جست‌وجوی استاد و پژوهشگر

Todd Kuffnerمشاهده پروفایل

دانشیار

• Statistical Inference
• Asymptotic Theory
• Bayesian Statistics
• +۷ مورد دیگر

Todd Kuffner is an Associate Professor in the Department of Mathematics and Statistics at Washington University in St. Louis , holding a Ph.D. in Mathematics from Imperial College London . His research bridges statistical theory , Bayesian asymptotics , and machine learning , focusing on rigorous inference after model selection. Education: Ph.D., Mathematics, Imperial College London M.Sc., Econometrics and Mathematical Economics, London School of Economics M.Sc., Economics, London School of Economics B.A., Economics, University of Michigan Kuffner's work investigates the validity, accuracy, and power of statistical procedures, with a focus on reconciling Bayesian, frequentist, and neo-Fisherian paradigms. His publications address higher-order asymptotics , post-selection inference , and resampling techniques for small-sample and high-dimensional settings. Key trends in his recent articles include: Bootstrap methods for high-dimensional and dependent data Bayesian approaches to volatility estimation in financial models Connections between machine learning and frequentist post-selection inference Formal Edgeworth and von Mises expansions for robust regression Empirical likelihood and model selection uncertainty Scientific contributions : Organizer of the Workshop on Higher-Order Asymptotics and Post-Selection Inference (WHOA-PSI) (2016-2020) Editorial roles at Harvard Data Science Review , Sankhya Series A , and Journal of the American Statistical Association Recipient of NSF grants for collaborative research His advising and organizational efforts emphasize collaborative research and innovative conference design , including interactive forums and workshops.

Christian LÉGERمشاهده پروفایل

استاد

• Statistical Methodology
• Bootstrap Methods
• Resampling Techniques
• +۴ مورد دیگر

Christian LÉGER is a Professor in the Department of Mathematics and Statistics at the University of Montreal , affiliated with the Faculty of Arts and Science . He holds a Ph.D. from Stanford University (1988). His research focuses on advancing statistical methodologies, particularly in resampling techniques like the bootstrap, adaptive estimation, and model selection, with applications in diverse fields such as biomedical research and industrial reliability. Research Interests: LÉGER’s work emphasizes leveraging computational power to enhance statistical methods. Key areas include bootstrap methodology for variance estimation, confidence interval construction, and parameter tuning (e.g., bandwidth selection in kernel density estimation). Recent projects explore inference in post-variable-selection regression models and the validity of bootstrap for estimators with non-standard convergence rates (e.g., least median of squares). Recognition: He received the Prix d'excellence en enseignement in 2000 from the Faculty of Arts and Sciences for outstanding teaching in the sciences. Advising & Collaboration: He has supervised multiple graduate students, including two industrial fellowships through the CRSNG program. His applied work includes consulting projects, with one leading to a master’s thesis. He collaborates on interdisciplinary topics such as age replacement policies in reliability engineering and statistical methods in medical imaging. Publications: Over 15 peer-reviewed articles since 1987 reflect his contributions to bootstrap theory, nonparametric methods, and statistical applications in fields ranging from biostatistics to operations research.

• Bayesian Statistics
• Statistical Modeling
• Operations Research
• +۷ مورد دیگر

David Valentín Conesa Guillén is a Full Professor in the Department of Statistics and Operations Research within the Faculty of Mathematics at the University of Valencia. He is an active researcher and a key member of the Valencia Bayesian Research Group (VABAR), focusing on advanced statistical methodologies and their applications. His primary research interests encompass Bayesian Statistics , Statistical Modeling , Operations Research , Epidemiological Modeling , Spatial Statistics , and Ecological Statistics . His work bridges theoretical development with practical applications in public health, environmental science, and financial systems. The trends in his recent publications reveal a strong emphasis on developing and applying Bayesian hierarchical models to complex real-world problems. Key areas include dynamic forecasting of influenza outbreaks, modeling spatial distributions of ecological and bioclimatic data, correcting biases in ecological survival estimates, and analyzing efficiency in sectors like banking and higher education. His methodology frequently involves advanced computational techniques like the Integrated Nested Laplace Approximation (INLA). PhD in Statistics, University of Valencia (2000) Thesis: "Inferencia y predicción en colas con ingresos o servicios en grupos" Supervised by Dr. Carmen Armero Cervera David Conesa has supervised academic work, as indicated by his role as a thesis advisor. His research has been supported by collaborative projects, leading to publications in top-tier journals such as Bayesian Analysis , European Journal of Operational Research , and Journal of Agricultural, Biological, and Environmental Statistics . He has extensive collaborations with researchers across Spain and internationally. His research is conducted within the VABAR (Valencia Bayesian Research Group) , a collaborative team dedicated to Bayesian methodology and its applications. This group provides a platform for interdisciplinary projects and the training of future statisticians.

