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• Linguistic creativity in communication
• Citizen Science Approaches in Psycholinguistics
• Resolution of compositional conflicts in Coercion
• +7 more

Prof. Dr. Oliver Bott is a Professor for Experimental Pragmatics at Bielefeld University's Faculty of Linguistics and Literary Studies. He leads the Experimental Pragmatics Research Group and holds multiple institutional roles including membership on the Faculty Conference, the Habilitation Committee, and the Board of the Linguistics Department. As Principal Investigator, he directs projects B01 and Ö of the Collaborative Research Center 1646 "Linguistic Creativity in Communication". Dr. Bott obtained his Habilitation in General Linguistics and German Linguistics in Summer 2017, followed by his appointment as Professor for Experimental Pragmatics at Bielefeld University in Winter Semester 2019/20. His academic journey began with a Master of Arts in German Linguistics from the University of Tübingen in 2000, and he later earned a Psychology Diploma from the same institution in 2004. His doctoral work in General Linguistics was completed in 2011. Dr. Bott's research focuses on the experimental investigation of linguistic meaning processing. His current work explores linguistic creativity in communication, citizen science approaches in psycholinguistics, and the resolution of compositional conflicts in coercion. He investigates how semantic and pragmatic expectations operate in discourse, particularly regarding implicit causality, and examines the interpretation of quantifying expressions and event interpretation in language. His methodology emphasizes psycholinguistic approaches to understand the cognitive mechanisms underlying language comprehension. His recent publications reveal a consistent focus on experimental approaches to semantic and pragmatic phenomena, with particular attention to implicit causality, coercion mechanisms, quantifier interpretation, and discourse processing. These works demonstrate his commitment to bridging theoretical linguistics with empirical cognitive science methods. Among his notable scientific contributions are his work on cataphoric resolution of projective content with occasion verbs, abductive inferences in causal discourse, and the processing of non-culminating accomplishments across languages. His research on neglect-zero effects in quantifier interpretation has also made significant contributions to our understanding of semantic processing. Dr. Bott actively supervises research projects and doctoral students through his role as Chairman of the Doctoral Committee. He has secured significant funding through the German Research Foundation for his work on coercion as a creative mechanism in compositional interpretation (SFB 1646, Subproject B01) and for public relations initiatives related to linguistic creativity.

Kurt HornikView profile

Professor

• Statistical Data Processing
• Statistical Learning
• Machine Learning
• +5 more

Kurt Hornik is a Professor at the Vienna University of Economics and Business (WU) , where he heads the Institute for Statistics and Mathematics and the Research Institute for Computationally Intensive Methods . He is renowned for his co-development of the R programming language, a cornerstone in statistical computing with over 15,000 extension packages. His research spans statistical data processing, machine learning, and quantitative risk management, focusing on combining ordinal ratings (e.g., credit scores) into consensus-based models. Education: Technical Mathematics (PhD, 1987, Vienna University of Technology) Postdoctoral work in statistics and mathematics Research Interests: Hornik's work addresses challenges in statistical modeling of ordinal preferences, particularly in creditworthiness assessment. He developed composite likelihood methods to improve calibration of credit rating systems, which underpin key Eurosystem monetary policies. His recent publications explore hyperspherical variational autoencoders, Watson distributions, and Kummer's function bounds, reflecting his contributions to computational statistics and high-dimensional data analysis. Scientific Awards: Golden Decoration of Merit for Services to the Republic of Austria (2007) Contributions: Hornik's research has produced widely used R packages like colors , ROI , and mvord , advancing statistical software infrastructure. He collaborates globally, with publications in journals such as Journal of Statistical Software and Annals of Applied Statistics .

• Global Optimization
• Interval Arithmetic
• Mathematical Modeling
• +5 more

Hermann Schichl is an Associate Professor at the University of Vienna , affiliated with the Faculty of Mathematics and Department of Mathematics. His work spans mathematical modeling, global optimization, and numerical analysis.

