جستجوی استاد راهنمای دانشگاهی

Jérémie LEFEBVREمشاهده پروفایل

استادیار

• Market Microstructure
• Financial Regulation
• Stock Market Liquidity
• +۳ مورد دیگر

Jérémie Lefebvre is an Assistant Professor of Finance at IÉSEG School of Management in Paris, France. He holds a Ph.D. in Finance from Tilburg University (2011), an M.Phil. in Econometrics (2010), and an MSc in Econometrics from the University of Montreal (2005). His research focuses on market microstructure, financial regulation, and stock market liquidity , with a particular emphasis on legal insider trading dynamics and regulatory policy impacts. He has published extensively in peer-reviewed journals such as International Review of Law and Economics and De Economist . His teaching spans Corporate Finance , Finance Strategy , and Financial Econometrics , across undergraduate and graduate programs including the MSc in Investment Banking and Capital Markets. Prior to IÉSEG, he was a Researcher at the Louvain School of Management (2009–2010). His work bridges legal frameworks and financial markets, analyzing how regulatory changes affect liquidity and market behavior in both developed and emerging economies like Vietnam and the Netherlands. Key research themes include pre-trade disclosure mechanisms , insider trading detection , and cross-market regulatory comparisons . His recent studies explore liquidity impacts of insider transactions in Vietnam’s stock market and the Netherlands’ regulatory environment. He has also developed methodologies for analyzing market reactions using event studies and bootstrap techniques. While no scientific awards are explicitly listed, his publications demonstrate significant contributions to understanding market microstructure and regulatory compliance. His academic advising and grant activities remain unspecified in available records. Lefebvre’s interdisciplinary approach integrates legal, economic, and quantitative perspectives to address contemporary challenges in financial markets.

Joseph Romanoمشاهده پروفایل

استاد

• Nonparametric Statistics
• Bootstrap Methods
• Multiple Testing
• +۷ مورد دیگر

Joseph Romano is a Professor of Statistics and Economics at Stanford University, where he has served since 1986. He holds dual appointments in both the Department of Statistics and the Department of Economics. His research focuses on theoretical statistics, nonparametric methods, bootstrap techniques, multiple testing, and their applications in econometrics, climate science, genetics, and clinical trials. He has received numerous awards, including the LGBTQ+ Scientist of the Year (2021) and the Presidential Young Investigator Award (1989–1994). Education: Ph.D. in Statistics, University of California, Berkeley (1986) M.S. in Statistics, University of California, Berkeley (1983) B.A. in Statistics, Princeton University (1982), Summa Cum Laude Research interests include developing robust statistical methodologies for high-dimensional data, controlling errors in multiple testing, and applying resampling techniques to complex datasets. He has authored over 100 papers, focusing on topics like permutation tests, bootstrap methods, and econometric modeling. Notable works include advancements in fixed sequence testing and randomized inference for modern data challenges. Awards and Recognition: Fellow, Institute of Mathematical Statistics (2000) Fellow, International Association of Applied Econometrics (2020) Canadian Journal of Statistics Award (1989) Romano has advised numerous students, including doctoral candidates in statistics and economics. He has held administrative roles at Stanford, such as Associate Chair and member of faculty committees. His grants include NSF-funded projects on resampling methods, multiple testing, and econometric theory. He is a vocal advocate for LGBTQ+ visibility in academia and has contributed to initiatives like 500 Queer Scientists. Beyond academia, he enjoys music, tennis, cooking, and architecture.

