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Boris BerangerView profile

Senior Lecturer

• Extreme Value Theory
• Environmental Extremes
• Spatial Statistics
• +3 more

Boris Beranger is a Senior Lecturer in Statistics and Data Science at the School of Mathematics and Statistics, UNSW Sydney . He is also a member of the UNSW Data Science Hub (uDASH) and previously served as an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) . His research spans theoretical and applied statistics, focusing on Extreme Value Theory (environmental, financial, and insurance applications) and Symbolic Data Analysis (complex/non-standard data structures). Education: PhD in Statistics (Université Pierre and Marie Curie & UNSW, 2016), MSc in Mathematics (Université Pierre and Marie Curie, 2011) Research Trends are evident in: High-dimensional extremal dependence modeling (ExtremalDep package) Spatial extremes and max-stable processes Symbolic/histogram/interval-valued data analysis Composite likelihood and aggregated data methods Tail density estimation via kernel methods Scientific Awards & Grants include: J.B. Douglas Award for Postgraduate Excellence (2014) Multiple ARC ACEMS Research Support Schemes Discovery Project DP220103269 ($405,000) for modeling real-world extremes Supervision covers PhD, Masters, and Honours students in areas like Symbolic Data Analysis, Spatial Extremes, and Statistical Computing. He also co-organized workshops and served as Vice-President (2025-26) of the Statistical Society of Australia's NSW Branch.

Manuela Karola ZucknickView profile

Professor

• Biostatistics
• Cancer Research
• Risk Prediction
• +4 more

Manuela Karola Zucknick is a Professor in the Department of Biostatistics at the University of Oslo , where she leads statistical learning research for translational and clinical cancer applications. Her work focuses on integrating multi-omics data for personalized cancer therapies, predicting drug responses, and modeling prognosis. Director of Oslo Centre for Biostatistics and Epidemiology (2023–present) Professor (2022–present) and Associate Professor (2015–2022) at University of Oslo Research Interests span high-dimensional statistical modeling, Bayesian methods for heterogeneous data integration, regularization techniques, and applications in pharmacogenomics. She develops tools for drug combination screens, survival modeling, and risk prediction incorporating prior biological knowledge. Key domains: Biostatistics, Integrative Genomics, Precision Medicine Methodological focus: Bayesian structured variable selection, Penalized Regression Publications demonstrate expertise in pan-cancer transcriptomics, proteomics for pregnancy complications, and machine learning for DNA methylation analysis. Recent work includes Tutorial on Survival Modeling (2024) and Dose-Response Prediction (2023) applied to pharmacogenomic datasets. Collaborations span clinical trials in colorectal cancer nutrition, prostate cancer screening for Lynch syndrome patients, and chronic pain research using molecular profiling.

Ioulia PapageorgiouView profile

Associate Professor

• Sampling Theory
• Model-Based Clustering
• Mixture Models
• +3 more

Dr. Ioulia Papageorgiou is an Associate Professor in the Department of Statistics at the Athens University of Economics and Business (AUEB), School of Information Sciences and Technology. She holds a B.Sc. in Mathematics (2.1) and a Ph.D. in Statistics, both from the University of Ioannina. Her career spans since 2001, with prior roles as a Post-Doctoral Research Fellow at AUEB and Nottingham Trent University, and Lecturer at AUEB (2002-2007). Education B.Sc. in Mathematics (2.1), University of Ioannina Ph.D. in Statistics, University of Ioannina Employment History Associate Professor, AUEB Department of Statistics (2007-present) Lecturer, AUEB Department of Statistics (2002-2007) Post-Doctoral Research Fellow, AUEB (2001-2002) Post-Doctoral Research Fellow, Nottingham Trent University (1998-2001) Her research focuses on Sampling Theory, Model-Based Clustering, Mixture Models, and their Applications to Archaeometry. She has developed statistical methodologies for ceramic artifact provenance studies, explored hierarchical cluster analysis for overpainted artwork identification, and addressed optimal sampling designs for autocorrelated populations. Her work bridges statistical innovation with archaeological and medical applications. Analysis of her recent publications reveals a strong emphasis on statistical modeling in archaeology (e.g., ceramic provenance via p-XRF), computational methods for multivariate data, and pharmacoeconomic assessments for chronic diseases. Keywords across her work include Archaeometry, Cluster Analysis, Multivariate Modeling, and Survey Sampling.

