جست‌وجوی استاد و پژوهشگر

• Statistics
• Applied Mathematics
• Data Science
• +۵ مورد دیگر

Rohan Dalpatadu is a Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas (UNLV). His research expertise spans statistical methodology, applied mathematics, and interdisciplinary applications in healthcare, environmental science, and gaming. He holds a terminal academic rank and has contributed to both theoretical and applied research areas. Research Interests include: Statistical modeling and machine learning applications in biomedicine Numerical methods for optimal control and environmental systems Probability distributions and their applications in gambling and actuarial science Data analysis techniques for imbalanced datasets and customer satisfaction studies Recent work focuses on cancer classification using gene expression data, contaminant transport modeling, and innovative statistical approaches for medical diagnostics. His publications demonstrate a strong interdisciplinary orientation across mathematics, engineering, and health sciences. Advising and Grants: No specific advising or grant information is explicitly listed in the provided text. His research has been applied to diverse sectors including airport operations, agricultural economics, and ergonomic design. Labs/Teams: No dedicated lab or collaborative team affiliations are mentioned in the current data.

Bo Honoréمشاهده پروفایل

استاد

• Econometrics
• Econometric Theory
• Panel Data Analysis
• +۳ مورد دیگر

Bo Honoré is the Class of 1913 Professor of Political Economy and Professor of Economics at Princeton University's Department of Economics. He has held leadership roles such as Director of Graduate Studies, Director of the Gregory C. Chow Econometric Research Program, and Department Chair. Previously, he taught at Northwestern University and held visiting positions at the University of Chicago and University of Copenhagen. He earned his Ph.D. from the University of Chicago. His research focuses on econometric theory, panel data analysis, duration models, and censored regression. Notable contributions include work on interdependent durations, binary outcome models, and identification strategies. He has been recognized with awards like the Richard E. Quandt Teaching Prize and the Rigmor and Carl Holst-Knudsen Award. His articles explore topics such as retirement planning dynamics, pandemic labor market impacts, and econometric methodology. He has served on editorial boards and contributed to policy-related research. His work bridges theoretical econometrics with applied issues in labor and health economics.

Han Hongمشاهده پروفایل

استاد

• Econometrics
• Health Econometrics
• Statistical Modeling
• +۲ مورد دیگر

Han Hong is a Professor of Economics at Stanford University's Department of Economics. He holds a Ph.D. in Economics (1998), M.S. in Computer Science (1998), and M.S. in Statistics (1997) from Stanford University, and a B.A. in International Trade from Zhongshan University (1993). His research focuses on econometric methodology, health econometrics, statistical modeling, and computational economics. He has developed innovative approaches for panel data analysis, structural estimation, and decision-making algorithms. Professor Hong has received numerous honors including the Willard G. Manning Memorial Award (2017), Arrow Award Honorable Mention (2015), and multiple NSF grants. He serves as Co-Editor of the Journal of Econometrics and is a Fellow of the Econometric Society. He teaches courses in Advanced Econometrics, Data Science, and Econometric Methods, and mentors students through honors thesis research and directed reading programs.

• Computational Genetics
• Genomic Data Analysis
• Statistical Genetics
• +۳ مورد دیگر

Kenneth Lange is a Professor at the University of California, Los Angeles (UCLA) in the departments of Computational Medicine and Human Genetics . He holds the Maxine and Eugene Rosenfeld Endowed Chair in Computational Genetics and focuses on genomic data analysis , statistical genetics , and optimization algorithms for biomedical applications. His research interests span Computational Genetics Biomedical Big Data Statistical Methods for Gene Mapping Optimization Algorithms Machine Learning . He has developed advanced methods for genetic admixture estimation, genotype imputation, and cancer stem cell therapy modeling. Dr. Lange's publications (2024-2013) emphasize statistical genetics , computational biology , and optimization techniques . Key trends include haplotype analysis , neuroimage registration , ancestry-informative markers , and penalized regression methods . Scientific awards include the Maxine and Eugene Rosenfeld Endowed Chair in Computational Genetics . He has advised graduate students such as Seyoon Ko , Benjamin Chu , and Jeanette Papp , with significant contributions to genomic analysis and biomedical informatics . Dr. Lange leads NIH-funded projects like R35GM141798 (Modeling, Inference, and Optimization for Genomic and Biomedical Big Data, 2021-2026) and co-led T32HG002536 (Genomic Analysis Training Grant, 2002-2022). He has also participated in grants for statistical methods (R01GM053275, 1995-2021) and integrative biology (T32GM008185, 1987-2023).

