جست‌وجوی استاد و پژوهشگر

• Mathematical Methods for Economy
• Financial Econometrics
• Probability
• +۴ مورد دیگر

Cecilia Mancini is a Full Professor at the Department of Economics, University of Verona. Her research focuses on Mathematical Methods for Economy , Financial Econometrics , and Statistical Modeling , with specific emphasis on stochastic processes, hypothesis testing, and optimization techniques in finance. She teaches courses like Financial Mathematics and Mathematical Finance across bachelor’s, master’s, and PhD programs. Research Interests include econometric modeling of financial markets, robust volatility estimates, and stochastic optimization for investment choices. She is involved in projects such as SHIELD: Securing Decentralized Finance and Remote Healthcare Systems . Member of Department Director Faculty Faculty Board of PhD in Economics and Finance Member of Mathematics and Data Science Teaching Committee Active in Research Advisory Board (RAB)

Chang-Xing Maمشاهده پروفایل

دانشیار

• Biostatistics
• Statistical Genetics
• Experimental Design
• +۳ مورد دیگر

Chang-Xing Ma, PhD, is an Associate Professor in the Department of Biostatistics at the School of Public Health and Health Professions, University at Buffalo. He serves as Co-Director of the MPH Biostatistics Concentration. With a PhD from Nankai University (1997), his expertise spans statistical genetics, experimental design, and advanced biostatistical methods. His research focuses on developing statistical methodologies for correlated data analysis, particularly in clinical trials and public health contexts. Dr. Ma’s work addresses challenges in analyzing paired and stratified data, including bilateral/unilateral studies in ophthalmology, pediatric care, and environmental health. Notable contributions include exact testing methods for proportion ratios, confidence interval construction for correlated outcomes, and applications in genetic epidemiology. His interdisciplinary projects integrate statistical theory with practical medical and environmental research. Key publications explore vaccine uptake barriers among pediatric patients, frailty assessment in veterans, and the efficacy of ketamine in delirium management. He has developed SAS macros for logistic regression modeling and contributed to studies on CFTR modulation in cystic fibrosis and statin-associated muscle symptoms. His work emphasizes rigorous statistical approaches to address real-world healthcare challenges.

Albert Vexlerمشاهده پروفایل

استاد

• Biostatistics
• Epidemiology
• Statistical Methods
• +۷ مورد دیگر

Albert Vexler, PhD, is a Professor in the Department of Biostatistics at the University at Buffalo, within the School of Public Health and Health Professions. He specializes in advanced statistical methodologies with applications to biomedical and health-related research. His work focuses on biomarker analysis, nonparametric testing, ROC curve methodologies, and empirical likelihood techniques. Education: PhD in Statistics and Probability Theory from the Hebrew University of Jerusalem (2003), followed by a Postdoctoral Research Fellowship at the National Institute of Child Health and Human Development (2004–2007). Research Interests: Dr. Vexler’s research spans biostatistical methods, including optimal designs, censored data analysis, sequential analysis, and Bayesian approaches. He develops tools for handling complex data structures such as skewed biomarker measurements and limits of detection in clinical studies. His work emphasizes robust statistical inference and practical applications in clinical trials and public health. Publications: His recent work highlights advancements in hypothesis testing, biomarker evaluation, and statistical computing. Key areas include density-based empirical likelihood tests, multivariate dependence measures, and computational tools for analyzing biomedical data. Professional Activities: He serves as an Associate Editor for Biometrics and Journal of Applied Statistics , and has reviewed for top-tier journals such as the Journal of the American Statistical Association and Biometrika . He contributes to statistical software development, including R packages like dbEmplikeGOF and testforDEP . Lab/Teams: Leads a research group focused on innovative statistical methods for health sciences, collaborating on projects involving clinical trial design, epidemiological modeling, and precision medicine.