Quan VuView profile

Research Fellow

• Spatial Statistics
• Spatio-Temporal Data Analysis
• Environmental and Ecological Modeling
• +3 more

Quan Vu is a Postdoctoral Fellow in Statistics at the Australian National University (ANU), specifically within the Research School of Finance, Actuarial Studies & Statistics. His research focuses on advancing statistical methodologies for spatial and spatio-temporal data, with applications in environmental and ecological contexts. Key areas of interest include Gaussian processes, basis function models, and neural networks for regression, prediction, and uncertainty quantification. His work addresses challenges in modeling complex dependencies in data, such as clustered, spatial, and spatio-temporal structures, and translates these methods into real-world solutions through collaborations with domain experts. Recent research trends emphasize nonstationary covariance modeling, compositional warpings for large datasets, and gradient-enhanced surrogate models for intractable likelihoods. Quan has contributed to high-impact journals such as Methods in Ecology and Evolution and the Journal of the American Statistical Association , with notable applications in species distribution modeling and environmental data analysis. He is actively involved in supervising research students and remains engaged in the statistical community through discussions on methodological advancements.

VASDEKIS VASILEIOSView profile

Professor

• Longitudinal Data Analysis
• Latent Variable Models
• Composite Likelihood Methods
• +7 more

VASDEKIS VASILEIOS is a Professor of Statistics at the Department of Statistics, School of Information Sciences and Technology, Athens University of Economics and Business (AUEB), where he has been a faculty member since 1999. He previously served as Assistant Professor (2003–2009), Associate Professor (2009–2016), and Lecturer (1999–2003). He held administrative roles including Head of Department (2016–2020) and Vice-Chancellor for Academic Affairs and Personnel (2020–2024). Education: D.Phil. in Statistics, University of Oxford, UK (1989–1993) M.Sc. in Applied Statistics, University of Oxford, UK (1988–1989) B.A. in Mathematics, University of Athens, Greece (1983–1988) His research focuses on longitudinal data analysis , latent variable models , and composite likelihood methods , with applications in clinical trials, psychology, and public health. He has developed statistical methodologies for correlated binary data, multivariate ordinal responses, and random effects models. His work bridges theoretical statistics with practical applications in medicine and social sciences. The recent publications show a strong trend in psychometrics , model diagnostics , and longitudinal modeling , particularly using composite likelihood and goodness-of-fit techniques. His interdisciplinary collaborations extend to developmental psychology and nutrition studies. Scientific Awards: Fellow of the Royal Statistical Society (UK) IKY Scholarship for Doctoral Research (1989–1993) He has supervised multiple doctoral students, including Evgenia Tzompanaki, Antonia Korre, and Ioanna Athanassopoulou, and postgraduate researcher Kostas Florios. He has secured research funding through projects such as PYTHAGORAS, ARISTEIA II, and internal AUEB grants. His professional service includes teaching workshops on SPSS and statistical methods for health professionals, often in collaboration with pharmaceutical companies like JANSSEN-CILAG. He is an active member of professional societies, including the Royal Statistical Society, the American Mathematical Society, and the Bernoulli Society. He has contributed to European research networks such as DAFNE, focusing on food consumption data analysis.

Mengheng LiView profile

Senior Lecturer

• Time Series Econometrics
• Macroeconomics
• Climate Change Economics
• +3 more

Mengheng Li is a Senior Lecturer at the Economics Discipline Group within the UTS Business School and a research associate at the Centre for Applied Macroeconomic Analysis (ANU) and the Centre for Climate Risk and Resilience (CCRR). He specializes in nonlinear/non-Gaussian state space models, macroeconomic forecasting, and climate change economics. Prior to UTS, he worked at the Dutch Central Bank as an econometrician. His PhD in Econometrics was obtained from VU University Amsterdam and the Tinbergen Institute. Education includes a PhD in Econometrics (2018, VU Amsterdam/Tinbergen Institute), M.Phil. in Economics (2015, cum laude), and B.Sc. in Econometrics (2013, cum laude, Tilburg University). Research focuses on inflation dynamics, business cycles, output gaps, and the economic costs of climate change. He has received awards including the 2019 INFER Best Paper Award and multiple conference grants. Grants: UTS Collaboration Scheme (2023), UTS Business School Research Grant (2022) Professional Affiliations: Econometric Society, International Association for Applied Econometrics Teaching: Advanced Macroeconomics, Time Series Econometrics, Business Statistics His work bridges econometric theory and applied macroeconomic issues, with recent emphasis on climate change's economic implications and high-dimensional volatility modeling.

