جست‌وجوی استاد و پژوهشگر

• Monte Carlo Methods
• Statistical Learning
• Semiparametric Statistics
• +۴ مورد دیگر

François Portier is an Associate Professor at CREST-ENSAI (École Nationale de la Statistique et de l'Analyse de l'Information) and Head of the Master for Smart Data Science program. He serves as Associate Editor for both the Electronic Journal of Statistics and Computational Statistics & Data Analysis . His research spans Monte Carlo Methods , Statistical Learning , and Semiparametric/Nonparametric Statistics , with focus areas including stochastic optimization, probability measure approximation, and local-averaging estimators. Recent work demonstrates strong computational statistics emphasis in adaptive sampling techniques and model validation frameworks. His publication trends reveal concentrated contributions to Stochastic optimization algorithms (2025) Dimension reduction in classification (2025) Covariate shift adaptation (2025) Density model diagnostics (2025) showcasing methodological innovation in high-dimensional statistical inference. Portier maintains active academic service through editorial roles and leads ENSAI's Smart Data Science program, bridging theoretical statistics with data science applications.

Georgios Iliopoulosمشاهده پروفایل

استاد

• Markov chain Monte Carlo methods
• Statistical Decision Theory
• Scale parameter estimation
• +۲ مورد دیگر

Professor Georgios Iliopoulos is a distinguished academic serving as Professor and Department Chair of the Department of Statistics and Actuarial Science at the University of Piraeus. With an extensive academic career spanning over two decades, Professor Iliopoulos has established himself as a leading expert in statistical theory and methodology. His leadership as Department Chair demonstrates his significant contribution to the academic community and his commitment to advancing statistical education and research. Professor Iliopoulos completed his educational journey at the University of Patras, earning his B.A. in Mathematics in 1993 followed by a PhD in Statistics in 1999. His academic career progressed through various institutions before he settled at the University of Piraeus, where he has held positions from Assistant Professor (2003-2010) to Associate Professor (2010-2015) and ultimately to Professor (2015-present). Professor Iliopoulos's research focuses on several key areas of statistical theory, with particular emphasis on Markov chain Monte Carlo methods, Statistical Decision Theory, Scale parameter estimation, Accurate inference under censorship, and Constrained inference arrangement. His work bridges theoretical statistics with practical applications, demonstrating how sophisticated statistical methods can solve real-world problems across various domains. His research has consistently addressed challenging theoretical questions while maintaining relevance to practical statistical challenges faced by researchers and practitioners. Analysis of Professor Iliopoulos's publication record reveals a strong focus on theoretical statistics with applications in reliability analysis, Bayesian inference, and censoring methodologies. His work demonstrates a consistent pattern of advancing statistical theory while maintaining practical relevance, particularly in the areas of parameter estimation, confidence interval construction, and inference with censored data. The interdisciplinary nature of his research is evident in publications spanning journals in statistics, biostatistics, and computational statistics. Professor Iliopoulos is actively involved in teaching both undergraduate and postgraduate courses. At the undergraduate level, he teaches Linear Algebra, Statistics II: Hypothesis Testing, and Special Topics in Statistics (Bayesian Statistics). For postgraduate students, he offers Computational Statistical Techniques as part of the Master of Science in Applied Statistics program. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications of advanced statistical methods.

Wanzhu Tuمشاهده پروفایل

استاد

• Nonparametric Statistics
• Semiparametric Models
• Biostatistics
• +۵ مورد دیگر

Dr. Wanzhu Tu is a faculty member at Indiana University with multiple roles: Professor of Biostatistics & Health Data Science Executive Vice Chair of the Department of Biostatistics & Health Data Sciences Adjunct Professor in the School of Public Health Scientist at the Indiana University Center for Aging Research Dr. Tu is an applied statistician specializing in nonparametric and semiparametric models. His research focuses on modeling biological processes in diseases like hypertension, and he develops methods for longitudinal data analysis and survival analysis. Applications include aging, chronic kidney disease, and public health. His recent publications highlight methodological innovations in clustering, causal inference, and semiparametric modeling. Applied work addresses hypertension, chronic kidney disease, aging, and public health using large health datasets, including electronic health records and longitudinal studies. Dr. Tu's advising of students and grant funding activities are not detailed in the provided information. He is affiliated with the Indiana University Center for Aging Research, contributing to interdisciplinary aging research.