Francis HuiView profile

Associate Professor

• Ecological Statistics
• Longitudinal and Spatio-Temporal Data Analysis
• Mixed Effects Models
• +5 more

Francis Hui is an Associate Professor in Statistics at the Research School of Finance, Actuarial Studies and Statistics (RSFAS), The Australian National University (ANU), specializing in ecological statistics, longitudinal data analysis, and statistical computing. His work bridges methodological advancements with practical applications in environmental science, actuarial studies, and multivariate data analysis. Education: PhD in Statistics from the University of New South Wales (2015) BSc/BA (First Class Honours, University Medal) from the University of New South Wales (2012) Research Interests: Approximate likelihood methods for estimation and inference Ecological statistics and joint species distribution modeling Dimension reduction and model selection for complex data Variational approximations and semiparametric regression Spatio-temporal and multivariate longitudinal data analysis Publication Trends: Francis's recent work focuses on robust statistical methods for ecological modeling, including generalized linear mixed models, composite likelihood techniques, and dimension reduction. His research also explores fair actuarial models for retirement outcomes and computational efficiency in handling large-scale environmental datasets. Projects: Current projects include developing modern statistical methods for clustering community ecology data, enhancing predictive accuracy in spatio-temporal models, and creating scalable tools for multivariate abundance data analysis. Software: He has contributed to R packages such as assam , COQUE , HPGEE , and gllvm , which implement advanced statistical techniques for ecological and longitudinal data.

Andreas FutschikView profile

Professor

• Applied Statistics
• Biostatistics
• Statistical Inference in High Dimensional Settings
• +2 more

Univ.-Prof. Dr. Andreas Futschik serves as Head of the Department and Head of the Division of Data Analysis and Econometrics at the Institute of Applied Statistics, Johannes Kepler University Linz, Austria. His academic leadership spans both administrative roles and active research contributions across multiple statistical disciplines. His institutional affiliations include the Kurt Rothschild School of Economics and Statistics (RoSES) where he maintains a prominent research and teaching presence. Professor Futschik's research focuses on Applied Statistics, Biostatistics, and Statistical Inference in High Dimensional Settings , with particular expertise in genomic statistics and population genetics. His work bridges theoretical statistical methodology with practical applications in biological and agricultural sciences. He has developed innovative approaches for analyzing complex genetic data, including methods for genomic selection, haplotype estimation, and detection of natural selection in population genetic data. His research demonstrates a consistent pattern of applying advanced statistical techniques to solve real-world problems in genomics and veterinary science. The analysis of his recent publications reveals a strong emphasis on methodological innovation in statistical genetics , particularly in genomic prediction models and population genetic inference. His work frequently employs Bayesian frameworks, time series analysis, and high-dimensional statistical techniques to address challenges in genomic data analysis. The interdisciplinary nature of his research connects statistics with evolutionary biology, veterinary medicine, and agricultural science, demonstrating the versatility of modern statistical approaches. Professor Futschik maintains active research collaborations through multiple ongoing projects including 'Statistische Modellierung zur Kartellschadensberechnung' (2023-2027), 'Statistical Literacy' (2019-2025), and 'Statistical Inference in Complex Situations' (2014-2025). His research group contributes significantly to both theoretical statistical development and practical applications across diverse scientific domains. He regularly presents at international conferences, with recent talks covering phase-type distributions in population genetics and statistical inference for time series allele frequency data.

