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Florian SchuberthView profile

Associate Professor

• Structural Equation Modeling
• Partial Least Squares
• Composite-based Methods
• +7 more

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

• Statistical Inverse Problems
• Applied Statistics in Science and Technology
• Medical Imaging Reconstruction
• +4 more

Nicolai Bissantz is a Senior Lecturer in the Department of Stochastics at Ruhr University Bochum's Faculty of Mathematics. His research focuses on statistical inverse problems, applied statistics in science and technology, and medical imaging reconstruction. He contributes to interdisciplinary projects in cybersecurity, astrophysics, and biophotonics. PhD supervision: Advises on statistical methods in interdisciplinary applications. Grants: Collaborates on BMBF-funded projects improving diagnostic precision in medical imaging. Recent work includes a 2024 Distinguished Paper Award for advancing software fuzzing evaluation methodologies. His statistical methods address challenges in internet security, medical imaging, and astrophysical modeling.

Ingrid VAN KEILEGOMView profile

Professor

• Survival Analysis
• Duration Analysis
• Measurement Error
• +5 more

Ingrid VAN KEILEGOM is a Full Professor at the Catholic University of Louvain (UCL), affiliated with the Institute of Statistics, Biostatistics and Actuarial Sciences. Her research spans survival analysis, econometrics, nonparametric methods, and copula theory, with significant contributions to statistical methodology for censored data and regression models. Research Expertise Professor Van Keilegom's work focuses on developing innovative statistical methods for complex data structures. Key areas include: Survival/duration analysis with cure models and dependent censoring Econometric techniques for instrumental variables and frontier models Semiparametric regression for measurement error problems Copula-based dependence modeling in multivariate settings Publication Trends Her recent publications (2015-2016) demonstrate a strong focus on semiparametric methods for censored data, frontier estimation in econometrics, and goodness-of-fit testing. Common themes include developing robust inference procedures for survival models, addressing endogeneity in econometric applications, and creating validation techniques for complex regression structures. Awards and Honors Fellow, Institute of Mathematical Statistics (2008) Fellow, American Statistical Association (2013) ERC Advanced Grant (2016-2021): Semiparametric inference for complex structural models ERC Starting Grant (2008-2014): M-/Z-estimation in semiparametric statistics Academic Leadership She supervises 5 doctoral students and has graduated 7 PhDs since 2005. Her editorial leadership includes serving as Joint Editor for Journal of the Royal Statistical Society - Series B (2012-2015) and associate roles in 6 other statistical journals. Research is supported by major grants including two European Research Council awards.

John E. KolassaView profile

Professor

• Asymptotics
• Biostatistics
• Nonparametric Statistics
• +3 more

John E. Kolassa is a Professor of Statistics at Rutgers, the State University of New Jersey. He is affiliated with the Department of Statistics, where he conducts research and teaching in asymptotics and biostatistics. His academic credentials include a Ph.D. from the University of Chicago, and he maintains an active research profile with numerous publications and contributions to statistical methodology. Ph.D., University of Chicago Dr. Kolassa's research is centered on asymptotic theory, nonparametric statistics, and biostatistical methods. His work includes the development and analysis of saddlepoint approximations, Edgeworth expansions, and inference techniques for complex data. He has a strong focus on theoretical statistics, with applications in medical and biological contexts. His expertise spans categorical data analysis, life data analysis, and regression models, as reflected in his teaching of graduate courses such as 960:555 (Nonparametric Statistics) and 960:583 (Methods of Inference). The 15 most recent publications, spanning from 2021 to 2013, demonstrate a consistent focus on statistical theory and methodology. Key themes include the refinement of approximation techniques (e.g., Edgeworth and saddlepoint), inference in complex models (e.g., posterior densities, penalized likelihood), and nonparametric methods. His work often addresses foundational issues in statistical inference, such as the validity of expansions, the reliability of p-values, and the handling of zero-event studies in meta-analysis. The research bridges theoretical development with practical application in biostatistics and health sciences. Fellow of the American Statistical Association (ASA) Fellow of the Institute of Mathematical Statistics (IMS) Elected member of the International Statistical Institute (ISI) Editor, Stat Dr. Kolassa is an active advisor and researcher, contributing to the academic community through his editorial role for the journal Stat . He has received significant recognition through his fellowships in the ASA and IMS, highlighting his impact on the field. His work has been supported through academic appointments and professional activities, though specific grant details are not provided in the source material. He is also involved in the development of statistical software, having created R packages for nonparametric methods and infinite estimates. Dr. Kolassa leads a research team focused on theoretical and applied statistics, with a particular emphasis on developing and validating statistical methodologies. He has mentored students and collaborated on interdisciplinary research, particularly in biostatistics and health outcomes. His laboratory or research group is centered on computational and theoretical statistics, utilizing tools like R for simulation and analysis.

