جست‌وجوی استاد و پژوهشگر

• Causal Inference
• Bayesian Modeling
• Biostatistics
• +۷ مورد دیگر

Xiaoxiao Zhou is an Assistant Professor in the Department of Biostatistics at the University of Alabama at Birmingham (UAB), affiliated with multiple centers including the Center for Outcomes and Effectiveness Research and Education (COERE), Center for Clinical and Translational Science (CCTS), and the Global Center for Craniofacial, Oral and Dental Disorders (GC-CODED). She holds a PhD in Statistics from The Chinese University of Hong Kong (2022) and completed a postdoctoral fellowship at Duke University's Department of Statistical Science. Her research focuses on causal inference, Bayesian methods, longitudinal data analysis, and survival analysis, with applications in Alzheimer’s disease, cardiovascular conditions, and neurodegenerative disorders. Dr. Zhou’s work integrates advanced statistical techniques with medical and behavioral data, including neuroimaging and latent variable modeling. Key areas include handling intercurrent events in clinical trials, causal mediation analysis, and joint modeling of longitudinal and survival outcomes. She collaborates widely with clinicians and biostatisticians to address real-world challenges in healthcare and disease progression studies. Her scholarly contributions span over a dozen peer-reviewed articles, emphasizing methodological innovations in biostatistics and their practical applications. She advises students such as Zhenying Ding and actively participates in academic committees. Outside academia, she enjoys outdoor activities like mountain hiking and weight lifting.

Keming Yuمشاهده پروفایل

استاد

• Quantile Regression
• Bayesian Statistics
• Survival Analysis
• +۱۲ مورد دیگر

Keming Yu is a Professor and Chair in Statistics at the Department of Mathematics, Brunel University London, within the College of Engineering, Design and Physical Sciences. He is also the Impact Champion for REF in Mathematical Sciences. He joined Brunel in 2005 after holding positions at the University of Plymouth, Lancaster University, and The Open University. He earned his PhD from The Open University and earlier degrees in Mathematics and Statistics from Chinese institutions. PhD in Statistics – The Open University, UK MSc in Statistics – China BSc in Mathematics – China His research centers on quantile regression, Bayesian modeling, survival analysis, and statistical methods for big data . His work spans applications in health, finance, environment, and social sciences. He has made significant contributions to robust and flexible regression methods, including expectile, mode, and censored quantile regression. His recent publications (2023–2025) show a strong focus on streaming data, spatiotemporal modeling, high-dimensional data, and Bayesian methods . He frequently publishes in top-tier journals such as the Journal of the Royal Statistical Society Series A, B, and C , Statistica Sinica , and Computational Statistics and Data Analysis . His work often involves collaboration with international researchers, especially in China and Europe. He has contributed to methodological discussions in leading statistical journals, demonstrating active engagement with the academic community. His work on financial risk, environmental statistics, and health data analysis reflects interdisciplinary impact. Reviewed and contributed to discussions on safe testing, confidence sequences, and betting-based inference. Active in developing methods for nonignorable missing data, censored models, and functional covariates. He supervises PhD students and is involved in teaching and curriculum development, including as Course Director for the MSc Statistics with Data Analytics. His research is supported by extensive publication output and academic service. He leads or contributes to research on Bayesian models, robust regression, and scalable methods for big data , often involving collaborations in interdisciplinary teams. His lab or research group focuses on statistical methodology development with real-world applications.

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.

Zeynep Işıl Kalaylıoğluمشاهده پروفایل

استاد

• Bayesian Statistics
• Environmental Statistics
• Health Statistics
• +۳ مورد دیگر

Prof. Zeynep Işıl Kalaylıoğlu is a Professor of Statistics at the Department of Statistics, Middle East Technical University (METU), Ankara, within the Faculty of Arts and Sciences. She holds a Ph.D. in Statistics from North Carolina State University (2002) and has held roles including Associate Professor (2014–2022), Assistant Professor (2009–2014), and researcher at the National Cancer Institute (2002–2007). She has served in administrative roles such as Assistant to the Department Head and member of METU’s Wind Energy Research Center and Faculty Board. Ph.D.: Statistics, North Carolina State University, 2002 M.S.: Statistics with Computational Engineering minor, North Carolina State University, 1999 B.S.: Statistics with Computer Engineering minor, Middle East Technical University, 1995 Her research focuses on Bayesian statistical methodologies for environmental and health phenomena. Key areas include spatial/temporal modeling of atmospheric and ecological variables, prediction of migratory patterns (e.g., wind direction, bird movements), and risk modeling for breast cancer using mammogram data. She develops flexible models for circular and extremal data, emphasizing applications in ecology, epidemiology, and environmental science. Her recent publications address topics like goodness-of-fit tests for statistical distributions, predictive model selection for circular data, and Bayesian approaches for missing covariates in generalized linear models. Current projects include mobile apps for olive harvest optimization and frost预警 systems, reflecting her commitment to applied statistical solutions. Labs/Teams: She leads the BayeZian Research Group, collaborating with METU’s Ecosystem Research Center on interdisciplinary projects. Her teaching expertise spans mathematical statistics, Bayesian theory, and statistical inference at undergraduate and graduate levels.