Benjamin Kedemمشاهده پروفایل

استاد

• Time Series Analysis
• Spatial Statistics
• Spectral Analysis
• +۵ مورد دیگر

Benjamin Kedem is a Professor in the Department of Mathematics at the University of Maryland, College Park, with affiliations at the Institute for Systems Research (ISR). His academic career spans several decades with significant contributions to time series analysis, spatial statistics, and statistical methodology. Dr. Kedem's research focuses on time series analysis, space-time statistical problems, and combination of information from multiple sources. His work includes significant contributions to higher order crossings (HOC), contraction mapping methods in spectral analysis, Rice formula applications, threshold methods for rainfall estimation, partial likelihood approaches, Bayesian-transformed-Gaussian spatial prediction, and statistical data fusion. His research has practical applications in environmental statistics, meteorology, and public health. His recent publications demonstrate a continued focus on semiparametric methods, statistical data fusion, and computational approaches to time series and spatial analysis. The 15 most recent articles span from 2017 back to 1994, showing both contemporary relevance and foundational contributions to the field of statistics. Dr. Kedem has directed 15 PhD dissertations, with students completing between 1983 and 2007, indicating his long-standing commitment to mentoring the next generation of statisticians. His former students include George Reed, Donald E.K. Martin, Silvia R.C. Lopes, Haralabos Pavlopoulos, James Troendle, Ta-Hsin Li, John Barnett, Konstantinos Fokianos, Victor De Oliveira, Neal Jeffries, Boris Kozintsev, Richard Gagnon, Haiming Guo, Guanhua Lu, and Shihua Wen. He has authored or co-authored several books including 'Regression Models for Time Series Analysis' (2002), 'Time Series Analysis by Higher Order Crossings' (1994), and 'Statistical Data Fusion' (2017). His teaching portfolio includes graduate courses such as STAT 730 (Time Series Analysis), STAT 740 (Linear Models I), and STAT 741 (Linear Models II).

• Statistics
• Biostatistics
• Survival Analysis
• +۳ مورد دیگر

Takumi Saegusa is an Associate Professor in the Department of Mathematics at the University of Maryland, College Park, specializing in Statistics and Biostatistics. His work bridges theoretical and applied statistical methods. Rank: Associate Professor Department: Department of Mathematics University: University of Maryland, College Park His research focuses on advanced statistical techniques for survival analysis, two-phase sampling, and data integration. He develops methods to handle longitudinal covariates, heterogeneous populations, and merged datasets from multiple sources. Recent publications address confidence bands for survival curves, variable selection in threshold regression, and computational approaches for statistical packages like osDesign and tpsDesign . These works span biostatistical applications, computational methods, and theoretical foundations. He contributes to statistical software development, including R packages for two-phase study design and analysis. His work has applications in epidemiology, clinical research, and precision medicine.

Eric V. Sludمشاهده پروفایل

استاد

• Mathematical Statistics
• Probability
• Census Statistics
• +۱۱ مورد دیگر

Eric V. Slud is a Professor in the Statistics Program within the Department of Mathematics at the University of Maryland, College Park. He has taught numerous graduate-level statistics courses including Mathematical Statistics, Categorical Data Analysis, and High-Dimensional Statistics since at least 2017. Dr. Slud's research spans several key areas of mathematical statistics and probability. His work focuses on: Census statistics, particularly demographic modeling of nonresponse to national surveys Small Area Estimation (SAE) with applications to the SAIPE program Survival data analysis including semiparametric inference and clinical trial design Meta-analysis in biostatistics Pharmaceutical statistical methods Large-scale data problems with emphasis on cross-classified data His publications demonstrate continued contributions across these domains, with particular emphasis on small area estimation methodologies, survival analysis techniques, and applications in survey statistics. Dr. Slud has developed innovative approaches to weighting adjustment, nonresponse correction, and estimation with complex survey data. Dr. Slud has advised several PhD students including Yang Cheng (2004), Sophie Tsou (2005), and others working on topics ranging from Factor Analysis to Survival Data. He has collaborated extensively with researchers at the Census Bureau and FDA. Dr. Slud maintains active research programs through multiple Research Interaction Teams (RITs) at the University of Maryland, focusing on advanced statistical methodologies and their applications to real-world problems in census methodology, healthcare, and other domains requiring sophisticated statistical analysis.

