جست‌وجوی استاد و پژوهشگر

Menggang Yuمشاهده پروفایل

استاد

• Causal Inference
• Observational Studies
• Risk Prediction
• +۷ مورد دیگر

Dr. Menggang Yu is a Professor of Biostatistics at the University of Michigan School of Public Health. He holds academic leadership roles, including former Director of the Biostatistics Core at UW Carbone Cancer Center and Associate Director of the Center for Health Disparities Research. His expertise spans causal inference, clinical biostatistics, and precision health, with a focus on cancer and chronic disease research. Education: PhD in Biostatistics (University of Michigan, 2004), MS in Applied Statistics (Bowling Green State University, 1999), BS in Computational Mathematics (Fudan University, 1996). Research interests emphasize causal inference, risk prediction, and treatment selection, particularly in clinical trials and observational studies. His work addresses methodological challenges in healthcare, including optimal surveillance strategies and subgroup analysis. Key awards include the Distinguished Student Paper Award (ENAR 2023) and ASA Biometrics Section Travel Awards (2017, 2018). He collaborates widely, contributing to high-impact journals like Biometrics , Journal of the American Statistical Association , and Cancer Cell . Grants and lab involvement include leadership roles in cores for cancer research and health disparities. Courses taught include survival analysis, causal inference, and clinical trial biostatistics.

Selin Ahipasaogluمشاهده پروفایل

استاد

• Operational Research
• Convex Optimization
• Robust Optimization
• +۵ مورد دیگر

Prof. Selin Damla Ahipasaoglu is a Professor in Operational Research at the University of Southampton's School of Mathematical Sciences . She serves on the management team of the UKRI CDT SustAI (Artificial Intelligence for Sustainability) as Senior Tutor and Co-Lead for the Transportation and Logistics Theme . Her work bridges mathematical optimization with practical applications in sustainability, finance, and transportation systems. Research Interests : Convex Optimization Robust Optimization Discrete Choice Theory Experimental Design Machine Learning Current Research : Focused on robust optimization and its applications in discrete choice modeling, portfolio optimization, and transportation systems. She explores theoretical frameworks alongside real-world implementations, particularly through interdisciplinary projects like the UKRI CDT SustAI. Teaching : In the 2025/2026 academic year, she teaches MATH3017: Mathematical Programming and MATH2013: Operational Research II . She supervises PhD students in Mathematical Sciences, including Kexin Lai, Samuel Jericho Ward, and others.

Aad van der Vaartمشاهده پروفایل

استاد

• High-dimensional statistics
• Nonparametric Bayesian statistics
• Statistical genetics
• +۵ مورد دیگر

Aad van der Vaart is a distinguished Professor of Statistics at Delft University of Technology (since 2021). Previously, he held Full Professorships at Leiden University (2012–2021) and Vrije Universiteit Amsterdam (1996–2012). His research focuses on foundational statistical theory and applications, including high-dimensional statistics, Bayesian methods, inverse problems, and genomics. He has made seminal contributions to nonparametric Bayesian inference, empirical processes, and semiparametric theory. Van der Vaart has authored influential textbooks such as Asymptotic Statistics (1998) and Fundamentals of Nonparametric Bayesian Inference (2017, with S. Ghosal). His work bridges theoretical rigor and practical applications, with over 34,830 citations (Google Scholar, 2023) and an H-index of 60. Key honors include the Spinoza Prize (2015, Netherlands’ highest science award), DeGroot Prize (2020), and membership in the Royal Netherlands Academy of Sciences. His academic journey includes roles such as Miller Fellow at UC Berkeley (2000), visiting positions at leading universities, and leadership in statistical societies. His research group actively explores modern challenges in statistical theory and methodology, including causal inference, adaptive estimation, and large-scale data analysis. Notable grants include an ERC Advanced Grant (2012) for Bayesian inverse problems. Collaborations span academia and industry, emphasizing interdisciplinary impact. While specific lab affiliations are not explicitly stated, his work is rooted in foundational mathematical statistics with broad applicability.