Kengo Katoمشاهده پروفایل

استاد

• Mathematical Statistics
• Applied Probability
• Econometrics
• +۳ مورد دیگر

Kengo Kato is a Professor in the Department of Statistics and Data Science at Cornell University , affiliated with the College of Arts and Sciences. Previously, he held a position at the Faculty of Economics at The University of Tokyo and served as a visiting scholar at MIT’s Department of Economics. His research focuses on Mathematical Statistics, Applied Probability, and Econometrics , with an emphasis on high-dimensional statistical models and optimal transport theory. His work spans topics such as statistical inference in optimal transport, Gromov-Wasserstein distances, bootstrap methods, and high-dimensional data analysis. Notably, he is Editor-in-Chief of the journal Bernoulli (2025-2027) and serves as an associate editor for Japanese Economic Review and Journal of Statistical Planning and Inference . His research bridges theoretical advancements with practical applications in econometrics and machine learning. Recent publications emphasize foundational results in optimal transport theory, including limit laws for Gromov-Wasserstein alignment, stability of entropic maps, and statistical guarantees for sliced Wasserstein distances. His work on bootstrap techniques addresses challenges in high-dimensional and spatial data analysis, offering robust inference methods for modern datasets. While no formal advisees are listed, his contributions to statistical theory and methodology suggest significant mentorship in graduate training programs. His research is supported by Cornell’s interdisciplinary environment and collaborations within affiliated institutes.

Larry Wassermanمشاهده پروفایل

استاد

• nonparametric inference
• causal inference
• machine learning
• +۶ مورد دیگر

Larry Wasserman is a UPMC University Professor at Carnegie Mellon University, jointly appointed in the Department of Statistics and Data Science and the Machine Learning Department. He received his Ph.D. from the University of Toronto in 1988 and is recognized as one of the leading statisticians of his generation. His research spans theoretical and applied statistics, with core interests in: Foundational inference : Nonparametric methods, asymptotic theory, causal frameworks Modern applications : Machine learning, high-dimensional statistics, astrostatistics Interdisciplinary domains : Bioinformatics, genomics, physical sciences via the STAMPS group His recent publications demonstrate strong emphasis on causal methodology, optimal transport, and robust inference, with applications ranging from particle physics to genomic analysis. Articles frequently develop novel nonparametric techniques with minimax optimality guarantees. Award highlights include: COPSS Presidents' Award (1999) - Top honor for statisticians under 40 CRM-SSC Prize (2002) - Landmark contributions to statistics Fellowships: American Statistical Association, Institute of Mathematical Statistics, AAAS He leads the Statistical Machine Learning Theory Group and founded STAMPS (Statistical Methods for Physical Sciences). His textbooks All of Statistics and All of Nonparametric Statistics are widely used in graduate programs globally.

• Spatial Statistics
• Quantile Regression
• Healthcare Data Analysis
• +۵ مورد دیگر