Yanqin Fanمشاهده پروفایل

استاد

• Nonparametric and Semiparametric Econometrics
• Partial Identification
• Program Evaluation
• +۴ مورد دیگر

Yanqin Fan is a Professor in the Department of Economics at the University of Washington. Her research spans advanced econometric methodologies and their applications in quantitative analysis. Research Interests: Nonparametric and Semiparametric Econometrics, Partial Identification, Program Evaluation, Copula, Quantile Estimation and Inference, Auction, Q-Learning. Contact: fany88@uw.edu

• Biostatistics
• Bioinformatics
• High-dimensional data analysis
• +۷ مورد دیگر

Tao Wang is an Assistant Professor Adjunct in the Department of Biostatistics at Yale School of Public Health and a tenure-track Associate Professor and Principal Investigator in the SJTU-Yale Joint Center for Biostatistics and Data Science at Shanghai Jiao Tong University. His work focuses on statistical methods for high-dimensional biomedical data, especially dimension reduction, microbiome analysis, and integrative genomic modeling. Education: PhD in Statistics, Hong Kong Baptist University, 2013 MS, East China Normal University, 2010 Research Interests: Dr. Wang develops statistical and computational techniques to tackle complex, high-dimensional data arising from modern biotechnologies. His methodological portfolio includes adaptive independence tests for microbiome community data, penalized prediction models for sequencing counts, and counting-process-based dimension reduction for censored survival outcomes. He also works on Bayesian tree-structured models linking dietary nutrients to gut microorganisms and joint estimation of Gaussian graphical models over spatial and temporal domains. These interests sit at the intersection of biostatistics, bioinformatics, machine learning, and epidemiology , with applications spanning the human microbiome, epigenomics, and large-scale cohort studies. Publications at a Glance: Across six major papers (2015-2019) in Biometrics and Biometrika , Wang has consistently advanced methodology for high-dimensional count, compositional, and censored data. Themes include adaptive testing, predictive modeling, dimension reduction, and network estimation, often motivated by collaborative projects in genomics and microbiome science. Honours: Elected Member, International Statistical Institute (2020) Collaboration & Advising: He maintains an active collaboration with Yale’s Dr. Hongyu Zhao, co-authoring all showcased publications. No formal advisees are listed in the provided material. Laboratories & Teams: He leads research activities within the SJTU-Yale Joint Center for Biostatistics and Data Science, integrating resources from both Shanghai Jiao Tong University and Yale to advance data-intensive biomedical science.

Jason Abrevayaمشاهده پروفایل

استاد

• Econometrics
• Applied Microeconomics
• Demography
• +۳ مورد دیگر

Jason Abrevaya serves as Associate Dean for Graduate Education and holds the Murray S. Johnson Chair in Economics at the University of Texas at Austin within the College of Liberal Arts and Department of Economics. His research spans econometric methodology, applied microeconomics, and demography, with significant contributions using U.S. birth databases to study birthweight inequality, smoking effects, and gender selection practices among ethnic groups. Dr. Abrevaya's work centers on econometrics (panel data analysis, treatment effects, missing data), health economics (birth outcomes, vaccination), labor economics, and sports economics. His methodological innovations address data challenges in causal inference and microeconomic applications, characterized by rigorous statistical approaches to real-world problems. Recent publications demonstrate continued emphasis on theoretical econometrics with empirical applications in public health and sports analytics. His article trends reveal sustained focus on treatment effects under heteroskedasticity, partial effects in nonlinear models, and applications to vaccine uptake and sports (e.g., penalty calls in hockey). Methodological contributions frequently bridge theoretical econometrics with empirical practice in health and social sciences. Dr. Abrevaya has secured research funding from the National Science Foundation and Robert Wood Johnson Foundation. He is author of the textbook Probability and Statistics for Economics and Business: An Introduction Using R . No scientific awards are explicitly mentioned in available sources. As Associate Dean for Graduate Education, he oversees graduate programs while maintaining active research and mentorship. His warning about scam impersonation attempts (via Gmail addresses posing as research opportunities) underscores the importance of verifying communications through his official university email.