Elizabeth Schifanoمشاهده پروفایل

دانشیار

• Biostatistics
• Statistical Genomics
• High-Dimensional Data Analysis
• +۳ مورد دیگر

Elizabeth Schifano is an Associate Professor in the Department of Statistics at the University of Connecticut. She focuses on biostatistics, statistical genomics, and high-dimensional data analysis with applications in medical research and genetic studies. Her work addresses complex challenges in missing data imputation, longitudinal studies, and variable selection in high-dimensional genomic datasets. Research Interests: Development of Bayesian methods for nonignorable missing data in clinical trials Statistical methodologies for analyzing SNP sets in genetic association studies Online updating algorithms for streaming big data Survival analysis with monotone partial likelihood Publications reflect her expertise in statistical genomics and advanced computational methods. She collaborates with researchers in public health and biomedical sciences to advance methodological innovations in data analysis. Office: AUST 317 | Contact: (860) 486-6143

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.

Daniel Kowalمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Environmental Health and Policy
• Public Health
• +۹ مورد دیگر

Daniel Kowal is an Associate Professor in the Department of Statistics and Data Science at Cornell University, effective July 2024. Previously, he served as the Dobelman Chair Assistant Professor at Rice University. His research focuses on Bayesian methodology for complex dependent data, including functional, time series, and spatial datasets, with applications in environmental health, epidemiology, finance, and astronomy. He develops scalable algorithms for high-dimensional data and interpretable uncertainty quantification. His work has been recognized with the Blackwell-Rosenbluth Award (2021) and ARO Young Investigator Award (2020). Education: Ph.D. in Statistics (Cornell University), M.S. in Statistics (Cornell University), B.A. in Mathematics (Washington University in St. Louis). Research interests include Bayesian models for prediction/inference, decision theory, discrete data analysis, and scalable approximations. Key areas of application: environmental health policy, wearable devices, economics, biomedical engineering, and astronomy. Recent grants include NSF-funded projects on adaptive dependent data models and Army Research Office initiatives on Bayesian approximations. He has supervised multiple Ph.D. students, including Yunan Gao, Thomas Sun, and Brian King. His software contributions include R packages like countSTAR, SeBR, and lmabc for Bayesian regression and data synthesis. Awards include Lindley Prize Honorable Mention (2024), Arnold Zellner Thesis Award (2018), and numerous student paper awards from ASA sections.

D. Sunshine Hillygusمشاهده پروفایل

استاد

• American Political Behavior
• Campaigns and Elections
• Survey Methodology
• +۴ مورد دیگر

D. Sunshine Hillygus is a Professor of Political Science at Duke University and serves as Interim Director of the Social Science Research Institute and Director of the Duke Initiative on Survey Methodology. She is also a Professor in the Sanford School of Public Policy. Ph.D., Stanford University (2003) M.A., Stanford University (2000) Her research focuses on American political behavior, campaigns and elections, survey methods, public opinion, and information technology's impact on politics. Recent work explores voter turnout barriers, youth engagement, and digital survey methodologies. The 15 most recent publications highlight methodological innovations in survey research, election analysis, and political communication studies. Key themes include voter turnout modeling, social media's role in campaigns, and survey data integrity. Scientific recognition includes the Howard D. Johnson Distinguished Teaching Award (2019) and the Robert E. Lane Award (2009). Current grants address election studies, social media polarization, and youth voter turnout solutions.

Daeyoung Kimمشاهده پروفایل

استاد

• Statistics
• Asymmetric Dependence
• Categorical Data Modelling
• +۴ مورد دیگر

Daeyoung Kim is a Full Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. He holds a Ph.D. in Statistics from The Pennsylvania State University (2008), and M.S. and B.S. degrees in Statistics from Korea University (2002, 2000). His research focuses on statistical dependence modeling via copulas, likelihood inference in categorical data, mixture modeling, and interdisciplinary applications in food science and environmental engineering. He has authored/co-authored numerous publications, including a book on Direction Dependence in Statistical Modeling . His work spans statistical methodology development and collaborative research in environmental and biomedical sciences. Education: Ph.D. in Statistics, The Pennsylvania State University, 2008 M.S. in Statistics, Korea University, 2002 B.S. in Statistics, Korea University, 2000 Research Interests: Kim’s work emphasizes asymmetric dependence analysis, categorical data modeling, and mixture models. He applies these methods to food science (e.g., lipid metabolism) and environmental challenges (e.g., cyanobacterial bloom modeling). His statistical contributions include copula-based regression and likelihood inference techniques for incomplete data. Collaborative Projects: Kim has contributed to studies on antimicrobial additives’ effects on colonic inflammation, lipid metabolism modulation in disease models, and the role of dietary fats in tumorigenesis. His interdisciplinary collaborations span environmental engineering, nutrition science, and biomedical research. Awards: Noted for award-winning teaching and research excellence, though specific honors are not detailed in the provided text.

