborates on projects involving CMS data applications and maintains GitHub repositories like hospODS for hospital profiling methodology implementation. His work bridges statistical theory with practical public health applications, particularly in environmental epidemiology and healthcare analytics.

Carmel Hannanمشاهده پروفایل

دانشیار

• Family Dynamics
• Child Development
• Social Stratification
• +۴ مورد دیگر

Carmel Hannan is an Associate Professor at the University of Limerick's Department of Sociology and the Health Research Institute, specializing in family dynamics, child development, and social stratification. She currently collaborates with Prof Ross MacMillan on pandemic-related child mental health research funded by the Health Research Board. Dr. Hannan holds a DPhil from the University of Oxford and has held academic positions at Brasenose College Oxford and the Institute for Social and Economic Research (University of Essex). Her research focuses on family structure, inequality, and child well-being. Key projects include studies on intercultural marriage patterns in Ireland, pandemic impacts on youth mental health, and the effects of family formation on social mobility. She teaches advanced social research methods including multilevel modeling and propensity score matching. Education: PhD in Sociology (University of Oxford, 2009) Masters in Social Science (University College Dublin, 1997) Bachelor of Arts in Sociology & Economics (Maynooth University, 1995) Her awards include the ESRC Award and Irish Research Council grants. Hannan contributes to UN Sustainable Development Goals related to quality education and reduced inequalities through her work on family policies and child development.

Jennie E. Brandمشاهده پروفایل

استاد

• Social Stratification
• Inequality and Mobility
• Social Demography
• +۳ مورد دیگر

Jennie E. Brand is a Professor of Sociology and Professor (by courtesy) of Statistics and Data Science at the University of California, Los Angeles (UCLA). She co-directs the Center for Social Statistics (CSS) and serves as President of the Association of Population Centers (APC). Her affiliations include the Population Association of America (PAA), International Sociological Association (ISA), and editorial boards of Social Forces , Sociological Methodology , and Science Advances . Research Interests include social stratification, inequality, causal inference, and the socioeconomic impact of higher education. She explores Access to and returns from higher education Consequences of job displacement and disruptive events Machine learning applications in social science Intergenerational mobility and family effects Scientific Awards include the ASA Mathematical Sociology Outstanding Publication Award (2024), Sociological Research Association membership (2019), and the ASA Methodology Leo Goodman Mid-Career Award (2016). Grants awarded by NICHD, NIH, NSF, and Robert Wood Johnson Foundation support her work on population research, causal inference, and health disparities. She has mentored numerous graduate students, including Ph.D. recipients at NYU, Stanford, and Oxford.

Stephen Vaiseyمشاهده پروفایل

استاد

• Cultural Sociology
• Political Beliefs
• Moral Psychology
• +۳ مورد دیگر

Stephen Vaisey is a Professor of Sociology and Political Science at Duke University, where he also directs the Worldview Lab. His research focuses on the origins and consequences of moral and political beliefs, with a particular emphasis on cultural evolution and applied statistical methodologies. He integrates interdisciplinary approaches to study how cultural dynamics shape societal attitudes and behaviors, emphasizing the interplay between individual motivations and societal structures. His work often employs panel data and sophisticated statistical techniques to disentangle mechanisms of cultural change, including cohort succession and belief network formation. He teaches applied statistics courses through Statistical Horizons and leads the Code Horizons initiative, promoting methodological rigor in social science research. Key themes in his research include the dual-process model of culture in action, the role of moral worldviews in political behavior, and the quantitative measurement of cultural stability and change. His analyses frequently address topics like occupational meanings, vulnerability assumptions, and the impact of education on political orientation. While no specific scientific awards are listed, his contributions to cultural sociology and statistical methodology have been widely recognized through peer-reviewed publications. His advising and grant activities are not detailed here, though his Worldview Lab likely hosts ongoing collaborative research projects. His work bridges theoretical innovation with practical applications in cultural analysis and social policy.

Franziska Meinckمشاهده پروفایل

مدرس ارشد

• Violence against children
• Sexual and gender-based violence
• Intervention research
• +۱۲ مورد دیگر