Meimei Chenمشاهده پروفایل

پژوهشگر

• Causal Inference
• Machine Learning
• Econometrics
• +۳ مورد دیگر

Meimei Chen is a PhD candidate at the Macquarie Business School, Macquarie University , affiliated with the Macquarie University Centre for the Health Economy (MUCHE) and Data Horizons Research Centre. Her research integrates causal inference , machine learning , and econometrics to analyze health policies and model individual preferences in large-scale health data. Education: Master of Statistics with Excellence (UNSW Sydney), Bachelor of Science in Statistics (University of Science and Technology of China), and Master by Research with High Distinction (Macquarie University) Her PhD thesis focuses on combining machine learning and semiparametric methods to estimate causal effects and preference heterogeneity. She has published in JAMA Network Open and the Journal of Nutrition, Health and Aging , with presentations at major conferences including AHES, AAG, ANZESG, ACE, and IHEA. Research Outputs: 2 peer-reviewed publications (2023-2025) Key Contributions: Novel multi-task machine learning models for health evaluation Her work has been recognized through competitive presentation slots at AHED Workshops (2022, 2024) and the IHEA Congress (2025). She possesses advanced skills in statistical modeling and programming with complex datasets.

James A. Duffyمشاهده پروفایل

دانشیار

• Econometrics
• Macroeconometrics
• Time Series Analysis
• +۵ مورد دیگر

James A. Duffy is an Associate Professor of Economics at the University of Oxford and the Andrew Glyn Tutorial Fellow at Corpus Christi College. He joined Corpus in 2016 after a postdoctoral fellowship at Nuffield College, Oxford, and holds dual appointments in the Department of Economics and his college. His educational background includes: PhD in Economics from Yale University (2014) Undergraduate studies in Economics and Mathematics at the University of Sydney Duffy's research centers on econometrics, with emphasis on macroeconometrics and time series analysis. He develops statistical methods for economic models involving nonlinear or highly persistent time series data, common in macroeconomics and finance. His work spans econometric theory , mathematical statistics , cointegration , and structural macroeconomic models , addressing inference challenges in strongly dependent processes. His 2016-2024 publications in premier journals reveal consistent innovation in time series methodology, particularly in unit root processes, fractional integration, and nonlinear cointegration. Key contributions include Tobit modeling for dynamic systems, robust inference for weakly nonstationary data, and discrete choice estimation techniques, bridging theoretical rigor with empirical applications. At Oxford, Duffy serves as course convenor for Quantitative Economics and lectures for the MPhil programme on instrumental variables, generalized method of moments, and maximum likelihood estimation. He also provides undergraduate tutorials in Microeconomics and Quantitative Economics at Corpus Christi College, integrating research insights into teaching.

Deniz Baglanمشاهده پروفایل

دانشیار

• Econometrics
• Nonparametric Econometrics
• Semiparametric Models
• +۴ مورد دیگر

Deniz Baglan is an Associate Professor of Economics and serves as the Director of Undergraduate Studies in the Department of Economics at Howard University, which is part of the College of Arts and Sciences. He holds a Ph.D. in Economics from the University of California, Riverside, and contributes significantly to econometric theory and its applications in macroeconomics and finance. Research Interests: His primary research lies in nonparametric and semiparametric econometric methods, particularly applied to panel data models. He investigates issues related to unobserved heterogeneity, dynamic modeling, and structural inference in economic and financial contexts. His work spans both theoretical developments and empirical applications, focusing on macroeconomic dynamics and empirical finance. Publication Trends: His recent scholarly output shows a consistent focus on advancing nonparametric techniques for panel data, including identification, estimation, and testing in models with fixed effects, time-varying parameters, and nonlinear structures. These contributions are published in notable journals such as Journal of Macroeconomics and Studies in Nonlinear Dynamics and Econometrics , reflecting a strong trajectory in methodological econometrics. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: While specific students and grant details are not listed, his role as Director of Undergraduate Studies suggests active involvement in academic mentoring and curriculum development. As a faculty member publishing in top-tier econometrics journals, he likely contributes to research supervision at graduate levels and may participate in externally funded research projects. Labs and Research Teams: There is no mention of specific labs, research centers, or collaborative teams in the provided information.