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).

Melissa Binghamمشاهده پروفایل

استاد

• Statistics for Directional Data
• Statistics for Three-Dimensional Rotation Data
• Nonparametric Statistics
• +۳ مورد دیگر

Melissa Bingham is a Professor in the Department of Mathematics & Statistics at the University of Wisconsin-La Crosse (UWL). She serves as Program Director for the M.S. in Applied Statistics program, advising all Applied Statistics M.S. and dual-degree B.S./M.S. students. Her research focuses on statistical techniques for analyzing three-dimensional rotation data, with applications in crystallography and directional data analysis. Education: Ph.D. in Statistics, Iowa State University (2009) M.S. in Mathematics, University of North Dakota (2004) B.S. in Mathematics, University of North Dakota (2002) Research Interests: Dr. Bingham develops novel statistical methods for analyzing three-dimensional rotation data, including permutation tests for comparing rotational symmetry and spread, bootstrap techniques for measures of center, and Bayesian inference for non-symmetric distributions. Her work bridges theoretical statistics and applied problems in materials science and crystal orientation analysis. Professional Activities: Presented invited talks at international workshops, including the 2022 Advances in Directional Statistics (ADISTA) conference in Spain. Teaches a wide range of courses, including Elementary Statistics, Mathematical Statistics, and Statistical Methods. Labs/Teams: Collaborates with interdisciplinary teams on crystallography and materials science projects, leveraging statistical methodologies for three-dimensional data analysis.

Marius Krollمشاهده پروفایل

پژوهشگر

• Nonparametric Statistics
• U and V Statistics
• Bootstrap Methods
• +۳ مورد دیگر

Marius Kroll is a Researcher at Ruhr University Bochum's Faculty of Mathematics within the Institute of Statistics, working as part of Professor Holger Dette's research group in stochastics. His position is based in Building IB 2/89 at Universitätsstraße 150 in Bochum. His educational background includes: Doctoral Thesis in Mathematics, Ruhr-Universität Bochum (2023) Master Thesis in Mathematics, Ruhr-Universität Bochum (2019) Bachelor Thesis in Mathematics, Ruhr-Universität Bochum (2017) Kroll specializes in nonparametric statistics with expertise in U and V statistics , bootstrap methodology , and analysis of short-range dependent processes . His research develops theoretical foundations for measuring statistical dependence, particularly through distance covariance and Chatterjee's rank correlation. He investigates asymptotic properties of these measures under various dependence structures and develops practical inference procedures. His publication record shows a focused trajectory examining the theoretical properties of dependence measures, with recent work extending to infinite dimensional data and establishing connections between different forms of asymptotic behavior. Kroll has published in top statistics journals including Biometrika and the Journal of Theoretical Probability, with several additional papers currently in preparation. His major recognition includes: Ruth Massenberg award for the best mathematical dissertation 2023 Kroll actively participates in the academic community through invited talks at major conferences including the Bernoulli-IMS World Congress in Probability and Statistics and German Probability and Statistics Days. He serves on the Executive Board of the RUB Research School (since October 2023) and the Early Career Researchers Board of the Research Academy Ruhr (since June 2025), demonstrating leadership in supporting early career researchers at his institution.