Dr. Franziska Meinck is a Senior Lecturer (Associate Professor) at the University of Edinburgh’s School of Social and Political Science, with Honorary Associate Professor status at the University of the Witwatersrand School of Public Health and Extraordinary Professorship at North-West University, South Africa. Her research focuses on violence against children, sexual and gender-based violence, intergenerational transmission of violence, and HIV/AIDS-related health outcomes in vulnerable populations across Southern Africa and Australia. Education: DPhil in Social Interventions (University of Oxford), MSc in Evidence-Based Social Interventions (University of Oxford), BA in Social Work (Free University of Bolzano-Bozen) Her work spans prevalence studies of child maltreatment, intervention research, and development of global child abuse measures. She leads the ERC Starting Grant-funded project INTERRUPT_VIOLENCE (2020–2025) and contributes to the Australian Child Maltreatment Study as a Co-Investigator. Franziska is on the Board of the International Society for the Prevention of Child Abuse and Neglect (ISPCAN) and supervises student research in quantitative methods on violence, HIV prevention, and mental health. Recent Publications highlight trends in violence epidemiology, measurement validation (e.g., ICAST tools), and mental health outcomes linked to adverse childhood experiences. Key themes include intergenerational pathways, gender-based violence, and context-sensitive intervention strategies. Scientific Awards: ISPCAN C. Henry Kempe Award (2016) She mentors PhD students on topics like violence measurement, HIV adherence, and adolescent health. Her collaborations include institutions in the UK, South Africa, Australia, and the Balkans.

Olga Maslovskayaمشاهده پروفایل

دانشیار

• Survey Methodology
• Data Quality
• Survey Data Collection
• +۳ مورد دیگر

Dr. Olga Maslovskaya serves as an Associate Professor in Survey Research and Social Statistics at the University of Southampton within the Faculty of Social Sciences, Department of Social Statistics and Demography. She is a prominent Survey Methodologist specializing in data quality and survey data collection methodologies. As Principal Investigator of the Survey Data Collection Network (SDC-Net), she leads research on the impact of COVID-19 on survey methodologies and previously directed the GenPopWeb2 international network focused on transitioning to online data collection. Her research interests center on all aspects of survey methodology with particular emphasis on data quality, survey data collection techniques, non-probability-based surveys, and mixed-mode survey design. She has made significant contributions to understanding smartphone survey effects, response propensity modeling, and the development of online survey methodologies for demographic research. Analysis of her recent publications reveals a strong focus on methodological innovations in survey research, particularly examining data quality across different survey modes, developing advanced statistical techniques for addressing nonresponse, and advancing online survey design for demographic studies. Her work frequently addresses practical challenges in contemporary survey methodology while maintaining rigorous statistical standards. Dr. Maslovskaya has secured substantial research funding through multiple ESRC grants for projects including the Generations and Gender Survey in the UK and the Survey Data Collection Network. She serves as a Co-Investigator on major ESRC-funded initiatives and leads methodological workpackages for national survey implementations. She currently supervises PhD students Ni Kadek Oki Wahyu Krisnadevi and Norizan Abdullah in the Social Statistics and Demography program. Her external professional engagements include membership on the editorial board of the Journal of Survey Statistics and Methodology (2020-2024), active participation in the Royal Statistical Society (2018-2024), and membership in the European Survey Research Association (2021-2025). As a member of the Statistical Sciences Research Institute (S3RI), Dr. Maslovskaya contributes to the Methodological Innovation: Survey Methods and Official Statistics research group. Her current projects examine underrepresented population subgroups in surveys, patient perspectives on health data collection, and continuing research on survey data collection methods in the post-pandemic era.

• Statistics
• Categorical Data Analysis
• Survey Methodology
• +۳ مورد دیگر

Thomas Suesse is a Senior Lecturer at the School of Mathematics and Applied Statistics, University of Wollongong, with a career spanning institutions in Germany, New Zealand, and Australia. His academic journey includes a M.Sc. in Mathematics from Friedrich-Schiller-University Jena (2003) and a PhD in Statistics from Victoria University of Wellington (2008). Education: M.Sc. in Mathematics (FSU Jena, 2003), PhD in Statistics (VUW, 2008) His research focuses on Statistics, particularly Categorical Data Analysis, Social Network Modeling, and Spatial Statistics. Recent work explores variational Bayes inference for spatial autoregressive models, environmental extreme value detection algorithms, and the impact of missing data in statistical models. His publications span topics in public health, environmental science, and educational methodology. Key trends in his 15 most recent articles (2021-2025) include advancements in spatial statistics, applications of social network analysis to finance, and educational research comparing laboratory teaching objectives. He has also contributed to understanding pandemic effects on child health and activity patterns. Scientific Awards: 2008 VUW PhD thesis submission award; 2005-2008 VUW Postgraduate Scholarship. Thomas co-supervises PhD students on topics like spatial autoregressive modeling with missing data and variance estimation in mixture models. He has secured internal grants for computational infrastructure upgrades and studies on child movement behaviors. His professional affiliations include the Statistical Society of Australia.

