Professor William Henley is a leading academic at the University of Exeter Medical School, where he serves as Professor of Medical Statistics and Head of the Health Statistics Group. His work focuses on advanced statistical methodologies for public health research. Qualifications: BA (Oxford), MSc (Southampton), PhD (Open University) Research Interests: Specializes in longitudinal data analysis for chronic disease progression, quasi-experimental designs for causal inference in observational studies, and treatment stratification frameworks for type 2 diabetes. Explores statistical applications in epidemiology of ageing, bioinformatics, and survival analysis. Scientific Contributions: Recent publications emphasize real-world evidence generation, including biologic therapies for asthma, corticosteroid pneumonia risk in COPD, and diabetes treatment optimization. Methodological innovations include prior event rate ratio adjustments for confounding bias.
Hyunjoo Kim Karlsson is a researcher at the Department of Economics and Statistics, School of Business and Economics, Linnaeus University. Her work focuses on statistics and finance, particularly in high-dimensional data analysis, wavelet decomposition, and machine learning applications. Doctoral thesis: Dynamics of macroeconomic and financial variables in different time horizons (2012), Jönköping International Business School. Her research spans shrinkage estimators, outlier detection, time series modeling, and multivariate analysis under multicollinearity. Recently, she has expanded into statistical learning and mixed data sampling (MIDAS) for economic nowcasting. Key publication trends include oil price impacts on economies, exchange rate dynamics, and nonlinear financial modeling using wavelet methods and machine learning. She collaborates with researchers like Krister Månsson and R. Scott Hacker. Hyunjoo is part of the Deterministic and Stochastic Modelling group within Linnaeus University's Data Intensive Sciences and Applications (DISA) center, contributing to interdisciplinary sustainable co-creation projects.
Brian Caffo is a Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, with an active research profile evidenced by 2024-2025 publications and GitHub contributions. He is a key contributor to Neuroconductor (an R platform for medical imaging analysis) and the Acute to Chronic Pain Signatures (A2CPS) project, while maintaining significant educational impact through Coursera data science courses and open-source textbooks. His research spans biostatistics, neuroimaging analysis, and causal inference methodology, with emphasis on functional MRI data, multi-omics integration, and machine learning applications in public health. Current work focuses on advanced neural architectures for brain imaging, causal mediation in autism research, and biomarker discovery for chronic pain and neurodegenerative diseases. Analysis of his 15 most recent publications reveals dominant themes in transformer-based neuroimaging analysis (28%), causal inference applications (27%), and multi-omics/pain signature research (20%). His work consistently bridges statistical methodology with clinical applications in HIV prevention, autism spectrum disorders, and long COVID prediction through large collaborative projects like the National COVID Cohort Collaborative. Dr. Caffo leads the JHU Data Science Lab and contributes to multiple interdisciplinary initiatives including shell MEA development for neural organoids and DREAM-02 HIV microbicide trials. His GitHub profile shows active maintenance of educational repositories like LittleInferenceBook and regmodsbook, supporting widespread adoption of data science methods in biomedical research.
Dr. Sven Klaaßen serves as a Research Fellow at the University of Hamburg's Hamburg Business School within the Professorship for Statistics with Application in Business Administration, collaborating closely with Prof. Dr. Martin Spindler since 2021. His research focuses on developing advanced statistical methodologies for complex data environments. His academic credentials include: Ph.D. in Statistics from Hamburg Business School (2020) Visiting Scholar at MIT Department of Economics (2022) M.Sc. in Business Mathematics from University of Hamburg (2016) BSc in Business Mathematics from University of Hamburg (2014) Dr. Klaaßen's research program centers on Machine Learning, Causal Inference, Deep Learning, and High-Dimensional Statistics, with particular emphasis on developing robust inference techniques for modern data challenges. His work bridges theoretical statistics with practical applications in business analytics and econometrics, often addressing the complexities of high-dimensional datasets where traditional methods fail. Analysis of his recent publications reveals a clear trajectory toward integrating machine learning with causal inference frameworks, exemplified by his leadership in the DoubleML software ecosystem. His research increasingly tackles multimodal data challenges while maintaining rigorous statistical foundations, with applications spanning economics, operations research, and business decision systems. As an active member of Prof. Spindler's research group, Dr. Klaaßen contributes to collaborative projects developing open-source statistical tools and advancing methodological frontiers in causal machine learning. The team maintains strong industry and academic partnerships focused on translating theoretical innovations into practical analytical solutions.