Paul MarriottView profile

Professor

• Geometric Statistics
• Differential Geometry
• Mixture Models
• +6 more

Paul Marriott is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His research focuses on integrating geometric principles, particularly differential and convex geometry, into statistical methodologies, with a recent emphasis on mixture models and information geometry. He has published extensively across diverse journals such as Biometrika, Annals of Statistics, and Psychological Medicine, bridging theoretical and applied statistics. Education: PhD, University of Warwick (1989) MA, University of Oxford (1984) Research Trends: His work explores geometric frameworks for statistical inference, mixture model parameterization, and robustness analysis. Recent publications highlight causal modeling, neural spike train analysis, and high-dimensional data applications. Contact: Office: Mathematics & Computer Building (M3) 4204, Phone: 519-888-4911 x35545, Email: pmarriot@math.uwaterloo.ca

Wei YangView profile

Professor

• Computational Chemistry
• Biophysics
• Structural Biology
• +7 more

Dr. Wei Yang is a Professor and Chair at Florida State University, where he leads research in computational biophysics. He earned his Ph.D. from the State University of New York at Stony Brook in 2001. His laboratory develops advanced atomistic simulation techniques to study complex biomolecular systems, with interdisciplinary applications spanning biochemistry, materials science, and biophysics. Research Focus: Dr. Yang's work centers on three pillars: First-principles protein/ligand design using quantum mechanical approaches Multi-scale simulation of biochemical events (DNA repair, enzyme kinetics, biomolecular recognition) Development of efficient computational methods including QM/MM, free energy sampling, and conformational analysis His recent publications demonstrate strong emphasis on methodological innovations in molecular dynamics (e.g., free energy surface construction, polarizable force fields) with applications to protein dynamics, membrane transport, and catalytic mechanisms. Work frequently integrates machine learning resampling techniques and experimental validation. Dr. Yang directs an active computational research group (Wei Yang Lab) focused on pushing boundaries in biomolecular simulation. While no specific awards or grants are detailed in available sources, his scholarly output reflects sustained contributions to computational chemistry methodology.

Geordie RichardsView profile

Assistant Professor

• Nonlinear Partial Differential Equations
• Probability Theory
• Dynamical Systems
• +6 more

Geordie Richards is an Assistant Professor in the Department of Mathematics and Statistics at the University of Guelph. He holds a PhD in Mathematics from the University of Toronto (2012) and has held academic positions at the University of Minnesota, University of Rochester, Utah State University, and the University of Toronto Mississauga. PhD in Mathematics, University of Toronto (2012) NSERC Graduate Scholarship (2006) Richards' research focuses on the analysis of deterministic and stochastic nonlinear partial differential equations (PDEs) arising in physics and engineering, particularly those modeling fluid dynamics and dispersive wave phenomena. His work integrates techniques from probability theory and dynamical systems to address questions of well-posedness, ergodicity, and uncertainty quantification in PDEs. Key themes include random data Cauchy theory, ergodicity of stochastic PDEs, singular stochastic dispersive equations, and applications to engineering uncertainty analysis. Recent publications highlight his contributions to stochastic hydrodynamic stability, surrogate modeling for nuclear energy systems, and ergodic theory for dispersive equations. His work bridges rigorous PDE analysis with practical applications in turbulence and nuclear reactor design. Mechanical & Aerospace Engineering Teacher of the Year, Utah State University (2019) NSERC Graduate Scholarship (2006) Richards has secured significant research funding including the NRC Faculty Development Grant (2019-2022) as Co-PI and NSF Conference Grant (2017). He teaches with active learning strategies, covering courses from first-year Calculus to advanced research topics.