Frédéric BERTRANDمشاهده پروفایل

استاد

• Statistical Modelling
• Statistical Learning
• Artificial Intelligence
• +۵ مورد دیگر

Frédéric BERTRAND is a Full Professor at the Université de Technologie de Troyes (UTT), part of the Computer Laboratory and Digital Society (LIST3N). He previously held positions at the Université de Strasbourg and Institut de Recherche Mathématique Avancée (IRMA). His academic qualifications include a habilitation to direct research (HDR) from the Université de Strasbourg, a Ph.D. from Université Louis Pasteur, and membership in the École Normale Supérieure de Lyon. He specializes in statistical modeling, machine learning, and artificial intelligence, with applications in bioinformatics, biostatistics, and medical data analysis. Roles and Affiliations: Full Professor at UTT Director of the Mastère Spécialisé in Big Data Analytical and Decisional Aspects Responsible for the OSS ED361 SPI specialty Responsible for the PEA Impact program Member of the LIST3N laboratory Research Interests: His work focuses on statistical and machine learning methods applied to complex systems, including bioinformatics, medical data analysis, and process mining. Key areas include Bayesian networks, partial least squares regression (PLS), variable selection algorithms, and handling missing data in large-scale datasets. He has developed multiple R packages, such as selectBoost , bootPLS , and Patterns , which are widely used in statistical modeling and data analysis. Publications and Contributions: He has authored/co-authored over 38 scientific articles, 14 books, and 14 R packages. His research emphasizes methodological advancements in statistics and their practical applications in biology, healthcare, and industry. Notable contributions include developing algorithms for high-dimensional data analysis and collaborative projects on cancer genomics and proteomics. Teaching: He teaches advanced courses in statistics, mathematics, and data analysis at the graduate and postgraduate levels, including modules on uncertainty analysis and complex systems at UTT and the Université de Strasbourg.

Anton Westveldمشاهده پروفایل

مدرس ارشد

• Bayesian Methodology
• Network Analysis
• Game Theoretic Data
• +۳ مورد دیگر

Anton Westveld is a Senior Lecturer in the Department of Statistics at the Australian National University (ANU), within the Research School of Finance, Actuarial Studies & Statistics (RSFAS). He also serves as an Affiliate Associate Professor at Virginia Commonwealth University since August 2023. His research focuses on Bayesian methodology, network analysis, game theoretic data, and statistical causality, with notable contributions to ecological modeling and agent-based stochastic simulations. Westveld holds a Bachelor’s in Economics and Political Science from the University of Michigan (Ann Arbor), a Master’s in Applied Economics and Statistics from the same institution, and a PhD in Statistics from the University of Washington. His work has been published in prestigious journals like the Annals of Applied Statistics and Proceedings of the National Academy of Sciences . His research interests span Bayesian inference, relational data analysis, and causal modeling, with applications in ecological and health sciences. Recent work includes developing Bayesian methods for ecological drivers in marine viral communities and latent socioeconomic health indices for policy evaluation. Notable articles include analyses of menstrual disorder surveys using Gaussian copulas, ecological metagenomics studies, and Bayesian-optimized bootstrap techniques for uncertainty quantification. His interdisciplinary collaborations bridge statistics with environmental science, public health, and economics.

Changbao Wuمشاهده پروفایل

استاد

• Survey Methodology
• Complex Surveys
• Empirical Likelihood Methods
• +۶ مورد دیگر

Changbao Wu is a Professor and Chair of the Department of Statistics and Actuarial Science at the University of Waterloo, within the Faculty of Mathematics. His research focuses on complex survey design and analysis, with expertise in empirical likelihood methods, resampling techniques, and missing data problems. He has developed R packages to implement these methods and is a Fellow of the American Statistical Association and Elected Member of the International Statistical Institute, receiving the 2012 CRM-SSC Prize in Statistics. Education: PhD in Statistics (1999) from Simon Fraser University under Prof. Randy Sitter. He has been at Waterloo since 1999. Research Interests: Design and analysis of complex surveys Semiparametric/nonparametric methods Empirical likelihood methods Resampling (jackknife/bootstrap) Missing data and measurement error Survey sampling methodology Professional Roles: Associate Editor: Survey Methodology (2006–), Biometrika (2008–), Journal of Nonparametric Statistics (2011–) Member: Statistics Canada’s Advisory Committee on Statistical Methods Recent Publications Trends: Focus on non-probability survey samples, causal inference, and pseudo-empirical likelihood methods. Recent work addresses challenges in combining non-probability and probability samples, doubly robust estimation, and calibration techniques. Awards: Fellow of ASA (since 2020) Elected ISI Member (since 2012) CRM-SSC Prize (2012) Grants and Advising: Extensive grants related to survey methodology and statistical inference. Advises on the International Tobacco Control (ITC) China Survey and collaborates on large-scale studies like the Canadian Longitudinal Study on Aging (CLSA). Labs/Teams: Leads research in survey statistics and statistical methodology at the University of Waterloo, contributing to interdisciplinary projects involving health, economics, and social sciences.