Kangjie Zhouمشاهده پروفایل

پژوهشگر ارشد

• High-dimensional statistics
• Non-convex optimization
• Probability theory
• +۳ مورد دیگر

Kangjie Zhou is a Founder's Postdoctoral Fellow in the Department of Statistics at Columbia University. He completed his PhD in Statistics at Stanford University under the advisement of Professor Andrea Montanari, with additional collaborative work with Professor Tselil Schramm. He holds a Bachelor's degree in Mathematics from Peking University's School of Mathematical Sciences. His research focuses on high-dimensional statistics, non-convex optimization, probability theory, and deep learning. Notable contributions include work on computational-to-statistical gaps, theoretical aspects of deep learning, and high-dimensional probability. He has been recognized with the 2024 Department Dissertation Award. Key research themes include analysis of tensor decompositions, optimization landscapes in neural networks, and the interplay between overparametrization and computational tractability. His work bridges statistical theory with algorithmic development, often addressing foundational questions in machine learning and data science.

Jacob Hibelمشاهده پروفایل

دانشیار

• Sociology of Education
• Educational Stratification
• Migration and Immigration
• +۱۲ مورد دیگر

Jacob Hibel is an Associate Professor in the Department of Sociology at the University of California, Davis, within the College of Letters and Science. His research and teaching focus on the sociology of education, migration, and educational inequality, with a particular emphasis on children of immigrants and policy impacts on marginalized communities. Education: Ph.D., Sociology and Demography, Pennsylvania State University, 2009 M.A., Sociology and Demography, Pennsylvania State University, 2005 B.A., Sociology, Johns Hopkins University, 2003 His research explores how immigration shapes educational experiences and outcomes, including disparities in special education, gifted education, and English learner services. He investigates neighborhood and school-level effects on academic achievement and policy implementation, particularly in immigrant-rich communities. His methodological expertise lies in quantitative analysis and large-scale data. The recent publications highlight trends in educational stratification, migration, and policy, with a focus on equity, institutional accountability, and demographic change. His work spans U.S. and international (e.g., China) contexts, revealing structural factors in educational outcomes. Scientific Awards: William T. Grant Foundation Scholars Award, 2016 Outstanding Reviewer Award, Sociology of Education, 2015 Chancellor's Fellowship, UC Davis, 2017 Division of Social Sciences Dean's Award for Innovation in Research and Scholarship, 2015 Faculty Fellow, DSS Institute for Social Sciences, 2013-14 Faculty Excellence in Research Award, Purdue University, 2013 Professor Hibel has been supported by extramural funding, notably from the William T. Grant Foundation, to study policy implementation in education for immigrant children. He advises students in sociology and education research, though specific advisees are not listed. He teaches both undergraduate and graduate courses in quantitative methods and the sociology of education. He is affiliated with research clusters on Social Transformations and Race, Ethnicity and Immigration at UC Davis, contributing to interdisciplinary scholarship on social inequality and demographic change.

• Political Communication
• Comparative Communication Research
• Politics
• +۴ مورد دیگر

Dr. Ling Liu is an Assistant Professor (non-tenure-track) at the Waseda Institute for Advanced Study within Waseda University, Japan. Her academic journey includes a PhD in Journalism (2022, Waseda University), a Master's in Journalism (2014, Waseda University), and a Bachelor's in Japanese (2012, Shanghai University of Finance and Economics, China). She has also engaged in interdisciplinary research as a Visiting Researcher at The Ohio State University (2016-2018) and Masaryk University (2011-2012). PhD (2022, Waseda University) Master's (2014, Waseda University) Bachelor's (2012, Shanghai University of Finance and Economics) Visiting Researcher at The Ohio State University (2016-2018) Visiting Student at Masaryk University (2011-2012) Liu's research focuses on political communication, media trust, and selective exposure mechanisms in high-trust environments like Japan. Her comparative studies across Japan, the US, and Hong Kong reveal how media trust levels influence partisan selective exposure behaviors, challenging assumptions based on Western contexts. She has contributed to theoretical frameworks distinguishing hostile media perception from general media distrust. Her publications span journals like Communication Research and European Journal of Political Research Political Data Yearbook , with notable work on cross-national differences in confirmation bias (2019) and partisan selective exposure (2024). She leads projects funded by organizations such as the Japan Society for the Promotion of Science and Waseda University's Special Research Grants, including the Disinformation and Democracy in East Asia (2023-2029) and Media Trust Impacts on Exposure to Political Information (2023-2024). Dean's Academic Prize (2022, Waseda University) JSPS Grants-in-Aid for Scientific Research Waseda University Special Research Grants Suntory Foundation for Young Researchers Liu has taught courses such as Intermediate Seminar (Political Communication and Media Studies) and Political Science Experiment at Waseda University. She serves on the editorial board of the Bulletin of the Waseda Institute for Advanced Study and contributes as a peer reviewer for journals like Communication Research and conferences including the International Communication Association. Her laboratory collaborations include the Media Trust and Hostile Media Perceptions project (2024), which developed a typology of media trusters, and the Media Trust Impacts on Exposure to Political Information project (2023), advancing theoretical foundations for cross-cultural analysis of media trust dynamics.