Professor Ji Meng Loh is a faculty member in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on spatial statistics, quantile regression, healthcare data analysis, and software testing methodologies. He has led multiple federally funded projects including 'Scalable Inference of Quantile Regression for Large-Scale Health Care Data' (2019-2022) and 'Spatial Inference with Application to Astronomy' (2005-2010). His work bridges theoretical statistics with applications in astronomy, biomedicine, and software engineering. Research Interests: Spatial point processes and bootstrap methods Statistical computing and machine learning Healthcare policy and data analysis Formal software testing and mutation analysis Astronomy data modeling Recent Research Trends: His 2020-2025 publications emphasize methodological advancements in: Mortality modeling with neural networks Biostatistical analysis of EEG data Formal verification of software loops Mutation testing metrics He has also contributed to healthcare decision-making frameworks and public health policy research. Grants & Collaboration: Co-PI on NSF-funded projects investigating local public goods allocation (2006-2012) and aerospace engineering cost modeling (2005-2013). Active collaborations span NJIT's departments of Computer Science, Public Health, and Biomedical Engineering. Professional Contributions: Developed novel statistical methods for analyzing spatial data in astronomy and public health contexts. His work on 'equivalent mutants' in software testing has influenced industry standards. Currently oversees statistical consulting initiatives for NJIT researchers.

Thomas Georgeمشاهده پروفایل

استاد

• Market Microstructure
• Asset Pricing
• Behavioral Finance
• +۳ مورد دیگر

Thomas George is the Senior Associate Dean for Faculty Affairs and Bauer Professor of Finance at the University of Houston's Bauer College of Business. He previously held positions at Ohio State University, the University of Iowa, and Northwestern University. His research focuses on market microstructure, asset pricing, and behavioral finance, with publications in top journals like the Journal of Finance and Journal of Financial Economics. He has won the Best Paper Prize from the Journal of Financial Intermediation and been nominated for the Smith-Breeden Prize. George leads faculty recruitment, retention, and promotion processes at Bauer College, directs the AIM Center for Investment Management, and oversees the Cougar Investment Fund. He co-developed the Graduate Certificate in Financial Services Management and teaches in Bauer's Executive MBA program. His consulting work includes designing educational programs on project valuation and performance evaluation. He served as Interim Dean during the 2018-19 academic year. Research interests include equity and fixed-income investment management, pricing errors, and liquidity dynamics. His work bridges theoretical finance and practical market mechanisms, addressing puzzles like momentum investing and post-earnings drift. Recent studies explore remote work productivity post-COVID-19 and China's stock market dynamics. Awards: Best Paper Prize (Winner/Runner-Up), Smith-Breeden Nominee Labs/Initiatives: AIM Center for Investment Management, Cougar Investment Fund Consulting: Corporate educational program design for valuation and risk management

• Statistics
• Data Science
• Machine Learning
• +۳ مورد دیگر

Qian Zhao is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. Their research focuses on statistical methodology, high-dimensional data analysis, and applications in computational biology. They are affiliated with the Lederle Graduate Research Tower (LGRT 1444) and actively contributes to interdisciplinary programs in data science and biostatistics. Dr. Zhao has been recognized for award-winning teaching and mentoring, including initiatives like near-peer mentoring in data science. Their work bridges theoretical statistics with practical applications in genomics and biomedical research. Education & Background: Details about educational background are not explicitly provided in the text, but their academic focus aligns with advanced statistical training typical for such roles. Research Interests: Dr. Zhao's research emphasizes developing statistical methods for high-dimensional data, with applications in genomics and computational biology. Their work often involves bootstrap methods, asymptotic theory, and interactive visualization tools for biological data analysis. Recent projects include exploratory gene ontology analysis and alignment techniques for cytometry data. Grants & Advising: Specific grants or advising records are not listed in the provided text. However, their involvement in mentoring programs and interdisciplinary initiatives suggests active engagement in student supervision and collaborative research projects. Labs/Teams: No specific lab or team affiliations are mentioned, but their research likely intersects with computational and biological science groups at UMass Amherst.