Deping Yeمشاهده پروفایل

استاد

• Convex Geometry
• Geometric Analysis
• Quantum Information Theory
• +۴ مورد دیگر

Deping Ye is a Full Professor in the Department of Mathematics and Statistics at Memorial University of Newfoundland (since 2021). He holds a PhD from Case Western Reserve University (2009), with postdoctoral training at the University of Missouri-Columbia (2009-2010) and a Fields-Ontario postdoc (2010-2011). His research focuses on convex geometry, geometric analysis, quantum information theory, functional inequalities, and nonparametric statistics. He has received the 2017 JMAA Ames Award for groundbreaking work in Orlicz-Brunn-Minkowski theory. Education: B.S., Shandong University (2000) M.S., Zhejiang University (China) Ph.D., Case Western Reserve University (2009) Research Interests: Convex geometry, geometric analysis, quantum information theory, functional inequalities, random matrices, and statistical models with measurement error. His work bridges pure mathematics (e.g., affine isoperimetric inequalities) and applied fields (e.g., compressed sensing and quantum computing). Grants: 2018-2023: 5-year NSERC grant 2012-2018: 6-year NSERC grant with early-career supplement 2011-2013: Memorial University startup grant Publications: Over 30 peer-reviewed articles, including seminal work on L_p affine isoperimetric inequalities and entanglement thresholds in quantum states. Recent topics include Orlicz-Brunn-Minkowski theory, p-capacitary problems, and quantum state volume analysis. Awards & Roles: Associate Editor, Canadian Journal of Mathematics (2024–2026) Early promotions: Associate Professor (2016), Full Professor (2021)

• Biostatistics
• Genetics
• Epigenetics
• +۳ مورد دیگر

Su Chen, PhD, is an Associate Professor in the Department of Biostatistics at the University of Nebraska Medical Center's College of Public Health since 2021. Previously, she held a tenured position as Associate Professor of Mathematical Sciences at the University of Memphis. She earned her PhD in Statistics from Oklahoma State University (2013), alongside advanced degrees in Quantitative Financial Economics (2012) and Statistics (2009), and a BS in Statistics from Wuhan University of Technology (2007). Her research focuses on non-/semi-parametric statistical modeling applied to large-scale human genetics/epigenetics and intelligent tutoring systems. Key areas include DNA methylation analysis, statistical consultation for research design, and learning analytics. Over 10 years, she has provided statistical guidance for study planning, power calculations, and data analysis across diverse projects. Dr. Chen's publications span advanced statistical methods (e.g., kernel density functionals) and interdisciplinary applications like epigenetic influences on health outcomes. Her work bridges biostatistics with education technology, particularly analyzing user engagement in intelligent tutoring systems. She is affiliated with the American Statistical Association, Institute of Mathematical Statistics, and International Biometrics Society.

Yaqing Chenمشاهده پروفایل

استادیار

• Metric Space Valued Data
• Non-Euclidean Data Analysis
• Functional/Longitudinal Data
• +۵ مورد دیگر

Yaqing Chen is an Assistant Professor in the Department of Statistics at Rutgers, The State University of New Jersey. Her primary affiliation is with the School of Arts and Sciences (SAS). She holds a Ph.D. and M.S. from the University of California, Davis, and a B.S. from Peking University. Her research focuses on statistical methodologies for complex data structures, including metric space valued data, non-Euclidean objects, functional/longitudinal data, and distributional data. Applications span longitudinal studies, biological and medical sciences, and social sciences. Key areas of innovation include distance profile-based inference, gradient synchronization in brain connectivity analysis, and Wasserstein regression techniques. Recent work emphasizes neurodevelopmental applications, such as analyzing brain volume trajectories in children and their associations with maternal education. Methodological contributions include R packages like 'fdaconcur' and 'frechet' for functional data analysis and non-Euclidean statistics. Her research bridges theoretical advancements with practical tools for high-dimensional and non-standard data types. Dr. Chen has secured grant funding supporting her work on temporal dynamics of disease spread and neurodevelopmental modeling. She collaborates with interdisciplinary teams in neuroscience and public health. Her lab focuses on developing scalable statistical methods for high-dimensional biomedical data.

