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Michael WolfView profile

Professor

• Nonparametric Inference Methods
• Multiple Testing Procedures
• Financial Econometrics
• +4 more

Michael Wolf is a Professor of Econometrics and Applied Statistics at the University of Zurich's Department of Economics, where he has been since 2005. He holds a Vordiplom in Mathematics from the University of Augsburg (1991), an MSc in Statistics from Stanford University (1995), and a PhD in Statistics from Stanford (1996). His research focuses on nonparametric inference methods, multiple testing procedures, financial econometrics, and large-dimensional covariance matrices. He has contributed to influential work on covariance matrix estimation, portfolio optimization, and statistical methodology. Wolf has served as an Associate Editor for journals including the Annals of Statistics (2004–2007), Statistics and Probability Letters (2014–2016), and the Journal of Financial Econometrics (2019–2022). His academic career includes roles at institutions like UCLA, Universidad Carlos III, and Universitat Pompeu Fabra, where he advanced from Assistant to Associate Professor before joining UZH. His research explores resampling techniques, financial markets analysis, and statistical methods for handling high-dimensional data. Notable contributions include the development of shrinkage-based covariance matrix estimators and their applications in portfolio management. He collaborates with researchers like Olivier Ledoit on nonlinear shrinkage methods and maintains software packages like covShrinkage for covariance matrix estimation. Wolf advises students on thesis topics in econometrics and maintains active roles in professional associations such as the American Statistical Association and the Institute of Mathematical Statistics. His work bridges theoretical statistics and applied finance, addressing challenges in modern portfolio theory, risk management, and statistical inference.

Luo XiaoView profile

Associate Professor

• Functional Data Analysis
• Nonparametric Regression
• Biostatistics
• +5 more

Luo Xiao is an Associate Professor in the Department of Statistics at North Carolina State University. His research focuses on statistical methodologies such as nonparametric regression, functional data analysis, and high-dimensional data analysis, with applications in neuroimaging, wearable computing, and genetic data. He leads the Functional Data Analysis Lab at NC State and teaches courses like Nonparametric Regression and Statistical Inference. Education: Ph.D. in Statistics from Cornell University (2012). Research interests emphasize statistical theory and applications, including: Nonparametric and semiparametric regression Functional data analysis Biostatistics Machine learning integration with functional data Medical imaging and single-cell genetic data analysis Recent publications highlight advancements in multivariate functional modeling, Bayesian inference for longitudinal data, and applications in Alzheimer's disease research. He has advised students such as Wanying Ma and Stephanie T. Chen. His work is supported by collaborations in medical and computational domains. Lab affiliations include the Functional Data Analysis Lab, with ongoing projects on wearable data analysis and multi-modal data integration. Teaching responsibilities include advanced statistical courses.

• Econometrics
• Panel Data Analysis
• Stochastic Modeling
• +2 more

Louise Laage is an Assistant Professor in the Department of Economics at Georgetown University, specializing in econometrics. Her research focuses on advanced econometric methodologies, including panel data analysis, stochastic modeling, and nonparametric techniques. She develops statistical tools for applied economics, such as the MFELOGIT Stata module for estimating marginal effects in fixed-effect logit models. Her recent work addresses topics like time-varying endogeneity in panel models, stochastic block models with covariates, and identification strategies in fixed-effect frameworks. These contributions bridge theoretical econometrics and practical applications in policy analysis and social sciences. Laage's publications span journals and modules since 2018, reflecting her expertise in both methodological innovation and software implementation. She holds no listed awards or advisees, but her work demonstrates a strong focus on advancing quantitative research tools in economics. Contact: louise.laage@georgetown.edu

Christian LÉGERView profile

Professor

• Statistical Methodology
• Bootstrap Methods
• Resampling Techniques
• +4 more

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.