Ronald de VlamingView profile

Assistant Professor

• Statistical Genetics
• Behavioral Genetics
• Econometrics
• +3 more

Ronald de Vlaming is an Assistant Professor at the Department of Econometrics and Data Science within the School of Business and Economics at Vrije Universiteit Amsterdam. He also holds an affiliation with Amsterdam Neuroscience – Complex Trait Genetics. His research focuses on applying statistical methods, matrix algebra, and numerical techniques to study the genetic architecture of social-scientific outcomes, with publications in journals like Nature Genetics , Nature Human Behaviour , and Molecular Psychiatry . Education: PhD in Economics (Linear Mixed Models in Statistical Genetics) from Erasmus University Rotterdam (2017). Research Interests: Genetic epidemiology, behavioral genetics, computational methods in genomics, and the application of econometric techniques to genetic data. His work bridges statistical genetics and social science, addressing topics like socio-economic health gradients and genetic influences on behavior. Teaching: Courses include Database Fundamentals, Data Structures and Algorithms, Differential Privacy, and Quantitative Research Methods I. Labs/Teams: Collaborates with Amsterdam Neuroscience and contributes to interdisciplinary genomic studies, such as the 2020 dataset on diet composition and health behavior.

David FirthView profile

Professor

• Statistical Theory
• Statistical Methodology
• Machine Learning
• +4 more

David Firth is a Professor of Statistics at the University of Warwick, having joined in 2003 from the University of Oxford, where he held the position of Professor of Social Statistics. His career includes prior roles at Imperial College London, the University of Texas at Austin, and the University of Southampton. Focused on statistical theory and applications, his research spans generalized nonlinear models, paired comparisons, compositional data analysis, and methodological contributions to fields like public policy and social science. He is a former Editor of the Journal of the Royal Statistical Society (Series B) and has served as an Associate Editor for prominent journals including Biometrika and Biostatistics . Recognized for his contributions, he became a Fellow of the British Academy in 2008 and received the Guy Medal in Silver (2012). He currently holds a Leverhulme Emeritus Fellowship (2023–2025). Research Interests: Firth’s work emphasizes statistical methodology, with notable contributions to paired comparison models (e.g., Bradley-Terry), compositional data analysis, and bias reduction techniques. His applied research includes election forecasting (e.g., BBC/ITV exit polling), development of deprivation indices (UK’s Index of Multiple Deprivation), and collaborations in biology and ecology. Recent projects explore computational methods for complex statistical models and interdisciplinary applications. Publications: His 15 most recent articles reflect methodological advancements in regression, paired comparisons, and computational statistics. Key themes include ridge regression for paired comparisons, dynamic Bradley-Terry models in sports, and compositional data analysis frameworks. Awards: In addition to his British Academy Fellowship and Guy Medal, Firth has been honored for his editorial work and contributions to statistical education. His Leverhulme Fellowship supports ongoing research in statistical methodology. Advising & Grants: While specific student names are not listed, Firth has advised numerous PhD students in statistical methodology. His grants include funding for collaborative projects on generalized nonlinear models and paired comparison systems. He is actively involved in Warwick’s statistical research community, contributing to conferences and workshops. Labs/Teams: Collaborates with researchers like Cristiano Varin (paired comparisons), Ioannis Kosmidis (bias reduction), and Heather Turner (R package development). His work bridges academia and policy, exemplified by his election forecasting and deprivation index projects.

• Cosmology
• Large Scale Structure
• Inflation
• +5 more

Guadalupe Cañas Herrera is a Research Fellow at the European Space Agency (ESA) , specializing in Theoretical Cosmology . She is a key member of the Euclid Consortium and a software developer for the CLOE (Cosmology Likelihood for Observables in Euclid) project, which is part of ESA's official data analysis pipeline for cosmological constraints. Her research focuses on statistical analysis of beyond-Standard cosmological models to study dark energy, dark matter, and primordial density perturbations, using data such as the Cosmic Microwave Background, weak lensing, and galaxy clustering. She also explores forecasting techniques for novel observables like gravitational waves and machine learning applications in cosmological inference. Collaborations : Euclid Consortium, Osservatorio Astronomico di Roma, University of Amsterdam, Leiden Observatory, Lorentz Institute for Theoretical Physics, University of Cantabria. Contact : guadalupe.canasherrera@esa.int Her work bridges theoretical cosmology with advanced statistical methods and software development, aiming to unravel the universe's origins and evolution through precision data analysis.