Roger Kleinمشاهده پروفایل

استاد

• Econometrics
• Semiparametric Models
• Estimation and Testing Methods
• +۳ مورد دیگر

Roger Klein is a Professor of Economics at Rutgers University, specializing in Econometrics with a focus on semiparametric models. He previously worked at Bellcore and Bell Labs before joining Rutgers. His research emphasizes estimation and testing of semiparametric models, decision theory, and estimation risk in portfolio decisions. Klein holds a Ph.D. in Economics from Yale University and serves as an Associate Editor for Econometric Theory . His research interests include semiparametric estimation methodologies, endogenous treatment effects, and econometric model specification. He has contributed to advancements in recursive differencing techniques, instrumental variable estimation, and bias correction in econometric models. Recent work explores applications in healthcare policy, robotic surgical system adoption, and intergenerational schooling effects. Notable recent publications include Recursive Differencing for Estimating Semiparametric Models (2024), Factors Associated with Hospital Decisions to Purchase Robotic Surgical Systems (2020), and Estimation of Marginal Effects in Semiparametric Selection Models (2015). His work consistently bridges theoretical econometric advancements with practical applications in labor, health, and policy analysis. Klein has no listed awards but maintains active editorial and research roles. His advising focus is not explicitly detailed, though collaborations with co-authors like Chan Shen and Frank Vella highlight ongoing research partnerships. No dedicated lab or team is noted in the provided materials.

Jong Soo Leeمشاهده پروفایل

دانشیار

• Statistics
• Applications of statistics
• Health Communication
• +۶ مورد دیگر

Jong Soo Lee is an Associate Professor in the Department of Mathematics & Statistics at the University of Massachusetts Lowell (UML), affiliated with the Kennedy College of Sciences. His expertise lies in Statistics and its applications, with research focusing on areas such as health communication, nutritional epidemiology, and statistical methodology development. He holds an office in Southwick 315A and can be reached at JongSoo_Lee@uml.edu . Lee’s research interests emphasize the practical application of statistical techniques to real-world problems. His work spans diverse domains, including the influence of figurative language on dietary choices, the relationship between vitamin D levels and mental health in Puerto Rican populations, and methodological advancements in functional data analysis. He has collaborated on interdisciplinary projects involving health communication, civil engineering, and optimization algorithms. His publications reflect a commitment to both theoretical and applied research, with recent contributions to journals like Health Communication and The Journal of Nutrition . Earlier work includes studies on structural engineering and optimization theory. Despite his extensive publication record, no scientific awards or grants are explicitly listed in the provided materials. Lee does not have any listed advisees or students, and no details about affiliated laboratories or research teams are provided in the text.

Ron Mittelhammerمشاهده پروفایل

استاد

• Econometrics
• Statistics
• Applied Economics
• +۶ مورد دیگر

Ron Mittelhammer is a Professor at the Washington State University , affiliated with the School of Economic Sciences . He holds dual appointments in economics and statistics, with a career spanning over four decades at WSU, including leadership roles as Dean and Interim Co-Provost. Education : PhD in Agricultural and Resource Economics (WSU, 1978), MS and BS in Agricultural Economics (Rutgers, 1974 and 1972) Research Interests focus on econometrics , statistical modeling , and applied economic analysis , particularly in agricultural economics, food safety, and policy impacts. His work integrates information theory and Bayesian methods to address nonlinearities and uncertainty in economic data. Recent Publications span liquor market dynamics, econometric methodological advancements, food safety disparities, and international trade analysis, reflecting his interdisciplinary approach. Scientific Awards : V. Lane Rawlins President’s Award for Distinguished Lifetime Service (2019), AAEA Fellow (2012), WSU Regents Professor (2004), AAEA Distinguished Graduate Teaching Award (2001) Professional Leadership includes roles as Dean of CAHNRS (2014-2018), Director of the School of Economic Sciences (2004-2010), and editorial service in journals like the American Journal of Agricultural Economics. He has authored foundational texts such as Mathematical Statistics for Economics and Business and An Information Theoretic Approach to Econometrics .