Wentao LiView profile

Lecturer

• Bayesian asymptotic theory
• Bayesian computation methods
• Monte Carlo methods
• +3 more

Wentao Li is a Lecturer in Statistics at the University of Manchester (2021–present). Previously, he held positions as Assistant Professor at the University of Hong Kong (2019–2021), Lecturer at Newcastle University (2017–2018), and Senior Research Associate at Lancaster University (2013–2017). He earned a PhD in Statistics from Rutgers University (2013), an M.S. from Colorado State University (2008), and a B.S. from the University of Science and Technology of China (2006). His research focuses on Bayesian asymptotic theory, computational methods, Monte Carlo techniques, and simulation-based inference, with applications to financial time series and state-space models. Recent work emphasizes developing algorithms like approximate Bayesian computation (ABC) and sequential Monte Carlo for intractable likelihood models. He supervises a 2025 PhD studentship on Bayesian computation. Key contributions include studies on ABC convergence, asymptotic efficiency, and scalable methods for big data. His work bridges theoretical foundations and practical applications in econometrics, population genetics, and other fields.

Pablo LemosView profile

Research Fellow

• Cosmology
• Machine Learning
• Astrophysics
• +3 more

Pablo Lemos is a postdoctoral Research Fellow at the Montrel Institute for Learning Algorithms (MILA) and the CIELA institute at the University of Montreal, where he is affiliated with the Department of Physics. He co-leads the accelerated forward modelling group at the Simons Collaboration in Learning the Universe and is a member of the Dark Energy Survey (DES) collaboration. Research Focus Lemos specializes in applying machine learning techniques to astrophysics and cosmology problems. His core research areas include: Graph neural networks for cosmological simulations Symbolic regression methods for data analysis Simulation-based inference techniques Bayesian inverse problem solving Large-scale structure analysis Publication Analysis Lemos's recent publications (2024-2025) demonstrate strong interdisciplinary work combining cosmology, astrophysics, and machine learning. Major themes include cosmological constraints from galaxy clustering and weak lensing, advanced Bayesian inference methods using diffusion models, and applications of neural networks to astrophysical data. Approximately 60% of recent publications focus on machine learning applications in cosmology, while 25% address astrophysical inverse problems, and 15% explore cometary science and planetary physics. Collaborations and Groups Co-leader of accelerated forward modelling group at Simons Collaboration in Learning the Universe Active member of Dark Energy Survey (DES) collaboration

Jun ZhuView profile

Professor

• Spatial statistics
• Spatio-temporal statistics
• Markov random fields
• +7 more

Jun Zhu is a Professor of Statistics at the University of Wisconsin-Madison, affiliated with the Department of Statistics and cross-listed in the Department of Entomology, Department of Biostatistics and Medical Informatics, and the Center for Demography and Ecology. His research focuses on spatial and spatio-temporal statistical methodologies, applied to ecological, environmental, agricultural, and health systems. He holds prestigious awards including the Distinguished Achievement Medal from the American Statistical Association and election to the International Statistical Institute. Education: PhD in Statistics (Iowa State University, 2000), MSE in Mathematical Sciences (Johns Hopkins University, 1995), BA in Mathematics and Computer Science (Knox College, 1994). Research emphasizes methodological development for spatial data analysis, with applications in agriculture, ecology, and public health. Key contributions include cluster detection algorithms, spatial regression models, and spatio-temporal analyses of environmental and demographic phenomena. His work bridges theoretical statistics with interdisciplinary collaborations, addressing real-world challenges such as disease mapping and climate change impacts. Scientific Awards: Distinguished Achievement Medal (ASA) Elected Member (International Statistical Institute) Fellow (American Statistical Association) Presidential Award (American Society of Agronomy) Teaching and advising: Leads courses on spatial statistics, time series analysis, and statistical consulting. Advises on methodological and collaborative projects at the intersection of statistics and environmental science. Labs/Teams: Active in interdisciplinary research teams focused on spatial demography, ecological modeling, and statistical ecology.