Arman Oganisianمشاهده پروفایل

استادیار

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

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

• Saddlepoint-Based Bootstrap
• Time Series Modeling
• Longitudinal Modeling
• +۳ مورد دیگر

Dr. A. Alexandre Trindade is a Professor in the Department of Mathematics & Statistics at Texas Tech University. His research focuses on developing novel statistical methodologies for complex data structures, with applications spanning finance, physics, and environmental science. He teaches graduate courses in Time Series Analysis and Nonparametric Statistics, and serves as advisor for the undergraduate Actuarial Science Minor program. His primary research areas include: Saddlepoint-Based Bootstrap (SPBB) : Methods for approximate inference in complex models with intractable distributions Time Series & Volatility Modeling : Multivariate autoregressive models, GARCH variants, and asymmetric distributions Spatial & Longitudinal Analysis : State-space models for missing data and spatial regression techniques Tail Risk Quantification : Nonparametric estimation of systemic risk measures like CoVaR Nonparametric Inference : Density estimation and signal detection for high-energy physics His publications demonstrate consistent innovation in statistical theory, particularly in developing resampling-based inference and extending time series methodologies. Collaborative projects include interdisciplinary work with petroleum engineering, nuclear science, and finance. Dr. Trindade maintains active research partnerships with national laboratories and industry, including reliability studies for Boeing and medical device research. He has developed specialized software for multivariate time series analysis and maintains public repositories for statistical computing resources.

Soutir Bandyopadhyayمشاهده پروفایل

استاد

• Spatial and Environmental Statistics
• Time Series Analysis
• Bootstrap/Resampling Methods
• +۴ مورد دیگر

Soutir Bandyopadhyay is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines. He holds a PhD in Statistics from Texas A&M University, an M.Stat from the Indian Statistical Institute, and a B.Sc in Statistics from St. Xavier’s College. His research focuses on spatial and environmental statistics, time series analysis, bootstrap methods, and large sample theory, with applications in energy systems, climate science, and machine learning. Recent work includes advancements in spatial extremes modeling using neural networks, risk analysis in oil and gas flowlines, and statistical frameworks for energy load forecasting. He has organized major conferences including the 2023 IISA Annual Conference and the 2024 workshop on uncertainty in civil/environmental systems. As Director of the Data Science Program at Mines since 2025, he leads interdisciplinary initiatives. Key contributions include development of the National Climate Database (NCDB), bias-correction methods for solar radiation data, and R packages like glmfitmiss for handling missing data in GLMs. His students have achieved notable recognitions, including Fulbright awards and ASA competition honors.

• Computational Statistics
• Markov Chain Monte Carlo Methods
• Bootstrap and Resampling Techniques
• +۴ مورد دیگر

Dr. Angelo J. Canty is an Associate Professor in the Department of Mathematics and Statistics at McMaster University, Canada. His research focuses on computational statistics, genetic data analysis, and resampling methods. B.Sc. (1989) from University College Cork, Ireland M.Sc. (1991) and Ph.D. (1995) from the University of Toronto Postdoctoral work at University of Oxford and EPFL, Lausanne Assistant Professor at Concordia University (1998–2001) before joining McMaster His research spans computational statistics, with key contributions to: Markov Chain Monte Carlo convergence diagnostics Bootstrap and resampling techniques Saddlepoint approximations in statistical inference Efficient algorithm implementation for survey data analysis Genetic data analysis (microarrays, GWAS) His publications highlight a focus on automating convergence assessment (1994–1999), resampling for labor statistics (1999), and saddlepoint approximations in resampling (1996–1999). He developed an influential S-Plus library for resampling methods. Teaching includes: Statistics 4C03/6C03: Generalized Linear Models (Undergraduate/Graduate) Statistics 752: Linear Models and Experimental Design (Graduate)