Kelly RamsayView profile

Assistant Professor

• Statistics
• Nonparametric Statistics
• Robust Statistics
• +9 more

Kelly Ramsay is an Assistant Professor in the Department of Mathematics and Statistics at York University, Faculty of Science, Toronto, Canada. Her research is focused on developing nonparametric and robust statistical tools for complex data, with a strong emphasis on differential privacy, functional data analysis, and high-dimensional statistics. She bridges theoretical statistics with real-world applications and computational implementation. Education: PhD in Statistics, University of Waterloo (2018–2022) MSc in Statistics, University of Manitoba (2016–2018) BSc (Honours) in Statistics and Actuarial Science, University of Manitoba (2012–2016) Her research interests include differential privacy, robust inference, functional data, changepoint detection, and data depth. She develops methods that are both theoretically sound and computationally feasible, with applications in f-MRI and privacy-preserving data analysis. Her work often integrates simulation studies and real data applications, reflecting her background in statistical consulting and technical analysis. The recent trend in her publications centers on differentially private statistical methods, particularly for multivariate and functional data. She has made significant contributions to differentially private medians, boxplots, scale testing, and changepoint detection, leveraging data depth and robust estimation techniques. Her work combines theoretical guarantees with practical utility in sensitive data environments. Scientific Awards and Funding: NSERC Discovery Grant (2023) NSERC Launch Supplement (2023) Canada Graduate Scholarship - Doctoral (NSERC, 2019) Sprott Scholarship, University of Waterloo (2021) Canada Graduate Scholarship - Masters (NSERC, 2016) SSC Student Presentation Award (2017) Outstanding Research by an M.Sc. Student, University of Manitoba (2019) Kelly Ramsay has extensive experience in statistical consulting and applied data analysis, having worked at the University of Waterloo and Bison Transport. She has contributed to R packages and large-scale web scraping projects. She actively presents her research at major conferences such as ICORS, SSC, JSM, and SIAM/CAIMS. She has collaborated with researchers including Shoja Chenouri, Dylan Spicker, and Aukosh Jagannath. There is no indication of advising graduate students yet, but her collaborative research suggests strong mentorship and teamwork. She is involved in academic seminars and research days at institutions such as McGill University and through CANSSI. Her work is disseminated via arXiv, peer-reviewed journals, and conference proceedings, highlighting her active role in the statistical research community.

Euan McGonigleView profile

Lecturer

• Nonstationary time series analysis
• Change point detection
• Multiscale methods in statistics
• +3 more

Euan McGonigle is a Lecturer in Statistics at the University of Southampton, affiliated with the Department of Mathematical Sciences and the Statistical Sciences Research Institute (S3RI). He holds a PhD in Statistics and Operational Research from Lancaster University (2020), an MRes in the same field (2017), and an MSci in Mathematics from the University of Glasgow (2016). His research focuses on nonstationary time series analysis, change point detection, and multiscale statistical methods, particularly involving wavelet-based techniques for analyzing complex temporal data. His work emphasizes developing robust methodologies for segmenting time series data, analyzing locally stationary processes, and addressing nonstationarity in statistical models. He has contributed to software packages like TrendLSW and CptNonPar, which implement his research into practical tools for data analysis. McGonigle currently supervises PhD students in Mathematical Sciences and is accepting new PhD applications. Key areas of application include signal processing, financial time series, and environmental data analysis. His recent publications highlight advancements in wavelet-based trend estimation, multiscale variance analysis, and nonparametric change point detection techniques for multivariate settings.

Qin FangView profile

Lecturer

• Dynamic Network Analysis
• Functional Data Analysis
• High-Dimensional Statistics
• +3 more

Dr. Qin Fang is a Lecturer (Assistant Professor) in the Discipline of Business Analytics at the University of Sydney Business School. She joined USYD in 2024 after completing a postdoctoral fellowship at the London School of Economics (LSE) under Prof. Qiwei Yao. Her research focuses on dynamic network analysis, functional data/time series analysis, and high-dimensional statistics. She holds a PhD in Statistics from LSE (2022) and an MSc with the Winton Prize for Academic Excellence. Her teaching experience includes courses at both the University of Sydney (e.g., QBUS2820 Predictive Analytics) and LSE (e.g., ST309 Elementary Data Analytics). She has received multiple accolades, including the LSE Class Teacher Award (Highly Commended) and the University of Sydney Business School Emerging Scholar Research Fellowship (2024–2026). Dr. Fang’s publications emphasize methodological advancements in high-dimensional statistical modeling, with applications to functional time series and network dynamics. Her work bridges theory and practice, addressing challenges in big data analysis and statistical inference. Grants/Awards: Emerging Scholar Research Fellow (USYD), LSE PhD Studentships, Winton Prize Teaching: Predictive Analytics, Data Analytics for Business Capstone, Quantitative Methods

Brian ReichView profile

Professor

• Spatial Statistics
• Bayesian Methods
• Environmental Science
• +6 more

Brian Reich is the Gertrude M Cox Distinguished Professor of Statistics at North Carolina State University. His research focuses on geospatial analytics, spatial statistics, and extreme value analysis, with applications to environmental science, climate change, and public health. He leads the Environmental Statistics Lab, developing computational methods for big spatial data and spatiotemporal modeling. Research Interests: Spatial and Bayesian statistical methods Extreme value analysis and machine learning applications Environmental applications in ecology, epidemiology, and climate science Awards: Gertrude M Cox Distinguished Professorship Grants & Programs: NSF Research Training Group (UQ4Life) NSF REU Site (DRUMS for undergraduates) Carolina Thyroid Cancer Study Labs/Teams: Environmental Statistics Lab Collaborations on marine conservation, wildfire management, and PFAS exposure studies