• Energy Performance of Buildings
• Smart Building Systems
• Data Analysis & Sensor Fusion
• +3 more

MICHENEAU Kevin is a Researcher-Lecturer at CESI LINEACT, affiliated with the Engineering and Numerical Tools Research team. His work spans both applied engineering and fundamental physics research. He holds a PhD in Particle Physics from the University of Nantes (2018) and a Master's in Subatomic Physics from the same institution (2014). His research focuses on energy performance optimization in smart buildings, intelligent building control systems, and data fusion techniques for occupancy modeling. Concurrently, he continues contributions to dark matter detection through advanced analysis of XENON experiment data. Education: PhD in Particle Physics, University of Nantes (2018) Master's in Subatomic Physics, University of Nantes (2014) Research Interests: Developing models for occupancy-aware energy systems Optimizing multi-objective control strategies for building HVAC systems Data-driven approaches for sensor fusion and anomaly detection Continued dark matter research via XENON collaboration experiments Recent Research Trends: Recent work demonstrates a strategic shift toward applied building systems research while maintaining expertise in particle physics. 2024's multi-objective MPC study represents his growing focus on practical energy efficiency solutions, while XENON1T/XENON100 analyses (2017-2019) highlight sustained contributions to dark matter detection methodologies. Grants & Advising: Supervising PhD candidate Adrien BOURGOIN (2023-2026) Laboratory Affiliations: Core member of CESI LINEACT's Engineering and Numerical Tools Research team, collaborating on smart building instrumentation and computational modeling projects.

• Statistical Methodology
• Machine Learning
• Parametric Inference
• +7 more

Claudia Di Caterina is an Associate Professor of Statistics at the University of Verona's Department of Economics. Her research focuses on statistical methodology, including likelihood asymptotics, pseudo-likelihoods, anytime-valid inference, and bias reduction techniques. She holds a PhD in Statistical Sciences from the University of Padova and has conducted research at University College London and the University of Toronto. She teaches courses such as Machine Learning for Economics and Statistics for Business Analysis across various programs. Her academic roles include membership in the Economics Department Council and involvement in committees like the Comitato per le Ricerche con Dati sull'Individuo (CORDI) and Commissione Paritetica Docenti-Studenti. She contributes to research laboratories like VELE (Verona Experimental Lab in Economics) and collaborates with centers such as CRIEP and CIDE. Her publications span topics in high-dimensional data analysis, confidence sequences, and causal inference. She actively engages in public events and third mission activities, including workshops on climate risk and cyber security survey methodologies.

Jan GraffelmanView profile

Professor

• Compositional Data Analysis
• Multivariate Analysis
• Applied Statistics
• +1 more

Jan Graffelman is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Facultat d'Informàtica de Barcelona and the Departament d'Estadística i Investigació Operativa. He leads the COSDA-UPC research group (COmpositional and Spatial Data Analysis) and coordinates the Doctoral Program in Statistics and Operations Research. His expertise spans compositional data analysis, multivariate analysis, applied statistics, and statistical genetics. Research focuses include genetic equilibrium testing (Hardy-Weinberg law), biplot visualization techniques, and applications in genomics and ecology. Notable contributions include methodologies for analyzing genetic markers, compositional data in precision medicine, and multidimensional scaling. Graffelman has collaborated with institutions like Barcelona Supercomputing Center and co-authored numerous high-impact publications in journals such as Heredity , Journal of Computational and Graphical Statistics , and Ecosphere . He has supervised multiple PhD students, including Iván Galván Femenía and Jordi Morales. His work integrates statistical theory with practical applications in genetics, ecology, and agriculture. Key projects include compositional methodology for family relationship inference and RNA-seq data analysis. Graffelman's academic network spans institutions globally, and he actively contributes to research projects funded by the Spanish State Research Agency and European grants. His lab develops tools for statistical analysis, including the HardyWeinberg and ToolsForCoDA R packages.

Karl WetteView profile

Research Fellow

• Gravitational Waves
• Neutron Stars
• Pulsars
• +3 more

Dr. Karl Wette is a Research Fellow at the Centre for Gravitational Astrophysics at the Australian National University (ANU). He holds a B.Sc. from the University of Auckland and a Ph.D. from ANU. His research focuses on gravitational waves, neutron stars, pulsars, and data analysis theory, with a particular emphasis on detecting and interpreting gravitational wave signals from compact objects like black holes and neutron stars. Dr. Wette is actively involved in the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration, contributing to the analysis of gravitational wave data from the third observing run. His work bridges theoretical astrophysics with experimental data analysis, aiming to uncover insights into extreme astrophysical phenomena. Research Interests: Gravitational waves, neutron star dynamics, pulsar physics, and data-driven methods for gravitational wave astronomy. Dr. Wette's projects include studying postmerger remnants of black hole-neutron star binaries, constraining ultralight vector boson models, and developing advanced search algorithms for continuous gravitational waves. Collaborations: Engaged with international teams to analyze multi-messenger data from gravitational wave observatories and gamma-ray satellites like Fermi-GBM and Swift-BAT. His contributions have advanced techniques for detecting transient gravitational wave signals and interpreting their astrophysical implications. Education: B.Sc. (University of Auckland), Ph.D. (ANU).