Xuan Liمشاهده پروفایل

پژوهشگر

• Neuroscience
• Neuroimaging
• Functional Connectivity
• +۳ مورد دیگر

Dr. Xuan Li is a Researcher at the Research Center Jülich GmbH, affiliated with the Institute of Neuroscience and Medicine (INM), specifically within the department of Brain and Behavior (INM-7). Their work focuses on advanced neuroimaging techniques and computational methods to study functional brain networks, with an emphasis on understanding neural connectivity patterns in both healthy and diseased states such as Alzheimer’s disease. Dr. Li’s research integrates machine learning approaches like non-negative matrix factorization and sparse coding to uncover complex network structures in resting-state and task-based fMRI data. Key research interests include developing predictive frameworks for naturalistic viewing experiments, analyzing hemispheric asymmetries, and identifying biomarkers through alterations in brain network topology. Their contributions span methodologies for detecting overlapping communities in brain networks, improving functional connectivity metrics, and enhancing signal processing in neuroimaging data. Dr. Li’s publications reflect a strong interdisciplinary approach, combining neuroscience, network science, and computational methods to address fundamental questions in brain function and disease. They are actively involved in the analysis of large-scale neuroimaging datasets to advance our understanding of individual brain variability and its implications for diagnostics and personalized medicine.

Hanlin Shangمشاهده پروفایل

استاد

• Functional data analysis
• Nonparametric and semiparametric statistics
• Bayesian econometrics
• +۲ مورد دیگر

Hanlin Shang is a Professor and ARC Future Fellow at Macquarie University’s Department of Actuarial Studies and Business Analytics. He holds affiliations with the Data Horizons Research Centre, Transforming Energy Markets Research Centre, Centre for Risk Analytics, and Frontier AI Research Centre. His research focuses on functional data analysis, nonparametric and semiparametric statistics, Bayesian econometrics, computational statistics, and demographic forecasting. Education: Ph.D. in Statistics (Monash University, 2010) and Bachelor with First Class Honours in Statistics (La Trobe University, 2006). He has held postdoctoral roles at the University of Southampton and Monash University. Currently, he serves as an editor for journals like the Australian and New Zealand Journal of Statistics and Journal of Computational and Graphical Statistics . Key achievements include the ARC Future Fellowship (2024), Mollie Holman Doctoral Medal (2010), and visiting scholarships at institutions such as Columbia University and the United Nations Population Division. His research has produced over 140 publications, with recent work emphasizing mortality forecasting, functional time series analysis, and demographic modeling. Supervision spans postdoctoral researchers and PhD candidates, many of whom now hold academic or industry roles. Current projects include functional panel data analysis and feature learning for high-dimensional functional time series, supported by ARC grants. He collaborates internationally, addressing challenges in data governance, climate risk, and actuarial science.

• Nonstationary time series econometrics
• Nonparametric statistics
• Econometric Theory
• +۴ مورد دیگر

Qiying Wang is a Professor of Statistics and Econometrics in the School of Mathematics and Statistics at the University of Sydney. His research focuses on nonstationary time series econometrics, nonparametric statistics, econometric theory, local time theory, and self-normalized limit theory. He is a member of the Statistics research group. Research Interests : Nonstationary time series econometrics Nonparametric statistics Econometric Theory Local time Theory Self-normalized limit theory Publications : Dr. Wang has published extensively in top journals such as Econometric Theory, Journal of Econometrics, and Statistica Sinica. Recent work includes advancements in nonlinear cointegration, optimal bandwidth selection, and stochastic processes analysis. His contributions span theoretical econometrics, time series modeling, and statistical methodologies. Awards/Grants : While no explicit awards are listed, his prolific publication record underscores sustained academic excellence. His research has been supported through collaborations with institutions and co-authors globally. Lab/Teams : Engaged with the Statistics research group at the University of Sydney, contributing to collaborative projects in econometric theory and statistical inference.