Rida BenhaddouView profile

Associate Professor

• Nonparametric Statistics
• Inverse Problems in Statistics
• Empirical Bayes Estimation
• +2 more

Rida Benhaddou is an Associate Professor in the Department of Mathematics at the College of Arts and Sciences, Ohio University. She is based in Morton Hall 563 and contributes to the academic and research missions of the department. Education: Ph.D., University of Central Florida Her research focuses on advanced statistical methodologies, particularly in nonparametric inference and inverse problems. She specializes in empirical Bayes methods, which bridge Bayesian and frequentist approaches in data analysis. These areas are critical in modern statistical theory and applications where distributional assumptions are weak or unknown. While no recent publications are listed in the provided text, her research interests suggest a strong theoretical foundation in mathematical statistics and statistical learning. Scientific Awards: No awards listed. She has not been mentioned as advising any students in the provided content. There is no mention of grants, research labs, or collaborative teams. Her work appears to be centered in theoretical statistics with potential applications in data science and inference under uncertainty.

Haiyan CaiView profile

Associate Professor

• Statistics
• Stochastic Simulations
• Computational Statistics
• +6 more

Haiyan Cai is an Associate Professor in the Department of Mathematics, Physics, Astronomy and Statistics at the University of Missouri—St. Louis (UMSL). She holds a PhD in Mathematical Statistics from the University of Maryland at College Park (1988) and has expertise in statistics, stochastic simulations, and computational mathematics. Education: PhD in Mathematical Statistics (1988), University of Maryland at College Park MS in Mathematical Statistics (1986), University of Maryland at College Park BS in Mathematics (1982), Xiamen University Her research focuses on stochastic simulations, computational statistics, statistical modeling, spatial statistics, and signal processing techniques like synchrosqueezing transforms. She has published extensively in journals such as Signal Processing , Statistical Analysis and Data Mining , and Applied and Computational Harmonic Analysis . Recent work explores adaptive algorithms for non-stationary signal separation and classification probability methods for statistical testing. While no awards or grants are explicitly listed in the provided text, her contributions to computational statistics and signal processing reflect significant academic impact.

Igor PrünsterView profile

Professor

• Bayesian Nonparametrics
• Bayesian Asymptotics
• Foundations of Statistical Inference
• +7 more

Igor Prünster is a Professor of Statistics at Bocconi University, where he serves as Director of the Ph.D. Program in Statistics and Computer Science. He is a Fellow of the Bocconi Institute of Data Science & Analytics (BIDSA) and other distinguished societies. Bayesian Nonparametrics Bayesian Asymptotics Probabilistic Machine Learning Random Measures His editorial roles include Editor-in-Chief of Bayesian Analysis and Associate Editor of Annals of Statistics . He has mentored students who have won competitive awards, such as the Blackwell-Rosenbluth Award (2024) and Lawrence D. Brown Ph.D. Student Award (2024). Scientific Awards Fellow, American Statistical Association (2019) Fellow, International Society for Bayesian Analysis (2018) Fellow, Institute of Mathematical Statistics (2015)

Johannes Schmidt-HieberView profile

Professor

• Statistics
• Machine Learning
• Deep Learning
• +3 more

Johannes Schmidt-Hieber is a Professor of Statistics at the University of Twente, Department of Applied Mathematics. He has held positions at Leiden University (2014-2018) and conducted postdoctoral research at Vrije Universiteit Amsterdam and ENSAE Paris. His work bridges deep learning theory with nonparametric statistics, focusing on convergence rates, regularization techniques, and Bayesian inference. Education: PhD from Universität Göttingen and Bern (2010), studies at Freiburg and Göttingen Research: Specializes in statistical learning theory, neural network analysis, and high-dimensional inference His 15 most recent publications address topics like GCN convolutions in regression, implicit regularization in deep networks, and posterior contraction in Gaussian processes. Key contributions include theoretical guarantees for optimization algorithms and bias-variance trade-off analysis. Scientific recognition includes: ERC Consolidator Grant (2024-2029) IMS Fellow (2024) Van Dantzig Award (2025) Frontiers of Science Award (2025) NWO Vidi grant (2019-2024) He served as associate editor for leading journals and organized multiple international workshops. Current projects include mathematical foundations of machine learning and statistical uncertainty quantification.