Pengfei Liمشاهده پروفایل

استاد

• Finite Mixture Models
• Empirical Likelihood
• Experimental Design
• +۵ مورد دیگر

Roles & Affiliations Professor at Department of Statistics and Actuarial Science , University of Waterloo since 2012. Serves as Associate Editor for Canadian Journal of Statistics and Metrika . Previously held roles at University of Alberta (2008-2011) and University of British Columbia (2008 postdoc). Education PhD in Statistics (2007) - University of Waterloo MA in Statistics (2004) - Nankai University BSc in Statistics (2001) - Nankai University Research Focus Specializes in statistical methodologies including finite mixture models, empirical likelihood, experimental design, biased sampling problems, capture-recapture data, and density ratio models. Current interests include statistical inference under constraints and analysis of non-probability survey samples. Awards CRM-SSC Prize in Statistics (2022) Institute of Mathematical Statistics Fellow (2022) Multiple awards for teaching and research excellence from University of Waterloo Students & Grants Advised over 10 PhD/Master’s students. Active grants include NSERC funding since 2009 and startup grants from University of Waterloo. Supervises postdocs in statistical methodology development.

Changbao Wuمشاهده پروفایل

استاد

• Survey Methodology
• Complex Surveys
• Empirical Likelihood Methods
• +۶ مورد دیگر

Changbao Wu is a Professor and Chair of the Department of Statistics and Actuarial Science at the University of Waterloo, within the Faculty of Mathematics. His research focuses on complex survey design and analysis, with expertise in empirical likelihood methods, resampling techniques, and missing data problems. He has developed R packages to implement these methods and is a Fellow of the American Statistical Association and Elected Member of the International Statistical Institute, receiving the 2012 CRM-SSC Prize in Statistics. Education: PhD in Statistics (1999) from Simon Fraser University under Prof. Randy Sitter. He has been at Waterloo since 1999. Research Interests: Design and analysis of complex surveys Semiparametric/nonparametric methods Empirical likelihood methods Resampling (jackknife/bootstrap) Missing data and measurement error Survey sampling methodology Professional Roles: Associate Editor: Survey Methodology (2006–), Biometrika (2008–), Journal of Nonparametric Statistics (2011–) Member: Statistics Canada’s Advisory Committee on Statistical Methods Recent Publications Trends: Focus on non-probability survey samples, causal inference, and pseudo-empirical likelihood methods. Recent work addresses challenges in combining non-probability and probability samples, doubly robust estimation, and calibration techniques. Awards: Fellow of ASA (since 2020) Elected ISI Member (since 2012) CRM-SSC Prize (2012) Grants and Advising: Extensive grants related to survey methodology and statistical inference. Advises on the International Tobacco Control (ITC) China Survey and collaborates on large-scale studies like the Canadian Longitudinal Study on Aging (CLSA). Labs/Teams: Leads research in survey statistics and statistical methodology at the University of Waterloo, contributing to interdisciplinary projects involving health, economics, and social sciences.

Soutir Bandyopadhyayمشاهده پروفایل

استاد

• Spatial and Environmental Statistics
• Time Series Analysis
• Bootstrap/Resampling Methods
• +۴ مورد دیگر

Soutir Bandyopadhyay is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines. He holds a PhD in Statistics from Texas A&M University, an M.Stat from the Indian Statistical Institute, and a B.Sc in Statistics from St. Xavier’s College. His research focuses on spatial and environmental statistics, time series analysis, bootstrap methods, and large sample theory, with applications in energy systems, climate science, and machine learning. Recent work includes advancements in spatial extremes modeling using neural networks, risk analysis in oil and gas flowlines, and statistical frameworks for energy load forecasting. He has organized major conferences including the 2023 IISA Annual Conference and the 2024 workshop on uncertainty in civil/environmental systems. As Director of the Data Science Program at Mines since 2025, he leads interdisciplinary initiatives. Key contributions include development of the National Climate Database (NCDB), bias-correction methods for solar radiation data, and R packages like glmfitmiss for handling missing data in GLMs. His students have achieved notable recognitions, including Fulbright awards and ASA competition honors.