Marinho Bertanhaمشاهده پروفایل

دانشیار

• Econometrics
• Causal Inference
• Applied Microeconomics
• +۵ مورد دیگر

Marinho Bertanha is a tenured Associate Professor of Economics at the University of Notre Dame, serving as concurrent faculty in Statistics and a faculty fellow at the Kellogg Institute. His academic work bridges econometrics and applied microeconomics with significant contributions to causal inference methodologies. His educational background includes a Ph.D. in Economics from Stanford University (2015), an M.A. from Fundação Getúlio Vargas in Rio de Janeiro (2009), and a B.A. from Universidade de São Paulo (2006). Prior to his current position, he completed postdoctoral work at the University of Louvain and served as a visiting professor at the University of Chicago. Bertanha's research focuses on causal inference , resampling methods , and policy evaluation , with particular expertise in regression discontinuity designs, bunching estimation, and permutation testing. His methodological innovations address critical challenges in econometric identification and statistical inference, especially in contexts with strategic reporting and imperfect compliance. His publication record shows consistent high-impact output in top journals including the Journal of the American Statistical Association , Journal of Econometrics , and Review of Economics and Statistics . Recent work demonstrates increasing focus on practical implementation through software development (Stata and MATLAB packages) that has become standard in empirical economics research. Through his concurrent appointment in Statistics and faculty fellowship at the Kellogg Institute, Bertanha contributes to interdisciplinary research initiatives focusing on economic policy evaluation and statistical methodology development. His current work-in-progress examines causal effects in matching mechanisms, returns to education with strategic reporting, and tax elasticity estimation across multiple countries.

Denis Chetverikovمشاهده پروفایل

استاد

• Econometrics
• High-Dimensional Models
• Empirical Process Theory
• +۵ مورد دیگر

Denis Chetverikov is a Professor of Economics at the University of California, Los Angeles (UCLA). His research focuses on econometric theory, with emphasis on high-dimensional models, empirical process theory, bootstrap methods, and applications to asset pricing and policy analysis. He has published in top journals such as Econometrica , Review of Economic Studies , and Annals of Statistics . Education: PhD from the Massachusetts Institute of Technology (MIT). His work bridges theoretical econometrics and computational methods, addressing challenges in modern data analysis. Key contributions include advancements in nonparametric estimation, rank-based inference, and regularization techniques for high-dimensional datasets. Research trends in his articles emphasize methodological innovations for handling complex economic data structures, including factor models, quantile regression, and robust inference frameworks. He has developed statistical software tools like the csranks R package for rank-based analysis. His grants and lab affiliations (if any) are not explicitly detailed in the provided text.

• Statistics
• Regression Analysis
• Bootstrap Methods
• +۳ مورد دیگر

William Cyrus Navidi is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines. He earned his Ph.D. in Statistics from the University of California, Berkeley in 1986 under Jacob Freedman with a dissertation titled "Edgeworth Expansions for Bootstrapping Regression Models". His research spans theoretical and applied statistics: Regression Analysis Bootstrap Methods Edgeworth Expansions Asymptotic Theory Statistical Education Dr. Navidi's work contributes to statistical inference foundations and has influenced statistical pedagogy through textbook authorship. His methodological research focuses on improving approximation techniques for regression models using resampling methods. Professional contact: wnavidi@mines.edu

Laurent Donzéمشاهده پروفایل

استاد

• Applied Statistics
• Econometrics
• Survey Methodology
• +۷ مورد دیگر

Laurent Donzé is a Professor of Applied Statistics and Modelling at the Department of Informatics, Faculty of Economics and Social Sciences, University of Fribourg. He is also a Research Professor at KOF ETH Zurich and a Professor of Econometrics at the University of Neuchâtel. He leads the ASAM research group and has extensive experience in teaching mathematics, econometrics, and statistics. His educational background includes a Ph.D. in Econometrics from the University of Fribourg, followed by research roles at IRE and KOF ETH Zurich. His academic journey reflects deep engagement with statistical methodology and applied economic research. Donzé's primary research interests lie in applied statistics, particularly survey methodology, fuzzy statistics, imputation, causal inference, matching techniques, and wage discrimination analysis . He has made significant contributions to the development and application of fuzzy statistical tools, especially in defuzzification and fuzzy regression. His recent publications (2019–2025) demonstrate a consistent focus on fuzzy confidence intervals, fuzzy p-values, fuzzy ANOVA, and fuzzy regression models , often applied to real-world datasets like SHARE and Swiss SILC. These works emphasize robust statistical inference under uncertainty and contribute to both theoretical and applied advancements in fuzzy statistics. Among his scientific recognitions is the Best Student Paper Award at IJJCI 2020 . He has also edited special issues and contributed to leading journals and conferences in computational intelligence and fuzzy systems. Donzé has been involved in numerous research and teaching initiatives, including grants from the Swiss National Science Foundation, mentoring at the Swiss Study Foundation, and leadership in statistical societies. He has supervised research projects and collaborated with institutions such as Nestlé, the Swiss Federal Statistical Office, and pharmaSuisse. He is actively involved in academic and professional communities, serving as president of the Education and Research section of the Swiss Statistical Society and contributing to the development of statistical infrastructure in social sciences.