Hervé Cardotمشاهده پروفایل

استاد

• Functional Data Analysis
• Stochastic Algorithms
• Survey Sampling
• +۱۰ مورد دیگر

Hervé Cardot is a Professor of Statistics and team leader of the Statistics, Probability, Optimization and Control (SPOC) group at the Institut de Mathématiques de Bourgogne (IMB) , University of Burgundy. His research spans functional data analysis, stochastic algorithms for robust estimation, survey sampling methodology, and applied statistics across diverse fields. Research Interests : Functional data analysis, online principal component analysis, zero-inflated regression, survey sampling, nonparametric estimation Key Applications : Agriculture, climatology, energy consumption, food science, remote sensing, medical imaging Recent Work includes: Developing statistical frameworks for analyzing categorical trajectories using multivariate functional PCA (2025) Advancing bias-robust estimation for functional data in survey sampling (2020) Creating online stochastic algorithms for high-dimensional robust estimation (2017) Applying semi-Markov models to sensory analysis (2018) Teaching at the University of Burgundy includes courses on statistical modeling, big data analysis, and applied statistics for economics and psychology. He supervises PhD students in statistics and co-edits Statistics and Probability Letters .

Andrei Volodinمشاهده پروفایل

استاد

• Statistical Inference
• Limit Theorems
• Distributions on Abstract Spaces
• +۱۲ مورد دیگر

Andrei Volodin is a Professor in the Department of Mathematics and Statistics at the University of Regina, Canada. He serves as the Co-op Work/Study Coordinator and has an extensive publication record spanning probability theory, statistical inference, and applied statistics. His research focuses on limit theorems, bootstrap methods, and distributional analysis with applications to quality control and healthcare economics.

Nan Zouمشاهده پروفایل

مدرس

• Time series analysis
• Extreme value theory
• Resampling-type methods
• +۳ مورد دیگر

Nan Zou is a Lecturer in Statistics at Macquarie University's School of Mathematical and Physical Sciences. She holds a Ph.D. in Mathematics with a Specialization in Statistics from the University of California San Diego (2017) and a B.B.A. in Management Science from Renmin University of China (2011). Her research focuses on statistical methodology and theory in time series analysis, extreme value theory, resampling techniques, and dynamical systems. She has collaborated internationally and received the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Her recent work addresses bias correction in extreme value analysis, bootstrap methods for unit root testing, and multivariate time series extremes. Key contributions include reweighted madogram estimators and seasonal unit root tests under periodic variation. Current research emphasizes statistical inference for massive datasets and dynamical systems. Nan Zou actively supervises PhD and MRes students at Macquarie University, encouraging applications from eligible candidates. She has published in journals like the Annals of Statistics and Journal of Nonparametric Statistics , with a focus on methodological advancements in statistical theory.

Jake Soloffمشاهده پروفایل

استادیار

• Statistical Machine Learning
• Empirical Bayes
• Nonparametric Estimation
• +۶ مورد دیگر

Jake Soloff is an Assistant Professor of Statistics at the University of Michigan. He earned his Ph.D. in Statistics from UC Berkeley in 2022 under advisors Adityanand Guntuboyina and Michael Jordan, followed by a postdoctoral fellowship at the University of Chicago with Rina Foygel Barber and Rebecca Willett. Research Focus: Statistical machine learning, algorithmic stability, empirical Bayes methods, and theoretical analysis of calibration and false discovery rate control. Key Contributions: Developed the Inflated Argmax for stable classification, proposed Cutoff Calibration Error metrics, analyzed distribution-free properties of isotonic regression, and created resampling techniques for stability guarantees. Publication Trends : Recent work emphasizes stability in machine learning pipelines, empirical Bayes optimization for high-dimensional problems, and novel approaches to hypothesis testing with stochastic monotonicity constraints. Software Development : Maintains the NPEB Python package for nonparametric empirical Bayes estimation.