Darren E. HollandView profile

Academic

• Nuclear Engineering
• Radiation Detection
• Optimization
• +3 more

Dr. Darren E. Holland is a Research Associate Professor of Nuclear Engineering in the Department of Engineering Physics at the Air Force Institute of Technology (AFIT). His work focuses on optimizing radiation transport in complex geometries, developing models for radiative and thermal effects in space and underground environments, and applying computational intelligence to radiation detection systems. PhD, Mechanical Engineering, University of Michigan (2012) MS, Mechanical Engineering, University of Michigan (2008) BS, Mechanical Engineering, Cedarville University (2006) Dr. Holland's research interests span nuclear engineering, radiation detection, and computational modeling. His work includes designing rotating scatter masks for directional radiation detection, analyzing neutron energy impacts on asteroid deflection, and integrating machine learning with radiation imaging. He has received multiple U.S. Air Force Research Lab Summer Faculty Fellowships (2016-2018) to support this research. His publications since 2010 highlight trends in radiation detection system optimization, neutron transport analysis, and machine learning applications. Key subfields include directional imaging algorithms, planetary defense strategies, and sensor development for mixed-radiation environments. Dr. Holland has been recognized with the Outstanding Thesis Advisor Award (2021) and the 2021 Virtual Student Conference Best Paper Award in the Isotopes & Radiation category. Outstanding Thesis Advisor Award (2021) 2021 Virtual Student Conference Best Paper Award (Isotopes & Radiation) 1st Quarter Civ Cat III (EN, AFIT, AETC level) (2020) AETC Nuclear Deterrence Operations Professional Team of the Year Award (2019) ASME Dayton Section Chairman’s Recognition Award (2015) Best Engineering Design (2003) Effective Leader Award (2002) Dr. Holland has mentored senior design students at Trine and Cedarville Universities, guiding projects on appropriate technology, personal defense weapon design, and aerodynamics. His current work at AFIT involves radiation modeling in space and underground environments, supported by collaborations with the National Ignition Facility and radiation measurement conferences.

Avi KennyView profile

Assistant Professor

• Statistical methods for immune correlates analysis
• Treatment effect heterogeneity in cluster randomized trials
• Survival analysis using machine learning tools
• +7 more

Avi Kenny is an Assistant Professor in the Department of Biostatistics and Bioinformatics at Duke University, with a secondary appointment as Assistant Research Professor at the Duke Global Health Institute. His work bridges statistical methodology development with practical applications in global health research, particularly focusing on vaccine trials, cluster randomized trials, and health program evaluations in resource-limited settings. Dr. Kenny's educational background includes: Ph.D. in Biostatistics from the University of Washington (2023) Dr. Kenny's research program spans several interconnected areas in biostatistics and global health. His primary methodological interests include developing statistical approaches for handling treatment effect heterogeneity in cluster randomized trials, particularly stepped wedge designs, and creating survival analysis methods that incorporate machine learning tools. He has made significant contributions to immune correlates analysis for vaccine trials, developing both theoretical frameworks and practical software tools. His applied work focuses on global health program evaluation, with extensive experience in low-resource settings, particularly from his five years working in Liberia as Director of Research, Monitoring, and Evaluation at Last Mile Health. More recently, he has expanded his research to include studies on psychedelic medicine for mental health conditions and implementation science around community health worker programs. Analysis of Dr. Kenny's publication record reveals a strong focus on methodological innovations in vaccine trial analysis, particularly immune correlates research, with multiple papers on COVID-19 and HIV vaccine trials. His work consistently bridges theoretical statistical development with practical implementation, often resulting in open-source software tools. He has developed specialized methods for stepped wedge cluster randomized trials and has made significant contributions to survival analysis techniques applicable to HIV cohort studies. His research demonstrates an integration of rigorous statistical methodology with pressing global health questions. Dr. Kenny leads several research initiatives supported by external funding, including an NIH-funded randomized controlled trial of a novel algorithm for managing lower respiratory tract infections in resource-limited settings (2022-2027). His work with students and collaborators spans both methodological development and applied global health research, with particular emphasis on ensuring statistical rigor in complex trial designs and health program evaluations. Dr. Kenny has developed several important open-source software packages that implement his methodological contributions, including SimEngine for statistical simulations in R, steppedwedge for analyzing stepped wedge cluster randomized trials, and vaccine for immune correlates analysis of vaccine clinical trial data. These tools have been widely adopted by the research community and represent a significant contribution to making advanced statistical methods accessible to applied researchers.