Moreno BevilacquaView profile

Associate Professor

• Spatial Statistics
• Covariance Modeling
• Geostatistics
• +3 more

Moreno Bevilacqua is an Associate Professor in the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice. His research focuses on spatial statistics, covariance modeling, and geostatistical methods, with applications in environmental and multivariate analysis. He has contributed to advancing covariance functions, composite likelihood approaches, and spatial data modeling techniques. Recent publications emphasize statistical methodologies for spatial and spatiotemporal data, including extensions of the Matérn model, composite likelihood for massive datasets, and copula-based approaches for bounded support data. His work bridges theoretical advancements and practical applications in environmental science and statistical computing. No scientific awards or grants are explicitly mentioned in the provided texts. Teaching activities include courses in statistics for undergraduate programs in Business Economics, covering foundational and advanced statistical methods. Current office location: Assegnisti A1 office (Zeta A building, 1st floor), Campus via Torino.

• Statistics
• Probability Theory
• Predictive Modeling
• +4 more

Federica GIUMMOLE' is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Informatics and Statistics. Her research focuses on statistical prediction, probabilistic modeling, and environmental applications. She teaches courses such as Applied Statistics for Biology and Biotechnology, and Probability and Statistics across multiple programs including Engineering and Digital Management. Role: Associate Professor Department: Department of Environmental Sciences, Informatics and Statistics University: Ca' Foscari University of Venice Her research emphasizes predictive modeling, with work on statistical inference, Bayesian methods, and environmental data analysis. Recent projects include calibrating predictive distributions for meteorological forecasting and developing optimal prediction techniques for quantiles and probabilities. Publications span peer-reviewed journals like Statistical Papers and Environmental and Ecological Statistics. She has contributed to methodological advancements in scoring rules and composite likelihoods, supported by grants such as the MIUR-funded project on complex dependency models.

• Robust Statistics
• Bias Reduction Methods
• Estimation Theory
• +5 more

Nicola Lunardon is an Associate Professor of Statistics at Ca' Foscari University of Venice. He holds a PhD in Statistics from the University of Padua and has held academic positions at the University of Milan-Bicocca, University of Trieste, and others. His research focuses on robust statistics, bias reduction methods, pseudo-likelihoods, and asymptotic theory. He has contributed to advancements in estimation functions and computational statistical methodologies. Current Affiliation: Department of Environmental Sciences, Computer Science and Statistics Previous Roles: Associate Professor at University of Milan-Bicocca (2019–2023), Research Fellowships at Padua and Trieste Universities (2012–2016) Research Interests: Lunardon's work emphasizes improving inferential procedures through robust statistical techniques. He has developed methods to address bias in estimating functions, tackle incidental parameters, and enhance computational efficiency. His Bayesian non-parametric approaches have been applied to molecular systems analysis. Publications span methodological contributions in top journals like Biometrika , Journal of the Royal Statistical Society , and Biometrics . Themes include global sensitivity analysis, longitudinal data modeling, and small-sample GEE estimation. Notable Software: Co-developed the ROSE package for imbalanced learning in R

Ege Holger RubakView profile

Associate Professor

• Spatial Statistics
• Point Processes
• Determinantal Point Processes
• +4 more

Ege Holger Rubak is an Associate Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science. His research focuses on spatial statistics, point processes, and random matrix theory with applications in computational methods and data analysis. He actively contributes to open-source statistical software development and serves as Principal Investigator for the Novo Nordisk Foundation-funded project AI - Aalborg Intelligence . Academic Rank: Associate Professor Institution: Aalborg University Department: Mathematical Sciences Research Interests: Spatial statistics, determinantal point processes, composite likelihood Email: rubak@math.aau.dk Research Trends: His recent publications emphasize spatial point process diagnostics, simulation methods for determinantal processes, and statistical modeling of complex systems. The 2025 dataset on residential household occupancy reflects interdisciplinary applications in smart energy systems. Collaboration: He collaborates extensively with researchers like Adrian Baddeley, Jesper Møller, and Rasmus Waagepetersen across institutions and disciplines.