Andreas Kurzمشاهده پروفایل

پژوهشگر

• Psychological Diagnostics
• Psychometrics
• Statistical Inference
• +۶ مورد دیگر

Andreas Kurz is a Senior Scientist at the Department of Psychology, University of Salzburg, where he conducts research in psychological diagnostics and psychometric modeling. His work focuses on statistical inference in small sample contexts and conditional likelihood methods, particularly in educational assessment and admission procedures. Position: Senior Scientist Institution: University of Salzburg Department: Department of Psychology Research Group: Admission Procedure for Teaching Professions in the Central Cluster Andreas Kurz holds a Master of Science in Psychology from UMIT TIROL (2021) and a Diploma in Civil Engineering (equivalent to MSc ETH) from ETH Zurich (1995). His academic journey includes roles as a Student Assistant at UMIT TIROL (2019–2022) and a Scientific Assistant at ETH Zurich (2007–2009). His research lies at the intersection of psychology and statistics, with a strong emphasis on methodological rigor in psychometric testing. He develops and applies advanced statistical techniques for small sample inference, particularly using resampling and conditional likelihood frameworks. His work supports valid and reliable assessment in educational and psychological settings. The publications and software tools developed by Andreas Kurz reflect a consistent trend in advancing statistical methodology for psychometrics. His contributions include peer-reviewed articles on the gradient test and conditional inference, as well as R packages like tcl and tclboot that implement these methods for practical use in research and assessment. Andreas Kurz has actively participated in international conferences such as IMPS 2021 and IMPS 2022, presenting on statistical testing in conditional likelihood frameworks. His work is supported by research in psychometrics and statistical computing, with no explicit mention of external grants or advising roles. He is involved in the development of admission procedures for teaching professions, indicating applied research with societal impact in education. His technical expertise bridges civil engineering, psychology, and statistical programming, contributing to robust assessment systems.

Florian Schuberthمشاهده پروفایل

دانشیار

• Structural Equation Modeling
• Partial Least Squares
• Composite-based Methods
• +۷ مورد دیگر

Florian Schuberth is an Associate Professor at the Chair of Product–Market Relations within the Faculty of Engineering Technology at the University of Twente. His research focuses on composite-based structural equation modeling (SEM), particularly Partial Least Squares (PLS) methods, with applications across Information Systems, Social Sciences, and Computer Science. He actively contributes to quantitative research methodology and open science. Bachelor's and Master's in Business Administration and Economics, University of Würzburg PhD in Econometrics, summa cum laude, University of Würzburg (2017) His research interests center on advancing composite-based SEM, including model specification, fit assessment, higher-order constructs, and robust estimation techniques. He emphasizes methodological rigor and reproducibility in social science research. His recent work, reflected in publications and software development, demonstrates a strong trend toward improving the transparency, validity, and accessibility of composite modeling techniques, particularly through R-based tools. He has made significant contributions to model evaluation, measurement invariance, and endogeneity correction in PLS-SEM. Florian Schuberth is a passionate educator, coordinating and teaching courses on quantitative research methods. He is also deeply committed to open science, serving as coordinator of the Open Science Community Twente. Notable contributions include the development and maintenance of the cSEM R package, which enables researchers to perform composite-based SEM in R, promoting open, reproducible research practices. Maintainer of the R package cSEM Coordinator, Open Science Community Twente (since 2022) Active contributor to methodological software and open science infrastructure