Mehrdad Naderiمشاهده پروفایل

مدرس

• Classification
• Cluster Analysis
• Finite Mixture Models
• +۵ مورد دیگر

Mehrdad Naderi is a Lecturer in Statistics at the Department of Mathematics, Physics, and Electrical Engineering , Northumbria University . His academic journey includes a PhD in Mathematical Statistics from Shahid Bahonar University of Kerman (2017) and postdoctoral research at National Chung Hsing University (Taiwan), Ferdowsi University of Mashhad (Iran), and University of Pretoria (South Africa). Education: PhD in Mathematical Statistics, Shahid Bahonar University of Kerman (2017) His research focuses on applied statistical inference with emphasis on classification , cluster analysis , factor analysis , finite mixture models , and EM algorithm for robust estimation. He has contributed to multivariate and matrix-variate analysis, particularly in handling outliers and asymmetrical data structures. Recent work includes three-way data clustering using matrix-variate normal distributions and robust Bayesian inference for censored mixture models. His publications demonstrate expertise in distribution theory, statistical computation, and applications to financial data, environmental modeling, and astrophysics. Current collaborations span multiple institutions, focusing on heavy-tailed distributions and computational methods for complex data structures.

Qiankun Zhouمشاهده پروفایل

استاد

• Theoretical Econometrics
• Applied Econometrics
• Panel Data Econometrics
• +۵ مورد دیگر

Qiankun Zhou is a Professor of Economics at Louisiana State University, holding the prestigious Freeport-McMoran Endowed Professorship in Economics #2. Based in the Business Education Complex South (2321) in Baton Rouge, LA, he can be contacted at qzhou@lsu.edu or 225-578-5211. Professor Zhou earned his PhD in Economics from the University of Southern California in 2015. His academic journey reflects rapid advancement to full professorship, underscored by his endowed chair position. His research program bridges theoretical and applied econometrics with exceptional focus on panel data methodologies. Key contributions include breakthroughs in treatment effects estimation with interactive fixed effects, high-dimensional inference through orthogonal projection, and specification testing for time-varying coefficient models. His work consistently addresses the tension between methodological rigor and practical applicability in economic analysis. Recent publications (2023-2025) reveal intensified exploration of causal inference frameworks and high-dimensional data challenges, with multiple papers in Journal of Econometrics and Econometric Theory . This output demonstrates sustained theoretical innovation while maintaining strong empirical relevance. His primary distinction is the Freeport-McMoran Endowed Professorship in Economics #2, recognizing exceptional research contributions at LSU. Professor Zhou maintains active research collaborations across institutions including USC, Florida State University, and international partners. His advising likely emphasizes mathematical rigor coupled with real-world application, preparing students for research careers through co-authorship opportunities on methodological advances. The absence of a named lab reflects economics' typical research structure where theoretical work occurs through individual and small-group collaboration.

Chao Chengمشاهده پروفایل

استادیار

• Causal mediation analysis
• Quantile causal inference
• Measurement error correction
• +۳ مورد دیگر

Chao Cheng serves as an Assistant Professor of Statistics & Data Science at Washington University in St. Louis within the Department of Statistics and Data Science. His academic profile combines methodological innovation with health science applications. Dr. Cheng's educational foundation includes: PhD in Biostatistics from Yale University MS in Applied Statistics from Tsinghua University BS in Economics from Donghua University His research program centers on advancing causal inference methodologies, specifically through causal mediation analysis, quantile-based causal estimation, and measurement error correction frameworks. These semiparametric approaches target enhanced robustness in effect estimation for complex health science problems. His work demonstrates consistent focus on developing statistically rigorous methods with direct practical utility in biomedical research. Dr. Cheng maintains active research engagement as evidenced by his inclusion in the department's Fall 2025 newsletter and June 2025 new faculty announcement. While specific grant details aren't provided, his methodological focus suggests involvement in NIH or NSF-funded biostatistics initiatives. His teaching responsibilities align with the department's graduate and undergraduate statistics curriculum. Based in Jolley Hall, Room 412, he is accessible through his professional email chaoc@wustl.edu and represents a growing research presence in the statistics community.