• Statistical Modeling
• Machine Learning
• Data Analysis
• +2 more

Mark de Rooij is a Professor of Artificial Intelligence and Data Theory at Leiden University's Social and Behavioural Sciences faculty, specializing in the Psychology Methodology and Statistics department. His work combines generalized linear model theory with multidimensional scaling techniques to analyze longitudinal categorical data, emphasizing interpretable classifiers and efficient parameter estimation.

Demba BaView profile

Associate Professor

• Computational Neuroscience
• Model-Based Deep Learning
• Signal Processing
• +3 more

Demba Ba is an Associate Professor of Electrical Engineering and Bioengineering at Harvard University's School of Engineering and Applied Sciences (SEAS). He serves as the Dean of Undergraduate Studies for Bioengineering since 2020 and joined SEAS in 2015 after a postdoctoral fellowship at MIT (2007-2014). His research bridges computational neuroscience and artificial intelligence, focusing on sparse signal representations, interpretable AI, and neural network theory. Fluent in Wolof, Fulani, French, Spanish, English, and Arabic, he also contributes to signal processing, statistical learning, and dynamic systems. PhD in EECS from MIT (2011) MS in EECS from MIT (2006) BS in Electrical Engineering from University of Maryland (2004) His work explores connections between sparse coding and neural networks through publications in top venues like NeurIPS, ICML, and IEEE Transactions. Articles emphasize convolutional dictionary learning, Bayesian frameworks, and applications to neural data analysis. He has received the 2016 Sloan Fellowship in Neuroscience and 2021 Roslyn Abramson Award for undergraduate teaching excellence. 2021: Gaussian process convolutional dictionary learning (Submitted) 2020: Deep residual auto-encoders for dictionary learning 2018: Multitaper time-frequency analysis for neuroscience Demba Ba leads the CRISP research group and holds advisory roles at Harvard. His awards include: 2021 Roslyn Abramson Award 2016 Alfred P. Sloan Foundation Fellow 2010 ICME Best Student Paper Award He teaches courses like ES 201 (Decision Theory) and ES 157 (Biomedical Signal Processing), while maintaining interdisciplinary collaborations in neuroscience, machine learning, and signal processing.

Lan WangView profile

Professor

• high-dimensional statistical learning
• quantile regression
• reinforcement learning
• +3 more