Zhimei RenView profile

Assistant Professor

• Distribution-free inference
• Multiple hypothesis testing
• Data-driven decision making
• +5 more

Zhimei Ren is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. She received her Ph.D. in Statistics from Stanford University in 2021 under the supervision of Professor Emmanuel Candès and completed a postdoctoral fellowship at the University of Chicago from 2021-2023, advised by Professor Rina Foygel Barber. Prior to her doctoral studies, she earned a B.S. in Statistics from Peking University in 2016. Her educational background includes: Ph.D. in Statistics, Stanford University, 2021 (Advisor: Emmanuel Candès) B.S. in Statistics, Peking University, 2016 Dr. Ren's research focuses on developing distribution-free statistical methods with applications across various domains. Her work centers on distribution-free inference, multiple hypothesis testing, data-driven decision making, causal inference, and survival analysis. She has made significant contributions to conformal prediction frameworks, developing novel approaches for selection-conditional coverage, interactive framework for conformal selection, and applications in survival analysis. Her research bridges theoretical statistics with practical applications in healthcare, education technology, and machine learning model validation. Analysis of her publication record reveals a strong focus on conformal prediction methods and their extensions. Over the past two years, she has published extensively on topics including conformal selection frameworks, distribution-free inference for survival analysis, and applications of conformal methods to foundation models and time series data. Her work demonstrates a consistent theme of developing theoretically sound statistical methods that provide rigorous guarantees without distributional assumptions, making them broadly applicable across scientific domains. Her notable scientific achievements include: Rising Star in Data Science, University of Chicago, 2022 Waterloo Student Conference Presentation Award, University of Waterloo, 2021 First Prize of the 2018 Citadel Datathon, Stanford University, 2018 Dr. Ren is actively engaged in academic service, currently co-organizing the International Seminar on Selective Inference with prominent researchers in the field. She teaches advanced statistical courses including Statistical Inference (STAT4310) and Seminar in Advanced Applications of Statistics (STAT9910) at the University of Pennsylvania. Her teaching focuses on statistical methodology and inference, preparing students for both theoretical and applied work in statistics and data science. Dr. Ren has also developed several R packages including cfsurvival for conformalized survival analysis and derandomKnock for implementing derandomized knockoffs procedures, demonstrating her commitment to making her research accessible and usable by the broader scientific community.

• Spatial Statistics
• Statistical Consulting
• Biostatistics
• +3 more

Ronald Barry is a Professor of Statistics at the University of Alaska Fairbanks, where he has maintained an active research and consulting practice since 1991. Holding a PhD from the University of California, Irvine, he operates from office CH 201C (contact: 907-474-7226, rpbarry@alaska.edu) and specializes in methodological innovations with cross-disciplinary applications. Education PhD in Statistics, University of California, Irvine (1991) Research Focus Barry's core expertise lies in spatial statistics, developing advanced techniques for geolocated data analysis in regions with irregular boundaries and holes. His work includes diffusion-based density estimators, nonparametric co-kriging, and lattice-based smoothing algorithms implemented through R packages. This methodological research extends to biostatistical applications in wildlife biology, nutrition, and fisheries, alongside chemometrics projects involving NMR spectroscopy for chemical mixture analysis and medical statistics research on sentinel node biopsy failures in melanoma using SEER cancer data. Publication Trends His recent publications (2015-2021) reveal three dominant trajectories: (1) continuous refinement of spatial modeling techniques for complex geometries, (2) expansion into biomedical and petroleum engineering applications through collaborative consulting, and (3) development of statistical software tools that bridge theoretical methodology with practical implementation. The interdisciplinary nature of his work is evident in publications spanning environmental statistics, reservoir engineering, and adolescent health studies. Scientific Awards No specific awards, fellowships, or honors were documented in the source material. Advising and Collaborations Barry actively mentors graduate students at UAF, with Bernard, Paryani, Canary, and Maier serving as primary authors on collaborative publications. His extensive consulting portfolio—covering experimental design, analysis, and reporting across wildlife biology, nutrition, and fisheries—suggests significant engagement with external research funding, though specific grants aren't detailed. Current projects indicate ongoing collaboration with medical researchers on melanoma diagnostics and petroleum engineers on reservoir modeling.

Stuart BarberView profile

Senior Lecturer

• Wavelet Methods in Statistics
• Statistical Bioinformatics
• Applied Statistics
• +5 more

Dr Stuart Barber is a Senior Lecturer in Statistics at the School of Mathematics, University of Leeds, affiliated with the Faculty of Engineering and Physical Sciences. His expertise spans wavelet methods, statistical bioinformatics, and applied statistics, with interdisciplinary applications in fields such as industrial tomography, spatial data analysis, and genomics. His research focuses on wavelet-based statistical techniques, Bayesian inference, and computational methods. Recent applications include clustering, phylogenetics, anaesthesiology, and copy-number genomic data segmentation. Research trends from his publications highlight advanced methodologies like Gaussian process modeling, Voronoi tessellations, and Approximate Bayesian Computation (ABC), alongside bioinformatics-driven studies on protein alignment and structure analysis. Responsibilities include serving as Head of the Department of Statistics. He actively supervises postgraduate researchers, including Fatih Gezer, and acknowledges support from the Simons Foundation. His work is associated with the Statistics research group, fostering collaborations and PhD opportunities in diverse statistical domains.