• Statistics
• Biostatistics
• Missing Data Analysis
• +۷ مورد دیگر

Dr. Biao Zhang is a Professor in the Department of Mathematics and Statistics at the University of Toledo, College of Natural Sciences and Mathematics. His research focuses on advanced statistical methodologies, particularly in missing data analysis, empirical likelihood, and semiparametric modeling, with applications to clinical trials and biostatistics. University: University of Toledo School: College of Natural Sciences and Mathematics Email: biao.zhang@utoledo.edu Research Interests Dr. Zhang specializes in Statistics , Biostatistics , and Missing Data Analysis . His work develops innovative methods for: Empirical likelihood in clinical trials Density ratio models for ROC curve analysis Causal inference with missing responses Semiparametric estimation techniques Robust methods for treatment effect estimation Publications Overview His extensive publication record (59 papers, 1994-2024) reveals expertise in: Empirical likelihood applications ROC surface methodologies Multivariate analysis Case-control study frameworks Nonparametric and semiparametric approaches Statistical software development Co-author network includes Steven T. Haller , Christopher Cooper , and Pamela S. Brewster .

Balgobin Nandramمشاهده پروفایل

استاد

• Bayesian Statistics
• Survey Sampling
• Small Area Estimation
• +۶ مورد دیگر

Balgobin Nandram is a Professor of Statistics at the Department of Mathematical Sciences , Worcester Polytechnic Institute (WPI). With a PhD in Statistics from the University of Iowa (1989) and a Master's from Imperial College London (1981), his career spans academic leadership, global research collaborations, and methodological innovation. BS, Mathematics & Physics, University of Guyana (1977) BA, Mathematics Education, University of Guyana (1979) MS, Statistics, Imperial College London (1981) PhD, Statistics, University of Iowa (1989) His research focuses on Bayesian statistics applied to survey methodology, small area estimation, categorical data analysis, and nonignorable missing data. He has developed computational methods for health statistics and data science, with significant applications at the National Center for Health Statistics (NCHS) and agricultural surveys. While his 2010-2002 refereed publications highlight Bayesian hierarchical models for BMI data and COPD mortality mapping, his invited presentations (2018-2015) emphasize logistic regression in small areas, multinomial count analysis, and projective inference. These works bridge theoretical advances with practical applications in public health and survey research. 2003 : Fellow, American Statistical Association (ASA) 2006 : SPAIG Award (WPI-NCHS partnership) 2014 : Visiting Global Scholar, Kyungpook National University 2004 : Sigma Xi membership As an advisor, he has mentored PhD students at WPI, the University of the Philippines, and Kyungpook National University, though specific advisees are not named. His grants include CDC collaborations on health monitoring and NCHS research fellowships. He leads international research teams in South Korea, the Philippines, and India, including partnerships with Yonsei University, DLSU, and Banaras Hindu University. His work at NCHS (1999-2000) and subsequent sabbatical (2003/2004) solidified his role in health statistics.

Jae-Kwang Kimمشاهده پروفایل

استاد

• Survey Sampling
• Missing Data Analysis
• Semiparametric Estimation
• +۳ مورد دیگر

Jae-Kwang Kim is a Professor in the Department of Statistics at Iowa State University's College of Liberal Arts and Sciences. He holds Fellow status in both the American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS), was named LAS Dean's Professor (2020-2022), and currently serves as President of the Korean International Statistical Society (KISS) for 2023-2024. His educational background includes: PhD in Statistics, Iowa State University (2000) MS in Statistics, Seoul National University (1993) BS in Computer Science and Statistics, Seoul National University (1991) Professor Kim's research spans survey sampling, missing data analysis, semiparametric estimation, and causal inference. He has pioneered methods for handling nonignorable nonresponse, developed fractional hot deck imputation techniques, and advanced data integration approaches combining probability surveys with big data sources. His work bridges theoretical statistics with practical survey methodology challenges. His recent publications (2022-2025) reveal a concentrated focus on semiparametric imputation using Gaussian mixture models, functional calibration under non-probability sampling, and bootstrap inference for complex survey designs. Key themes include handling multivariate missing data, addressing selection bias in voluntary samples, and developing robust estimation techniques under informative sampling frameworks. His major recognitions include: Gertrude M Cox Award (2015) LAS Dean's Professor (2020-2022) Fellow of the American Statistical Association Fellow of the Institute of Mathematical Statistics Professor Kim has held academic appointments at Hankook University of Foreign Studies (2002-2003), Yonsei University (2004-2008), and KAIST (2016-2018), alongside his primary position at Iowa State University (2008-present). His pre-academic career included roles as Mathematical Statistician at the US Census Bureau (1999-2000) and Senior Statistician at Westat (2000-2001).