Lan Wang is a Centennial endowed chair professor and Department Chair of the Department of Management Science at the Miami Herbert Business School, University of Miami. She holds secondary appointments as Professor in the Department of Health Management and Policy within the Miami Herbert Business School and as Professor in the Department of Public Health Sciences at the Miller School of Medicine. Dr. Wang earned her Ph.D. in Statistics from Pennsylvania State University and her Bachelor's degree in Applied Mathematics from Tsinghua University, China. Prior to joining the University of Miami, she was a Professor of Statistics at the School of Statistics, University of Minnesota. Dr. Wang's research spans several interrelated areas including high-dimensional statistical learning, quantile regression, reinforcement learning, optimal personalized decision recommendation, survival analysis, and business analytics. Her work is characterized by strong methodological development with applications in business, economics, healthcare, and other domains. She is particularly interested in interdisciplinary collaboration that addresses real-world problems through innovative statistical approaches. Her research has significant implications for precision medicine, where she develops methods to identify optimal individualized decision rules to improve patient outcomes. Dr. Wang's recent publications demonstrate a consistent focus on advancing statistical methodology for high-dimensional data analysis and personalized decision making. Her work bridges theoretical statistics with practical applications, particularly in healthcare analytics. She has made significant contributions to quantile regression theory, high-dimensional regression techniques, optimal treatment rules, and statistical learning frameworks. A notable theme across her publications is the development of robust methods that maintain performance even with heavy-tailed error distributions. Fellow of the American Statistical Association Fellow of the Institute of Mathematical Statistics Member of the International Statistical Institute Dr. Wang has served as Co-Editor for Annals of Statistics (2022-2024) and as associate editor for several leading statistical journals including Journal of the American Statistical Association, Annals of Statistics, Journal of the Royal Statistics Society, and Biometrics. Her editorial leadership reflects her standing in the statistical community and her commitment to advancing methodological research. While specific grant details aren't provided, her extensive publication record in top-tier journals suggests substantial research funding supporting her work.

• Statistical Experimental Design
• Screening Designs
• Supersaturated Designs
• +4 more

Anthony Cossari is a University Researcher in Statistics (SECS-S/01) at the Department of Economics, Statistics, and Finance "Giovanni Anania" (DESF) at the University of Calabria, where he has been employed since September 1, 2003. He holds a Laurea in Scienze Statistiche ed Attuariali from the University of Calabria (1995) with highest honors. Cossari is a member of the Italian Statistical Society (SIS) and the European Network for Business and Industrial Statistics (ENBIS). His research interests center on statistical experimental design methodologies: Screening designs for identifying significant factors in preliminary experimental phases Supersaturated designs for studying numerous factors with limited experimental runs Follow-up designs to enhance initial experimental analyses Robust designs for minimizing variability from environmental factors Cossari maintains active research collaborations across disciplines, particularly with medical researchers from the Catholic University of the Sacred Heart in Rome and mechanical engineering researchers from the University of Calabria. His publication record shows a transition from purely statistical experimental design research to increasingly collaborative medical research, especially in sepsis prognosis, alcoholic cardiomyopathy, and dental health in patients with alcohol use disorders. Recent publications (2021-2025) demonstrate strong interdisciplinary work while maintaining his core statistical expertise. His academic service includes membership on the Departmental Board (2004-2006) and the Scientific-Technical Committee of the Interdepartmental Library of Economic and Social Sciences "E. Tarantelli" (2007-present). He has served as thesis advisor for numerous undergraduate and graduate students across various statistical topics and was a member of the Doctoral Committee for the PhD program in "Economic History, Demography, Institutions and Society in Mediterranean Countries" (2007-2009).

Jörg BreitungView profile

Professor

• Econometrics
• Statistics
• Panel Data Analysis
• +3 more

Jörg Breitung is a Professor of Econometrics and Statistics at the Institute of Econometrics and Statistics within the Faculty of Management, Economics and Social Sciences (WiSo Faculty) at the University of Cologne since 2014. He also serves as a Research Professor of the German Bundesbank in Frankfurt since 2002. Research Focus: Panel Data Analysis Time Series Analysis Forecasting Financial Econometrics Scientific Contributions: Developed advanced GMM estimators for spatial regression models Innovative approaches for assessing causality in frequency domains Created robust tests for slope homogeneity in panel data Pioneered methods for serial correlation testing in fixed effects models Contributed to nonlinear panel data modeling and bootstrap techniques Honors and Editorial Roles: Associate Editor of International Journal of Forecasting (2019-) Associate Editor of Journal of Business and Economic Statistics (2017-) Associate Editor of Econometric Reviews (2014-) Contributed to leading journals like Econometrica and Journal of Econometrics

• Resampling methods
• Model selection
• Non-parametric estimation
• +2 more

Guillaume MAILLARD is a permanent member of CREST (Center for Research in Economics and Statistics) at ENSAI (National School of Statistics and Economic Administration), where he joined in September 2024. His academic career includes a PhD from Université Paris-Saclay (2020) and three years as a post-doctoral researcher at the University of Luxembourg. Research Interests: Resampling and model selection techniques Non-parametric and robust estimation methods Statistical learning applications Academic Affiliation: CREST, a renowned research center in economics and statistics, is the institutional framework guiding his research activities.