Fabrizio Leisenمشاهده پروفایل

استاد

• Bayesian Inference
• Bayesian Nonparametric Methods
• Objective Bayesian Analysis
• +۳ مورد دیگر

Fabrizio Leisen is a Professor of Statistics at King’s College London, Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. Previously, he held positions at the University of Nottingham (Professor), University of Kent (Reader), and institutions in Spain and Italy. He earned a PhD in Mathematics from the Università di Modena e Reggio Emilia, specializing in Probability. His research focuses on Bayesian inference, nonparametric methods, objective Bayesian analysis, and foundational statistical theory, with notable contributions to predictive constructions, knockoff procedures, and stochastic processes. He serves as an Associate Editor for Bayesian Analysis , Statistics and Probability Letters , and Statistical Methods and Applications . Leisen’s work bridges theoretical and applied statistics, emphasizing Bayesian nonparametric priors for complex data structures and model selection. His recent articles address survival analysis, prior elicitation without subjective inputs, and algorithmic advancements in Bayesian computation. He collaborates globally, including co-supervising a PhD student at the Università di Bologna. His teaching includes Computational Statistics and Probability and Statistics Skills sessions. Research interests span Bayesian foundations, model selection via knockoffs, stochastic processes, and interdisciplinary applications like genomic and biostatistical analysis. His lab is affiliated with King’s Statistics group, focusing on MCMC methods, Bayesian nonparametrics, and experimental design.

Xinyi Xuمشاهده پروفایل

استاد

• Bayesian Statistics
• Model Uncertainty and Selection
• Decision Theory
• +۴ مورد دیگر

Xinyi Xu is a Professor of Statistics and Vice Chair for Graduate Studies at The Ohio State University's Department of Statistics. She is affiliated with the Institute for Population Research and serves as an Associate Editor for Bayesian Analysis . Her research focuses on Bayesian hierarchical modeling, decision theory, and high-dimensional data analysis with applications in biostatistics, sociology, and finance. Education Ph.D. in Statistics, University of Pennsylvania (2005) M.A. in Statistics, University of Pennsylvania (2003) B.S. in Mathematics, University of Science & Technology of China (2001) Research Interests Bayesian hierarchical modeling Statistical decision theory Model selection and averaging Applications in biostatistics, sociology, and finance Grants National Science Foundation grants (DMS-2015552, DMS-1613110, DMS-1209194) National Institute of Child Health grants National Center for Research Resources grants Labs/Teams Collaborates with the Translational Data Analytics center at Ohio State and interdisciplinary experts.

Yuan Zhangمشاهده پروفایل

دانشیار

• Statistical Network Analysis
• Nonparametric Inference
• U-statistics
• +۴ مورد دیگر

Yuan Zhang is an Associate Professor of Statistics at The Ohio State University, affiliated with the Department of Statistics and Translational Data Analytics (TDA) initiative. He holds a BSc in Statistics from the University of Science and Technology of China (2011) and a PhD in Statistics from the University of Michigan (2016). His research focuses on statistical network analysis, nonparametric inference, U-statistics, and conformal prediction. He advises several PhD students, including Meijia Shao (recipient of the 2023–2024 Whitney Research Award), Torey Hilbert, Kiljae Lee, Fangyi Wang, and Qian Zhou. Key research contributions include advancements in network-based statistical methods, robust distribution-free testing, and conformal prediction frameworks. His work bridges theoretical statistics with practical applications in neuroscience, genomics, and high-dimensional data analysis. Notable publications include work on U-statistic reduction (Journal of the American Statistical Association, 2025), stable conformal prediction (ICLR 2025), and neuroimaging connectivity analysis (Biometrics, 2025). Zhang collaborates extensively with researchers in biostatistics and computational science, contributing to projects in voxel-wise genome-wide association studies and brain connectivity analysis. His research is supported by affiliations with TDA and interdisciplinary collaborations at Ohio State.