• nonparametric statistics
• Statistics of random processes
• numerical methods of stochastics
• +۵ مورد دیگر

Denis Vitalievich Belomestny is a Leading Researcher at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE) in Moscow. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis. Belomestny joined HSE in 2014 and has accumulated 11 years of scientific and teaching experience. His academic background includes a Candidate of Physical and Mathematical Sciences degree from Lomonosov Moscow State University (2002) and a specialty in Applied Mathematics from the same university (1998). Belomestny's research focuses on nonparametric statistics , statistics of random processes , numerical methods of stochastics , and financial mathematics . His work demonstrates a strong interdisciplinary approach bridging theoretical mathematics with practical applications in machine learning and artificial intelligence. He has made significant contributions to the analysis of stochastic differential equations, particularly McKean-Vlasov type models, and has developed innovative methods combining deep learning with traditional statistical techniques. His recent publication record shows a clear trend toward integrating deep neural networks with classical stochastic analysis. The 15 most recent articles reveal a growing emphasis on applying machine learning techniques to solve complex problems in statistical inference, particularly in areas like variance reduction, optimal stopping, and density estimation. His research spans both theoretical foundations and practical applications, with increasing focus on the intersection of probability theory and modern AI methodologies. Academic Work Allowance (2020–2021, 2019–2020) Bonus for an article in a foreign peer-reviewed scientific publication (2016–2018) Best Teacher of 2019 Belomestny has been actively involved in advising students and junior researchers, with numerous co-authored publications indicating his mentorship role. He is a key member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, which received a significant grant from the Russian Science Foundation in 2018 (project No. 18-11-00132) led by Alexey Naumov, with Belomestny as a principal investigator. The laboratory focuses on high-dimensional data analysis and modern stochastic algorithms, positioning Belomestny at the forefront of research in these rapidly developing fields.

Yichi Zhangمشاهده پروفایل

استادیار

• Causal Mechanism and Causal Learning
• High-dimensional Models
• Large-scale Computations
• +۲ مورد دیگر

Yichi Zhang is an Assistant Professor in the Department of Statistics at Indiana University Bloomington. Previously, he was a Postdoctoral Scholar at Duke University with dual appointments in the Fuqua School of Business and Department of Biostatistics & Bioinformatics. His academic credentials include a Ph.D. in Statistics from North Carolina State University (2023) and a B.S. in Mathematics and Applied Mathematics from Sichuan University (2018). Professor Zhang's research centers on methodological innovation in statistical learning, with primary focus areas: Causal Mechanism and Causal Learning High-dimensional Models Large-scale Computations Randomized Algorithms Uncertainty Quantification His publication trajectory (2023-2025) reveals rapid advancement in theoretical statistics and machine learning, with increasing contributions to causal inference frameworks and computational methodologies. Recent work demonstrates particular strength in adapting statistical theory to AI challenges, including large language model evaluation and network analysis. His scholarly recognition includes: ASA Nonparametric Statistics Student Paper Award Finalist (2023) IMS Hannan Graduate Student Travel Award (2021) ENAR Distinguished Student Paper Award (2021) Mu Sigma Rho National Statistics Honor Society (2018) As an early-career faculty member, Professor Zhang is establishing his research program while teaching core statistics courses. His active publication pipeline suggests robust research momentum, though specific grant details aren't provided. Prospective students interested in theoretical-computational statistics should contact him directly regarding advising opportunities and current projects.

• Statistics
• Nonparametric Statistics
• Semiparametric Statistics
• +۳ مورد دیگر

Professor Natalie Neumeyer is a faculty member in the Department of Mathematics at the University of Hamburg, specifically within the Division of Mathematical Statistics and Stochastic Processes. Her office is located at Bundesstraße 55, 20146 Hamburg. Her research focuses on advanced statistical methodologies with emphasis on: Nonparametric and semiparametric techniques under structural constraints Monotonicity and concavity assumptions in regression and density estimation Adaptive inference procedures with theoretical efficiency guarantees She actively leads multiple German Research Foundation (DFG) projects including the Research Unit 'Structural Inference in Statistics: Adaptation and Efficiency', demonstrating significant research leadership. Her current projects investigate shape-constrained estimation methods and semiparametric structural analysis, contributing to foundational advances in statistical theory. Professor Neumeyer's work shows consistent funding support through DFG grants, indicating strong peer recognition of her methodological contributions to statistical science.