• Time Series Analysis
• Applied Statistics
• Biostatistics
• +4 more

Ying Zhang is a Professor in the Department of Mathematics and Statistics at Acadia University, maintaining an office in Huggins Science Hall, Room 151. She earned her BSc from Shandong Normal University and advanced degrees (MA, MSc, PhD) from Western University, complemented by P.Stat. certification (Certificate #78) from the Statistical Society of Canada. Her educational background includes: BSc from Shandong Normal University MA, MSc, PhD from Western University Professor Zhang's research centers on Time Series Analysis and Applied Statistics , extending to Statistical Computing, Symbolic Algebra Computing, and Statistical Consulting in Biostatistics, Survey Design, and Research Methodology. Her work demonstrates significant applications in environmental science (water resources trend analysis), health sciences (drug safety and utilization studies), and ecological modeling (wildlife population dynamics), with methodological innovations in nonparametric testing and hierarchical modeling. Analysis of her 2013-2018 publications reveals a dominant focus on developing novel time series methodologies for environmental and health contexts, particularly seasonal trend detection, medication utilization patterns, and ecological data analysis. Her work consistently bridges theoretical statistics with practical applications across disciplines. She actively contributes through the Statistical Consulting Centre and the CANSSI Maritime Statistical and Health Sciences Collaborating Centre , holding P.Stat. designation from the Statistical Society of Canada. While her collaborative publications indicate interdisciplinary engagement, specific details of grant funding and student advising are not documented in available sources.

Benjamin KedemView profile

Professor

• Time Series Analysis
• Spatial Statistics
• Spectral Analysis
• +5 more

Benjamin Kedem is a Professor in the Department of Mathematics at the University of Maryland, College Park, with affiliations at the Institute for Systems Research (ISR). His academic career spans several decades with significant contributions to time series analysis, spatial statistics, and statistical methodology. Dr. Kedem's research focuses on time series analysis, space-time statistical problems, and combination of information from multiple sources. His work includes significant contributions to higher order crossings (HOC), contraction mapping methods in spectral analysis, Rice formula applications, threshold methods for rainfall estimation, partial likelihood approaches, Bayesian-transformed-Gaussian spatial prediction, and statistical data fusion. His research has practical applications in environmental statistics, meteorology, and public health. His recent publications demonstrate a continued focus on semiparametric methods, statistical data fusion, and computational approaches to time series and spatial analysis. The 15 most recent articles span from 2017 back to 1994, showing both contemporary relevance and foundational contributions to the field of statistics. Dr. Kedem has directed 15 PhD dissertations, with students completing between 1983 and 2007, indicating his long-standing commitment to mentoring the next generation of statisticians. His former students include George Reed, Donald E.K. Martin, Silvia R.C. Lopes, Haralabos Pavlopoulos, James Troendle, Ta-Hsin Li, John Barnett, Konstantinos Fokianos, Victor De Oliveira, Neal Jeffries, Boris Kozintsev, Richard Gagnon, Haiming Guo, Guanhua Lu, and Shihua Wen. He has authored or co-authored several books including 'Regression Models for Time Series Analysis' (2002), 'Time Series Analysis by Higher Order Crossings' (1994), and 'Statistical Data Fusion' (2017). His teaching portfolio includes graduate courses such as STAT 730 (Time Series Analysis), STAT 740 (Linear Models I), and STAT 741 (Linear Models II).