Paulo Parenteمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Quantile Regression
• +۳ مورد دیگر

Paulo Parente is a Professor at the Instituto Superior de Economia e Gestão (ISEG), part of the Universidade de Lisboa. He holds a PhD in Economics from the University of Warwick (2007), a Master's in Economics from Nova School of Business and Economics (2002), and a Bachelor's in Mathematics Applied to Economics from the Higher Institute of Economics in Lisbon (1998). He currently serves as President of ISEG, overseeing its academic and administrative leadership. His research focuses on advanced econometric methodologies, including time series analysis, quantile regression, bootstrap techniques, and empirical likelihood methods. His work has been published in top journals such as Journal of Econometrics , Econometric Theory , and Journal of Time Series Analysis . Parente teaches courses such as Econometrics, Quantitative Methods, and Advanced Econometric Topics at both undergraduate and graduate levels. He has supervised numerous Master’s theses, advising students on topics ranging from labor market dynamics to energy market forecasting and insurance industry analysis. He is actively involved in ISEG's research initiatives, contributing to labs like the Data Lab, Futures Lab, and Policy Lab, which address contemporary economic challenges through interdisciplinary approaches.

• Statistics
• Bootstrap Methods
• Asymptotic Theory
• +۱ مورد دیگر

Aslak Mykland is a Professor in the field of Statistics at the University of Chicago. He completed his Ph.D. in Statistics at the University of California, Berkeley in 1989 with a dissertation titled "Bootstrap and Edgeworth Methods for Dependent Variables" under the advisement of Rudolph Beran. His research focuses on advanced statistical methods for dependent data structures, including resampling techniques and asymptotic expansions.

• Mathematics Education
• Statistics
• Applied Statistics
• +۴ مورد دیگر

Associate Professor RECEP BİNDAK is a faculty member at Gaziantep University's Faculty of Education, Department of Mathematics and Science Education. With a Doctorate in Mathematics from Dicle University (2004), he has built an extensive academic career spanning over two decades, including positions at Mardin Artuklu University and Gaziantep University's Technical Sciences Vocational School. His educational background includes a Doctorate (2000-2004) from Dicle University's Institute of Sciences in Mathematics, a Master's degree (1994-1997) from Yuzuncuyil University's Institute of Sciences in Mathematics, and a Bachelor's degree (1987-1991) in Mathematics Teaching from Dicle University's Faculty of Education. BINDAK's research focuses on mathematics education, statistics, and educational psychology, with particular emphasis on mathematical self-efficacy, problem-solving attitudes, measurement techniques, and quantitative research methods. His work bridges theoretical statistical concepts with practical educational applications, making significant contributions to both fields. He has published extensively on topics ranging from mathematical justification skills in middle school students to psychometric properties of Likert scales and teacher burnout predictors. His recent publications (2018-2024) reveal a strong trend toward interdisciplinary research combining mathematics education with psychological factors affecting student performance. The majority of his work centers on middle school mathematics education, examining variables like self-efficacy, problem-solving attitudes, and mathematical justification skills. He also maintains active research in statistical methodology, particularly in bootstrap methods and nonparametric techniques, demonstrating his dual expertise in both educational theory and quantitative analysis. TUBITAK Publication Incentive Award (2011) TUBITAK Publication Incentive Award (2012) Professor BINDAK has supervised 22 graduate theses (1 doctoral and 21 master's), reflecting his commitment to mentoring the next generation of researchers. His students have explored diverse topics within mathematics education, including mathematical connection self-efficacy, geometric justification skills, and the relationship between school climate and teacher burnout. His teaching portfolio spans doctoral, master's, undergraduate, and associate degree levels, with courses focused on statistics, probability, data analysis, and mathematical research methods. As evidenced by his extensive publication record and thesis supervision, Professor BINDAK maintains an active research laboratory focused on quantitative educational research, with particular attention to measurement theory, statistical methodology in education, and mathematics learning processes. His work continues to influence both educational practice and research methodology in Turkey's academic community.

