Find Academic Supervisors

Rasmus T. VarneskovView profile

Professor

• Econometrics
• Financial Economics
• Time Series Analysis
• +3 more

Rasmus T. Varneskov is a Professor in the Department of Finance at Copenhagen Business School, Denmark, with an active research profile evidenced by publications through 2025. His work bridges theoretical econometrics and financial applications, focusing on methodological innovations for market volatility and return prediction. Research interests include: Econometrics Financial Economics Time Series Analysis Volatility Modeling Asset Pricing Structural Change His recent publications (2022-2025) reveal a concentrated focus on bootstrap techniques for volatility estimation and robust inference in predictive regressions. Key contributions address challenges in high-frequency financial data, Laplace transforms for volatility metrics, and handling persistent autoregressive processes with structural breaks, primarily through advanced statistical modeling. Scientific Awards: No awards, fellowships, or medals are documented in the provided text. Advising: Varneskov has supervised 5 works (likely graduate theses), though student names and grant funding specifics are absent. His mentorship activity aligns with his role as a research-active faculty member in econometrics.

Thomas NaglerView profile

Professor

• Mathematical Statistics
• Machine Learning
• High-dimensional Dependence Models
• +3 more

Thomas Nagler is a Professor at the Department of Statistics, Faculty of Mathematics, Computer Science and Statistics at Ludwig Maximilian University of Munich (LMU Munich). He also serves as a principal investigator at the Munich Center for Machine Learning (MCML), where he leads research at the intersection of mathematical statistics and machine learning. Nagler received his academic training at Technical University of Munich (TU Munich), earning a BSc in Mathematics (2009-2012), followed by an MSc in Mathematical Finance (2012-2014), and ultimately a PhD in Mathematical Statistics (2014-2018). Prior to his current position at LMU Munich, he held assistant professor positions at TU Delft (2021-2022) and Leiden University (2019-2021). Professor Nagler's research focuses on developing novel statistical methods with theoretical guarantees and scalable algorithms. His work spans high-dimensional dependence modeling, particularly using vine copulas, statistical machine learning, time series and functional data analysis, and statistical computing. He emphasizes creating methods that can be practically implemented and applied to solve real-world problems across diverse domains. An analysis of Nagler's recent publications reveals a strong emphasis on vine copula methodology, uncertainty quantification in machine learning, and applications to climate science and epidemiology. His work bridges theoretical statistics with practical implementation, often resulting in open-source software tools that make advanced statistical methods accessible to practitioners. The interdisciplinary nature of his research is evident in collaborations spanning climate modeling, healthcare, and finance. While specific awards are not detailed in the available information, Nagler's research impact is evident through his significant contributions to statistical methodology and his active engagement with the research community through open-source software development. As a principal investigator at MCML and Professor at LMU Munich, Nagler leads a research group focused on advancing statistical methodology for complex data analysis. His GitHub profile indicates active collaboration with students and researchers, with several followers from LMU Munich and other institutions. His research program appears to be well-funded through the MCML and university resources, supporting both methodological development and application-focused projects. Nagler maintains strong ties with the computational statistics community through his leadership of the VineCopula and pyvinecopulib projects, which provide essential tools for dependence modeling. His work with the Munich Center for Machine Learning positions him at the forefront of interdisciplinary research combining statistical theory with practical machine learning applications.

Yili HongView profile

Professor

• Machine Learning
• Reliability Analysis
• Survival Analysis
• +4 more

Yili Hong is a Professor in the Department of Statistics at Virginia Tech's College of Science. Her research focuses on statistical methodologies in reliability analysis, machine learning, survival analysis, and spatial data analysis. She holds a Ph.D. from Iowa State University (2009), with prior degrees from the same institution and the University of Science and Technology of China. Hong has been recognized with awards such as the ASA Physical and Engineering Sciences Award (2017) and the DuPont Young Professor Award (2011). She serves on editorial boards for journals like Technometrics and Journal of Quality Technology, emphasizing contributions to statistical reliability and big data applications. Her work bridges theoretical statistics with practical challenges in engineering and public health, including studies on Lyme disease emergence in Virginia through spatial modeling. Education: Ph.D. in Statistics, Iowa State University, 2009 M.S. in Statistics, Iowa State University, 2005 B.S. in Statistics, University of Science and Technology of China, 2004 Research interests span machine learning robustness, reliability of AI systems, degradation analysis, and statistical computing. Notable achievements include pioneering work on mixture experiments for AI algorithm evaluation and statistical methods for high-performance computing variability. Her recent projects address the reliability of autonomous systems and AI safety through recurrent event data analysis. Awards and recognitions highlight her contributions to statistical methodology and industrial applications. Editorial activities reflect her leadership in advancing statistical practice through peer-reviewed journals.

Sylvain ArlotView profile

Professor

• Statistical Learning
• Non-Parametric Statistics
• Data-Driven Tuning of Learning Algorithms
• +5 more

Sylvain Arlot is a Professor at the Mathematics Department of Université Paris-Saclay, affiliated with the Probability and Statistics team at Laboratoire de Mathématiques d'Orsay. He leads the Celeste INRIA Saclay project-team and is a junior member of the Institut Universitaire de France (IUF) since 2020. His research focuses on statistical learning theory, non-parametric methods, model selection, and change-point detection. Arlot has contributed to foundational work on cross-validation, penalization techniques, and random forests. He co-organizes the Séminaire Palaisien and serves as an associate editor for the Annales de l'Institut Henri Poincaré B. Education: PhD in Mathematics from Université Paris-Sud (2007), HDR (Habilitation) from Université Paris Diderot (2014). Research Interests: Core areas include statistical learning theory, resampling methods (e.g., cross-validation and bootstrap), and applications in high-dimensional data analysis. His work bridges theoretical guarantees with practical algorithm design, emphasizing data-driven model selection and robust estimation techniques. Grants & Projects: Leads the PEPR IA Project Causali-t-AI (2023–2028) and was a member of the ANR Fast-Big project (2018–2023). He coordinates the math-AI program under Labex Mathématique Hadamard. Awards: Junior IUF membership (2020–2025). Labs/Teams: Heads the Celeste team at INRIA Saclay, collaborating on statistical machine learning and data science challenges.

Jérémie LEFEBVREView profile

Assistant Professor

• Market Microstructure
• Financial Regulation
• Stock Market Liquidity
• +3 more

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 RomanoView profile

Professor

• Nonparametric Statistics
• Bootstrap Methods
• Multiple Testing
• +7 more

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 BERTRANDView profile

Professor

• Statistical Modelling
• Statistical Learning
• Artificial Intelligence
• +5 more

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 WestveldView profile

Senior Lecturer

• Bayesian Methodology
• Network Analysis
• Game Theoretic Data
• +3 more

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 WuView profile

Professor

• Survey Methodology
• Complex Surveys
• Empirical Likelihood Methods
• +6 more

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 OganisianView profile

Assistant Professor

• Bayesian Statistics
• Causal Inference
• Missing Data
• +4 more

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
• +3 more

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 BandyopadhyayView profile

Professor

• Spatial and Environmental Statistics
• Time Series Analysis
• Bootstrap/Resampling Methods
• +4 more

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
• +4 more

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 CardotView profile

Professor

• Functional Data Analysis
• Stochastic Algorithms
• Survey Sampling
• +10 more

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 VolodinView profile

Professor

• Statistical Inference
• Limit Theorems
• Distributions on Abstract Spaces
• +12 more

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.