• Causal Machine Learning
• Labour Economics
• Health Economics
• +۳ مورد دیگر

Michael Lechner is a Professor of Economics at the University of St. Gallen (HSG) with a focus on Applied Microeconometrics. His work integrates causal machine learning with labor, health, and sports economics. He holds affiliations with institutions such as the Centre for Economic Policy Research (CEPR) and the Institute for the Study of Labor (IZA). Lechner earned his academic credentials from the University of Mannheim (1990-1998). His research explores topics like policy evaluation, treatment heterogeneity, and the socioeconomic impacts of sports and health interventions. He has published extensively on causal machine learning frameworks and their applications in public policy. His editorial roles include co-editing Empirical Economics (2014-2017) and serving on editorial boards of journals like German Economic Review and Econometric Reviews . He is a member of the German National Academy of Sciences Leopoldina, recognizing his contributions to economic research. Lechner’s recent work emphasizes causal machine learning to analyze labor market policies, sports participation effects, and pandemic-driven market shifts. His research bridges econometric rigor with modern computational techniques to address real-world policy challenges.

Dimitris N. Politisمشاهده پروفایل

استاد

• Time Series Analysis
• Nonparametric Statistics
• Bootstrap Methods
• +۶ مورد دیگر

Dimitris N. Politis is a Distinguished Professor in the Department of Mathematics and the Halicioglu Data Science Institute at the University of California, San Diego. He holds the prestigious Halicioglu Data Science Institute Chancellor's Endowed Chair II position and has been affiliated with UCSD since 1997, progressing from Associate Professor to his current distinguished position. His educational background includes a Ph.D. in Statistics from Stanford University (1990), along with multiple master's degrees in Statistics, Mathematics, and Computer and Systems Engineering from Stanford and Rensselaer Polytechnic Institute. Professor Politis's research focuses on advanced statistical methodologies, with particular expertise in: Time Series and Random Fields analysis Computer-Intensive Methods in Statistics Resampling and Subsampling for Dependent Observations Spatial Statistics and Point Processes Nonparametric Spectral and Probability Density Estimation Model-free Prediction and Regression Information Theory and Signal Processing Econometric Analysis of Financial Time Series His scholarly output includes over 100 journal papers and several influential books, most notably "SUBSAMPLING" (1999), "MODEL-FREE PREDICTION AND REGRESSION" (2015), and "TIME SERIES: A FIRST COURSE WITH BOOTSTRAP STARTER" (2020), which has become a key educational resource in the field. Professor Politis has received numerous prestigious awards and honors: Guggenheim Fellowship (2011) Fellow of the American Statistical Association (2011) Fellow of the Institute of Mathematical Statistics (2004) Distinguished Author Award from the Journal of Time Series Analysis (2020) Econometric Theory Multa Scripsit Award (2013) Tjalling C. Koopmans Econometric Theory Prize (2012) He has been principal investigator on numerous NSF and NIH grants, including current funding for "Computer-intensive methods for dependent and complex data" (NSF DMS 24-13718, 2024). Professor Politis has held significant leadership roles, including serving as Chair of the Faculty Council of the Halicioglu Data Science Institute (2019-2023) and Associate Director (Founding) of the Institute (2018-2024). As a co-founder of the International Society for NonParametric Statistics, Professor Politis has made substantial contributions to the organization of major conferences and workshops in his field, including the First Conference of the International Society for NonParametric Statistics in 2012. His editorial work includes serving as Co-Editor of the Journal of Time Series Analysis since 2013 and Senior Editor for the ACM/IMS Journal of Data Science since 2022.