Mårten Schultzbergمشاهده پروفایل

پژوهشگر

• Statistics
• Causal Inference
• Experimental Design
• +۳ مورد دیگر

Mårten Schultzberg is a Researcher at the Department of Statistics at Uppsala University , Sweden. His work spans causal inference, experimental design, and applied statistics, with a focus on methodological advancements and interdisciplinary applications in energy economics and behavioral science. Research Interests : Causal inference in observational and experimental studies Optimal experimental design and rerandomization strategies Statistical methods for energy demand analysis and behavioral modeling Recent Publications highlight his contributions to balancing covariates in randomized experiments, causal mediation analysis, and energy policy evaluation. His work appears in journals such as Journal of Causal Inference , Statistical Papers , and Energy Research & Social Science . Contact: marten.schultzberg@statistik.uu.se

• Statistical science
• Economic statistics
• Survey methodology
• +۴ مورد دیگر

Hiroshi Saigo is a Professor at Waseda University's School of Political Science and Economics, where he has been serving since 1999 after progressing through the ranks from Assistant Professor (pre-1992) to Associate Professor (1992-1994). His academic career spans over three decades with a consistent focus on statistical methodology and its applications in economic surveys. Dr. Saigo earned his Master's degree in Economics from Waseda University Graduate School of Economics in 1992, with a specialization in economic theory and economic history, following undergraduate studies in the Division of Economics at Waseda University's School of Political Science and Economics completed in 1984. His research interests center on statistical science and economic statistics, with particular expertise in survey methodology, sampling theory, and bootstrap methods. Dr. Saigo has made significant contributions to official statistics, developing innovative approaches for variance estimation, handling missing data, and improving survey design. His methodological work bridges theoretical statistical advancements with practical applications in official statistics production. His extensive publication record demonstrates expertise across multiple statistical domains, with particular emphasis on bootstrap methods for complex survey designs, variance estimation techniques, and optimal allocation strategies in stratified sampling. His work shows a consistent trajectory from theoretical developments in sampling theory to practical applications in Japanese official statistics. The Canadian Statistical Society The American Statistical Association The Japanese Economic Association The Japan Statistical Society Dr. Saigo has led and participated in numerous research projects funded by the Japan Society for the Promotion of Science, including current research (2023-2027) on systematizing culture and arts statistics in Japan. His recent projects focus on detection and handling of outliers in surveys, statistical models for official statistics, and cohort analysis for micro-data in statistical surveys. At Waseda University, Dr. Saigo teaches statistics courses at both undergraduate and graduate levels, including 'Statistics,' 'Economic Statistics,' and specialized seminars on statistical methodology, reflecting his expertise in applying rigorous statistical methods to economic and social data analysis.

Megan Heymanمشاهده پروفایل

دانشیار

• Nonparametric Statistics
• Time-Series Analysis
• Climate Change Modeling
• +۲ مورد دیگر

Megan Heyman is an Associate Professor in the Department of Mathematics at Rose-Hulman Institute of Technology, specializing in nonparametric statistical methods and time-series data analysis for climate change and environmental phenomena. Her educational qualifications include: PhD in Statistics from the University of Minnesota (2016) MS in Statistics from the University of Minnesota (2014) BS from Rose-Hulman Institute of Technology (2008) Dr. Heyman's research centers on bootstrapping methods for spatio-temporal data and nonparametric environmental modeling , with significant contributions to climate informatics. She develops innovative statistical frameworks for tree-ring analysis, crop yield prediction, and climate model validation, while integrating pedagogical approaches like rubber chicken demonstrations for teaching statistical inference. Her publication record reveals a consistent trajectory in applying bootstrap techniques to environmental datasets, evolving from foundational R package development (lmboot, WiSEBoot) toward interdisciplinary climate applications. Recent work emphasizes probabilistic evaluation of climate models and spatio-temporal relationships in ecological records, demonstrating strong collaboration with NASA's Jet Propulsion Laboratory. Dr. Heyman has not listed any scientific awards in her current profile. As co-director of the Independent Project/Research Opportunity Program, she mentors undergraduate researchers in statistics and environmental projects. Her NASA collaboration represents significant external research engagement, though specific grant details are not provided in the source material. She operates within environmental statistics research networks focused on climate informatics, collaborating with NASA JPL on data analysis initiatives while maintaining active statistical consulting practice.