Fang Hanمشاهده پروفایل

دانشیار

• Rank-based methods
• Graph-based methods
• Statistical optimal transport
• +۱۰ مورد دیگر

Fang Han is an Associate Professor in the Department of Statistics at the University of Washington and an Adjunct Professor in Economics. He is also an Affiliated Investigator at Fred Hutchinson Cancer Research Center. His research focuses on rank-based and graph-based methods , statistical optimal transport , and nonparametric/semiparametric regression . Education: Ph.D. in Biostatistics, Johns Hopkins University (2015) - supported by Google Ph.D. Fellowship M.S. in Biostatistics, University of Minnesota B.S. in Mathematics, Peking University Research interests span rank correlation , causal inference , nonparametric regression , high-dimensional statistics , and random matrix theory . His recent work includes adaptations of Chatterjee's correlation to manifold data and rigorous analysis of matching estimators' variance. Scientific awards include Bernoulli Society New Researcher Award (2021) Google Ph.D. Fellowship (2013-2015) Margaret Merrell Award (2015) National Science Review 2015 Best Paper (2016) Teaching includes courses like Advanced Theory of Statistical Inference , Time Series Analysis , and Foundations of Machine Learning . He serves as Associate Editor for Bernoulli and editorial board member for Dependence Modeling .

Orla Doyleمشاهده پروفایل

استاد

• Economics of human development
• Health economics
• Labor economics
• +۵ مورد دیگر

Orla Doyle is a Professor in the School of Economics at University College Dublin and a Research Fellow at the UCD Geary Institute for Public Policy. She serves as Director of the UCD Childhood and Human Development Research Centre and has held various academic positions at UCD since 2004, progressing from Postdoctoral Researcher to full Professor in 2023. Her academic journey includes appointments as Assistant Professor (2011-2017), Associate Professor (2017-2023), and multiple leadership roles including Deputy Head of School and Director of Teaching & Learning. BA (Mod) from Trinity College Dublin PhD from Trinity College Dublin Professor Doyle's research focuses on the economics of human development, particularly examining how early life conditions shape later life outcomes through rigorous experimental methods. Her work spans health economics, labor economics, political behavior, early child development, and policy evaluation methodologies. She emphasizes interdisciplinary collaboration across economics, psychology, medicine, and public health to advance understanding of human capital formation. Her publication record demonstrates consistent research productivity across multiple disciplines, with recent articles examining gender norms, cardiovascular disease prevention, rare disease economics, and early childhood interventions. The research shows a strong emphasis on experimental designs and causal inference methods to evaluate policy effectiveness, particularly in early childhood contexts. A significant portion of her work examines the long-term impacts of interventions through longitudinal studies. Professor Doyle leads major research initiatives including the Preparing for Life study, the longest-running early childhood field experiment in Europe. She has secured substantial research funding from diverse sources including the Irish Research Council, Medical Research Council, and international collaborations. Her professional activities include committee memberships, conference organization, and public engagement initiatives focused on policy relevance and gender equity in economics. She directs the UCD Childhood and Human Development Research Centre, leading a team of researchers focused on early intervention programs. Her work bridges academic research with practical policy applications, particularly through collaborations with Irish government agencies and health services to translate research findings into actionable programs for disadvantaged communities.

Ingeborg Waernbaumمشاهده پروفایل

استاد

• Biostatistics
• Causal Inference
• Observational Studies
• +۱۱ مورد دیگر

Ingeborg Waernbaum is a Professor at the Department of Statistics, Uppsala University, and a Researcher at the Institute for Evaluation of Labour Market and Education Policy (IFAU). Her work bridges biostatistics, causal inference, and epidemiology, with a focus on observational studies and methodological innovations. Primary Affiliation: Department of Statistics, Uppsala University Secondary Affiliation: IFAU (Institute for Evaluation of Labour Market and Education Policy) Her research centers on: Causal inference methodologies for observational data Propensity score modeling and doubly robust estimation Selection bias bounding and sensitivity analysis Applications to diabetes epidemiology and socioeconomic health disparities Recent publications (2023-2025) highlight her dual focus on theoretical advances in causal effect estimation (e.g., entropy balancing, mediation analysis) and applied biostatistical studies in diabetes and public health. She has developed R packages for selection bias bounding and covariate selection, emphasizing practical tools for researchers. Key methodological contributions include: Clarifying limitations of doubly robust estimators Advancing sensitivity analysis for unmeasured confounding Examining efficiency trade-offs in propensity score methods Applied work spans: Diabetes incidence trends and complications Socioeconomic determinants of health outcomes Fracture risk analysis in pediatric populations She actively engages in: Register data analysis Statistical software development (R packages) Methodological critiques and robustness studies