Joseph Antonelliمشاهده پروفایل

استادیار

• Causal Inference
• High-Dimensional Modeling
• Bayesian Methods
• +۳ مورد دیگر

Joseph Antonelli is an Assistant Professor of Statistics at the University of Florida , where he has been since 2018. He holds a PhD in Biostatistics from Harvard University (2015) and completed postdoctoral training there until 2018. His research focuses on causal inference, high-dimensional modeling, Bayesian methods, spatial statistics, and applications in environmental health and criminology. Education: B.S. in Statistics, University of Florida (2011) M.S. in Biostatistics, Harvard University (2013) Ph.D. in Biostatistics, Harvard University (2015) Research Interests: Professor Antonelli develops statistical methods to address complex causal questions in health and social sciences. His work emphasizes robust approaches to confounding adjustment, spatial and environmental data analysis, and policy evaluation. He applies these methods to study air pollution effects, policing policies, and opioid policy impacts, bridging theoretical advancements with real-world applications. Key Contributions: His articles span causal inference techniques, Bayesian modeling, and spatial analysis, with recent work addressing racial bias in policing and air pollution mixtures. His research has been recognized with awards from the Health Effects Institute, JSM Biometrics, and ISBA. Awards: 2020 Health Effects Institute Young Investigator Award 2020 Journal of Speech, Language, and Hearing Research Editors Award 2014 ENAR Distinguished Student Paper Award Advising and Grants: He advises over 15 graduate students and has led grants totaling $3.5M, including NIH funding for auditory deficits in children and CDC studies on firearm policy effects. He also serves as Associate Editor for Bayesian Analysis .

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.

• Latent variable models
• Survival analysis
• Event history analysis
• +۷ مورد دیگر

Brian Ling is an Assistant Professor in the Department of Mathematics and Statistics at Queen's University. His office is located in Jeffery Hall, Room 405. He holds a Ph.D. from Columbia University, an M.Phil. from The Chinese University of Hong Kong, and a B.Sc. from the same institution. His research focuses on statistical inference for incomplete and event history data, with current projects involving biased samples, cross-sectional sampling data, and process data. Methodological interests include nonparametric and semiparametric statistics, latent variable models, and shape-constrained inference. No specific awards, grants, or advising records are listed. His work is centered on advancing statistical methodologies applicable to complex data structures.

Arman Oganisianمشاهده پروفایل

استادیار

• Bayesian Statistics
• Causal Inference
• Missing Data
• +۴ مورد دیگر

Arman Oganisian is an Assistant Professor of Biostatistics at Brown University, holding the Thomas J. & Alice M. Tisch Professorship. He earned his PhD in Biostatistics from the University of Pennsylvania (2021), and prior degrees from the same institution (MS, 2018) and Providence College (BA, 2013). His research focuses on Bayesian nonparametric methods for causal inference in sequential treatment strategies, particularly in diseases requiring long-term management like oncology and HIV. Research & Grants: His work is funded by PCORI contracts for Bayesian methods in sequential treatments (AML leukemia) and incomplete covariates. Notable grants include NIH R01 awards for HIV care cascade analysis and osteoporosis pharmacotherapy. He develops tools like the causalBETA R package for survival outcome causal inference. Awards: Salomon Faculty Research Award (2022), Saul Winegrad Dissertation Award (2021), ENAR Student Paper Award (2020). Teaching: Leads courses on Bayesian methods and statistical inference at Brown. Service: Serves on NIH review panels, PCORI, and academic committees, including Brown’s MPH admissions. Labs & Collaboration: Engages in collaborative projects on Bayesian causal modeling and nonparametric methods, with a focus on healthcare applications in oncology and rheumatology.

Edsel Peñaمشاهده پروفایل

استاد

• Mathematical Statistics
• Survival Analysis
• Reliability
• +۵ مورد دیگر

Edsel Peña is a Professor and Chair of the Department of Statistics at the University of South Carolina, within the McCausland College of Arts and Sciences. He holds a BS (Magna Cum Laude) from the University of the Philippines Los Baños and a PhD from Florida State University. His research focuses on Mathematical Statistics, Survival Analysis, Reliability, and Biostatistics. Peña has held roles including visiting professorships at the University of Michigan and served as Executive Secretary of the Institute of Mathematical Statistics (2017–2023) and NSF Program Director (2020–2023). Education: Bachelor of Science in Statistics, University of the Philippines Los Baños (1979) PhD in Statistics, Florida State University (1986) Research Interests: Peña’s work spans foundational statistical theory, survival analysis applications, and high-dimensional inference. He emphasizes methodological development in reliability systems and nonparametric approaches for recurrent events. His recent work includes optimal correlation-based prediction and asymptotic theory for recurrent event models. Awards: Fellow, American Statistical Association Member, International Statistical Institute Fellow, Institute of Mathematical Statistics Michael Mungo Graduate Teaching Award (University of South Carolina) Severino and Paz Koh Lectureship Award Grants & Leadership: Secured funding from NSF, NIH, and EPA. Served on editorial boards of top journals including the Journal of the American Statistical Association and Scandinavian Journal of Statistics. Currently chairs the Department of Statistics at USC.