Xiaofeng Shaoمشاهده پروفایل

استاد مدعو

• Statistics
• Data Science
• Time Series Analysis
• +۵ مورد دیگر

Xiaofeng Shao is an Adjunct Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. His research focuses on statistical methodology for time series, spatial data, and high-dimensional data, with applications in econometrics, environmental science, and neuroscience. Shao earned his PhD in Statistics from the University of Chicago in 2006. He has held grants as PI or co-PI from NSF-CMG, NSF-DMS, the Research Board at UIUC, and industry partners like ABInBev and Jump. His work has been recognized with awards such as the Tjalling C. Koopmans Econometric Theory Prize (2009) and the Centennial Scholar designation (2013–2016). His research spans long memory processes, functional data analysis, resampling methods, and applications in atmospheric science, economics, and neuroscience. Notable contributions include self-normalization techniques for high-dimensional time series and spatial bootstrap methods. He has advised numerous students and collaborates across disciplines, maintaining an active publication record in top journals like the Journal of the American Statistical Association and Biometrika.

Jacob Couttsمشاهده پروفایل

مدرس

• Mediation analysis
• Moderation analysis
• Resampling methods
• +۹ مورد دیگر

Jacob Coutts is a Lecturer in the Social Data Science Major and Department of Psychology at the University of Maryland. He holds a PhD and MS in Quantitative Psychology and MS in Applied Statistics from The Ohio State University. His research focuses on advancing statistical methods for mediation, moderation, and conditional process analysis, emphasizing accessibility through software development (e.g., R packages). He leads the Computational and Statistical Inference (CSI) Lab, which prioritizes mentorship, methodological innovation, and programming proficiency (R/Python). Education: PhD in Quantitative Psychology, The Ohio State University (2023) MS in Quantitative Psychology, The Ohio State University MS in Applied Statistics, The Ohio State University BS in Psychological Sciences, Northern Arizona University Research Interests: Mediation & moderation analysis Resampling methods Statistical power analysis Data visualization Dyadic data analysis Human sexuality He emphasizes open science practices and active learning in education, incorporating live coding and critical thinking exercises. Recent work includes presentations on quantitative pedagogy and DEI strategies in methodology courses, as well as collaborative projects on autism screening via machine learning and neurodegenerative disease research. His team’s 2024 projects explored bootstrap applications, workplace wellness, and ADHD diagnosis methods (winning an Outstanding Research Award). Teaching: He instructs courses ranging from introductory psychology to advanced topics like mediation analysis and survey data science, prioritizing programming skills (R/Python) and practical statistical literacy.

• Operations Research
• Optimization Algorithms
• Logistics
• +۶ مورد دیگر

Dr. Mahdi Shavarani is a Lecturer in Business Analytics at the Kent Business School, University of Kent. He holds two Ph.D.s: one in Industrial Engineering from Eastern Mediterranean University (2018), and another in Multi-Objective Optimization and Multi-Criteria Decision-Making from the University of Manchester. His research focuses on optimizing real-world business challenges through advanced algorithms, with specialties in logistics, supply chain management, transportation, and manufacturing systems. Education: - Ph.D. in Industrial Engineering, Eastern Mediterranean University (2018) - Ph.D. in Multi-Objective Optimization and Multi-Criteria Decision-Making, University of Manchester Research Interests: Dr. Shavarani specializes in developing sustainable optimization models using machine learning and interactive evolutionary algorithms. His work emphasizes reducing decision-makers' cognitive load while improving computational efficiency. Key areas include multi-criteria decision-making, dimension reduction in high-dimensional objective spaces, and simulating decision-making behaviors. He is affiliated with the Decision and Cognitive Science Research Center at Alliance Manchester Business School. Publications: His recent work explores topics like energy-efficient robotic systems, dynamic preference adaptation in algorithms, and congested facility location problems. Over 30 peer-reviewed publications demonstrate his contributions to optimization theory and practical applications. Grants and Advising: No specific grants or advising roles are detailed in the provided text. Affiliations: - Member, International Society on Multiple Criteria Decision Making - Affiliated Researcher, Decision and Cognitive Science Research Center