• Fatigue
• Mechanical characterization
• Structural dynamics
• +3 more

Massimo Rossetto is a Full Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS), Polytechnic University of Turin, where he has held various leadership roles including Director (2015-2023) and Coordinator of the College of Mechanical, Aerospace, Automotive and Production Engineering. He is affiliated with the Interdepartmental Center J-Tech@PoliTO and contributes to research in fatigue and mechanical characterization of materials. Research Interests: Focus on Very High Cycle Fatigue (VHCF) behavior of materials, particularly in additive manufacturing contexts, addressing defect-life relationships and structural integrity. Teaching: Currently teaches 'Tools for product innovation' (MSc in Industrial Production and Technological Innovation Engineering) and 'Design of experiments and reliability' (MSc in Mechanical Engineering). Research Projects: Led projects like DynLab4JMat (2018-2021) on structural integrity, NEXT MIRS (2008-2010) for tire production, and composite materials studies (2007-2010). Publications: Over 140 papers on fatigue behavior, structural dynamics, and materials in additive manufacturing, including recent works on statistical fatigue models and defect analysis. Collaborations: Partnered with institutions like the Italian Space Agency (2016-2021) in aerospace engineering and space biomedicine.

• Bayesian Statistics
• Biostatistics
• Epidemiology
• +5 more

Stefano Cabras is a Full Professor in the Department of Statistics at University Carlos III de Madrid (UC3M), where he serves as Director of the Flores de Lemus Institute and Deputy Director of the Master's Degree in Statistics for Data Science. His office is located at 10.1.10 - Campomanes (Getafe). Professor Cabras maintains broad interdisciplinary research interests spanning: Bayesian statistics and computational methods Biostatistical applications in nutrition, epidemiology, and medicine Causal inference and policy impact evaluation Machine learning applications in transportation and sports science Statistical modeling of complex biological systems His publication trends show strong emphasis on Bayesian methodologies applied to healthcare, public policy, and urban systems. Recent work demonstrates increasing integration of deep learning with traditional statistical approaches for problems in causal inference, pandemic forecasting, and transportation optimization. Professor Cabras leads several major research initiatives: Principal researcher for 'Statistical modelling for smart cities' (Beijing Metro Group, 2020-2023) Director of 'Contrato marco para servicios de consultoría estadística' (KROLL Advisory, 2022-2024) Co-researcher for 'Predicción de edad biológica mediante IA' (Fundación Mutualidad Abogacía, 2020-2021) He has supervised graduate research including theses on Bayesian metro occupancy modeling and maintains active collaborations through the Flores de Lemus Institute. His research has been applied in policy analysis, healthcare diagnostics, and urban planning internationally.

Aurelien FraisseView profile

Lecturer

• Cosmology
• Astrophysics
• Gravitational Lensing
• +3 more

Aurelien Fraisse is a Research Scholar and Lecturer at Princeton University's Department of Physics, contributing to cosmology and astrophysics research. His work focuses on cosmic microwave background (CMB) observations using balloon-borne instruments like SPIDER and SuperBIT, gravitational lensing studies, and instrumental developments for high-altitude platforms. Office: Jadwin Hall 221 Email: afraisse@princeton.edu Key research interests include CMB polarimetry, weak gravitational lensing, galactic dust composition, and the design of stratospheric observatories. Fraisse has led contributions to the SPIDER and CMB-S4 projects, advancing instrumentation for detecting primordial gravitational waves and characterizing cosmic foregrounds. His recent work emphasizes improving data retrieval from high-altitude balloons and optimizing observational strategies for suborbital telescopes. Publications span technical advancements (e.g., TES arrays, in-flight monitoring) and cosmological analyses using Planck and SPIDER datasets. Research collaborations include the SuperBIT telescope (commissioned in 2019), enabling diffraction-limited visible-to-near-UV imaging from stratospheric balloons, and refining CMB power spectrum estimation methods for future surveys.