Anindya Royمشاهده پروفایل

استاد

• Time Series Analysis
• Nonparametric Bayesian Analysis
• High-dimensional Estimation and Inference
• +۳ مورد دیگر

Anindya Roy is a Professor of Statistics in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). His research focuses on time series analysis, Bayesian methods, high-dimensional statistics, and applications in biomedical and engineering domains. He serves as an Area Editor for Statistical Analysis and Data Mining , and on editorial boards for Sankhya, Ser. A and Statistics in Medicine . His research interests include nonparametric Bayesian approaches, geometric constraints in statistical models, and sequential experimental design. He has received research funding from the National Science Foundation (NSF DMS–2210282). Recent work addresses privacy-preserving data synthesis, causal inference in time series networks, and fetal growth modeling. Key contributions include methodologies for high-dimensional time series with directed acyclic graphs, Bayesian approaches for relational graph estimation, and statistical techniques for medical applications such as cyclotron ion source development and cervical cancer screening risk prediction. Notable collaborative efforts involve proteomic analysis of chemical mixtures, oncology trial design, and biomarker studies. His work bridges theoretical statistics with applied problems in health sciences and engineering, emphasizing interdisciplinary impact.

Asokan Variyathمشاهده پروفایل

استاد

• Statistical Process Control
• Experimental Design
• Machine Learning
• +۲ مورد دیگر

Asokan Variyath is a Professor of Statistics at Memorial University of Newfoundland, specializing in experimental design, statistical process control, and educational statistics. Education: Ph.D. in Statistics, University of Waterloo (2006) S.D.P Fellow (SQC & OR), Indian Statistical Institute (1991) Post Graduate Diploma in SQC & OR, Indian Statistical Institute (1988) M.Sc. Agricultural Statistics, Kerala Agricultural University (1987) Research Interests: Develops novel statistical methods in experimental design, quality control, and multivariate analysis. Applied research spans fisheries management, reliability engineering, and computational statistics. Created educational statistical applets to enhance student learning. Publication Trends: Recent publications focus on Bayesian methods, statistical education tools, and applications in fluid dynamics. Methodological work emphasizes variable selection, longitudinal data analysis, and nonparametric techniques. Leadership: Director of Statistical Consultancy Centre since 2010. Former Chair of Master of Data Science program and Deputy Head for Graduate Studies. Received Dean's Service Awards in 2023 and 2024.

Gongjun Xuمشاهده پروفایل

دانشیار

• Latent Variable Models
• Psychometrics
• Statistical Learning
• +۶ مورد دیگر

Gongjun Xu is an Associate Professor of Statistics and courtesy Associate Professor of Psychology at the University of Michigan. He leads the Master's Program in Applied Statistics and holds positions in the Department of Statistics within the College of Literature, Science, and the Arts (LSA). His research focuses on latent variable models, psychometrics, statistical learning, and high-dimensional statistics. Xu completed his Ph.D. in Statistics at Columbia University and B.S. at the University of Science and Technology of China (USTC). His research interests include advanced statistical methodologies for educational and psychological measurement, such as cognitive diagnosis models, item response theory, and network data analysis. He has contributed to high-dimensional inference, survival analysis, and machine learning applications in healthcare. Xu serves as Co-Editor-in-Chief of the Journal of Educational and Behavioral Statistics and holds editorial roles in multiple top-tier journals. Xu has been recognized with prestigious awards including the AERA Research Methodology Award (2025), ICSA President’s Citation (2024), and COPSS Emerging Leader Award (2023). His team advises numerous graduate and undergraduate students, many of whom pursue academic and industry roles in statistics, data science, and education. Key collaborations include work on educational testing, health analytics, and AI-driven scientific methods via the Schmidt AI in Science Fellowship. He oversees a vibrant research group with active projects in latent variable modeling, differential item functioning detection, and scalable Bayesian methods for large-scale assessments. His lab emphasizes interdisciplinary applications spanning education, healthcare, and social sciences.