Spatial Resampling Professors and Supervisors

Ingrid VAN KEILEGOMView profile

Professor

University of LouvainBelgium

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.

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Paul MarriottView profile

Professor

University of WaterlooCanada

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

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Jörg BreitungView profile

Professor

University of CologneGermany

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

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Ying ZhangView profile

Professor

Acadia UniversityCanada

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.

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Benjamin KedemView profile

Professor

University of Maryland, College ParkUnited States

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).

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Gaurav TiwariView profile

Associate Professor

Indian Institute of Technology Kanpur (IITK)India

Dr. Gaurav Tiwari is an Associate Professor in the Department of Civil Engineering at the Indian Institute of Technology Kanpur . His research focuses on Rock Mechanics , with expertise in Geotechnical Engineering and Rock Engineering . Education : PhD in Geotechnical Engineering (Rock Mechanics) from IISc Bangalore, M.Tech in Geotechnical Engineering from IIT Roorkee, and B.E. in Civil Engineering from M.B.M. Govt. Engineering College, Jodhpur. His work emphasizes Experimental Rock Mechanics , Probabilistic Rock Engineering , and Reliability Analysis for rock structures with limited data. Key applications include rock slope stability , tunnel support design , and dynamic behavior of jointed rocks . He has developed hybrid reliability frameworks and sensitivity analysis methods for spatially varying rock properties and data-deficient scenarios , as evidenced by his recent articles in journals like International Journal of Rock Mechanics and Mining Sciences and Rock Mechanics and Rock Engineering . Dr. Tiwari’s students have contributed to high strain rate rock analysis , grouted flaw dynamics , and probabilistic modeling . His scientific awards include the IGS-Leonard’s Best PhD Thesis Award (2018-2019), ISSMGE scholarship (2017), and multiple IGS-Roorkee YGE Awards for Best Papers. Laboratory Facilities : Computational tools (FLAC-2D/3D, UDEC, Rocscience Academic Bundle) and experimental setups (high-rate direct shear testing, 3D-printed synthetic samples, rock durability tests).

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Ryan RenslowView profile

Associate Professor

Paul Drude Institute for Solid State ElectronicsGermany

Ryan Renslow serves as a Chemical Engineer at Pacific Northwest National Laboratory (PNNL) and holds a Research Associate Professor position at Washington State University's Gene and Linda Voiland School of Chemical Engineering and Bioengineering. His interdisciplinary work bridges computational modeling, advanced imaging, and experimental biology to address complex challenges in metabolomics and microbial systems. Education BS in Chemical Engineering, Washington State University MS in Chemical Engineering, Washington State University PhD in Chemical Engineering, Washington State University Linus Pauling Distinguished Postdoctoral Fellowship, Pacific Northwest National Laboratory Renslow's research centers on identifying novel metabolites in complex biological samples, deciphering microbial community structure-function relationships, and understanding emergent properties in multispecies systems. He employs computational mathematics, machine learning, and high-resolution imaging techniques including mass spectrometry and nuclear magnetic resonance. His work spans diverse applications from human health diagnostics and bioenergy production to ecological monitoring and national defense solutions, with particular emphasis on biofilm dynamics and metabolite characterization in challenging environments. Analysis of his recent publications reveals a strong trend toward integrating ion mobility spectrometry with computational modeling for metabolite identification, developing in silico libraries for small molecule annotation, and applying machine vision to biological systems. His work consistently demonstrates cross-cutting applications across energy, environmental science, and biomedical research through sophisticated data analysis frameworks. Scientific Awards: No awards listed in available documentation. Renslow's collaborative research program involves extensive partnerships across PNNL's Environmental Molecular Sciences Laboratory and Washington State University. His work receives institutional support through DOE-funded initiatives focused on chemical biology and exposure science, with emphasis on developing advanced analytical capabilities for complex sample analysis. While specific grant details aren't provided, his publication record indicates consistent funding for interdisciplinary projects combining experimental and computational approaches. At PNNL, Renslow contributes to the Chemical Biology and Exposure Science group within the Biological Sciences division. He leverages specialized facilities including high-field mass spectrometers, nuclear magnetic resonance microimaging systems, and biofilm reactors to investigate microbial community dynamics. His research team integrates expertise from chemical engineering, microbiology, and computational science to develop novel approaches for characterizing complex biological systems at multiple scales.

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Johannes KrebsView profile

Professor

Catholic University of Eichstätt-IngolstadtGermany

Prof. Dr. Johannes Krebs holds the Chair of Mathematics - Statistics at the Faculty of Mathematics and Geography, Catholic University of Eichstätt-Ingolstadt since 2021. His academic service includes membership on the editorial board of Statistics , the tenure track board, examination committees for B.Sc. Mathematics and Data Science programs, and the faculty council. His research focuses on topological data analysis, persistent homology, and spatial statistics with applications in stochastic geometry. Funded by Deutsche Forschungsgemeinschaft (KR 4977/2-1 and KR 4977/1-1), his work bridges theoretical statistics with computational topology to develop novel methods for analyzing complex data structures. His methodological contributions span wavelet analysis, functional data, and bootstrap techniques for dependent data. Analysis of his recent publications reveals strong emphasis on topological inference (persistent homology, Betti numbers), spatial statistics, and wavelet methods across multiple high-impact journals including Bernoulli , Annals of Statistics , and Stochastic Processes and their Applications . His work demonstrates consistent innovation in applying topological concepts to statistical problems with theoretical guarantees. Academic service highlights include: Editorial Board Member, Statistics journal Member of Tenure Track Board Examination Committee for B.Sc. Mathematics & Data Science Faculty Council Member His research program is supported by significant DFG funding for projects on Topological Data Analysis (2021-2023) and Dynamic Objects on Random Fields (2017-2020), reflecting sustained contribution to advancing statistical methodology through topological frameworks. He maintains active collaborations with researchers at UC Davis, KU Leuven, and Heidelberg University.

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