Scott Davies serves as an Adjunct Professor in the Department of Sociology within McMaster University's Faculty of Social Sciences. With a prolific scholarly output spanning from 1990 to the present, his work primarily focuses on the sociology of education, educational policy, and issues of inequality within educational systems. Davies' research interests center on educational stratification, school choice mechanisms, private tutoring markets, cultural capital theory, and summer learning programs. His work often examines how market forces intersect with educational institutions, particularly investigating how supplementary education services and private schooling options create or mitigate educational inequalities. A significant portion of his research analyzes Ontario's educational landscape, with numerous studies examining French-language schools, achievement gaps, and policy implications of various educational interventions. Analysis of his recent publications reveals a consistent focus on educational inequality, with particular attention to summer learning programs and their differential impacts on student outcomes. His research increasingly incorporates longitudinal data analysis and causal inference methods, with several recent studies examining how educational interventions affect vulnerable populations. Davies has also expanded his work to address contemporary challenges like the impact of the COVID-19 pandemic on educational equity and the role of digital technology in classroom engagement. Davies maintains an extensive collaborative network, with recent co-authorship on numerous publications related to child mental health and educational outcomes through the Ontario Child Health Study. His scholarly impact is evident through significant citation metrics and engagement across academic, policy, and media platforms.
Dr. Sarah Wieten is an Assistant Professor at Durham University , affiliated with the Department of Philosophy . She also serves as Co-Director of CHESS (Centre for Humanities Engaging with Science and Society) and is a Fellow of the Durham Research Methods Centre and the Wolfson Research Institute for Health and Wellbeing. Research Interests : Philosophy of medicine, epidemiology, economics, and science; meta-research; bioethics; clinical ethics. Key Projects : Examining hospital code status ontologies, adaptive meta-analysis challenges, causal language in health sciences, and ethical integration in systematic reviews. Future Work : Multi-year study on methodological differences in epidemiology and economics; developing ethical systematic review variations; qualitative analysis of adaptive meta-analysis participation issues. Scientific Awards : 2022: Invited Talks at Ethics Committee Consortium and University of Exeter Causality Seminar Series. 2021: Highlighted Philosop-Her of Science by Philosophy of Science Association Women’s Caucus. Notable Contributions : Co-developed DAGWOOD framework for causal assumption analysis; published on ventilator triage policies during the pandemic; explored ethical dimensions of biohybrid robotics and patient values in evidence-based medicine.
Fredrik Falkenström is a Professor at Linnaeus University, where he is affiliated with the Department of Psychology within the Faculty of Health and Life Sciences. He leads the Division of Clinical Psychology and is the principal investigator for the "Identifying the Active Ingredients of psychotherapy: A Methods development project (AIM)", which focuses on developing innovative methods to identify mechanisms of action in psychotherapy. Dr. Falkenström's research spans multiple critical areas in clinical psychology and psychotherapy science. His work primarily investigates the mechanisms of change in psychotherapy, with particular emphasis on methodological innovations that bridge the gap between research and clinical practice. His research interests include: Advanced statistical methods for psychotherapy process research Therapeutic alliance and attachment processes Comparative effectiveness of different psychotherapy approaches Applications to anxiety disorders, particularly panic disorder Emotion regulation and its role in therapeutic change Working alliance dynamics across different populations His publication record demonstrates a strong trajectory of methodological innovation in psychotherapy research. Recent work has focused on time-lagged panel models, copula models for causal inference, and detrending methods to better understand the complex temporal dynamics of therapeutic change. His research consistently examines how specific therapeutic processes (such as working alliance, attachment patterns, and emotional processing) relate to treatment outcomes across various disorders and populations, with particular attention to panic disorder and depression. Dr. Falkenström has published extensively in top-tier journals including Clinical Psychology Review, Journal of Consulting and Clinical Psychology, and Psychotherapy Research. His work has significant implications for both the science and practice of psychotherapy, helping clinicians better understand which components of therapy contribute to positive outcomes and how to optimize treatment approaches for individual patients. His research extends across multiple settings and populations, including studies on adolescent mental health, foster care interventions, and cross-cultural applications of psychotherapy. This breadth demonstrates his commitment to understanding psychotherapy mechanisms across diverse contexts and client groups.
Charles Burant is Professor of Nutritional Sciences at the University of Michigan School of Public Health and holds a professorship in the Section of Metabolism, Endocrinology, and Diabetes within the Medical School's Department of Internal Medicine. He holds the endowed Dr. Robert C. and Veronica Atkins Professor of Metabolism chair. His clinical practice focuses on obesity, type 2 diabetes, and related metabolic disorders at the WK Kellogg Eye Center, Brehm Tower in Ann Arbor. Dr. Burant's research integrates multi-omic approaches (genomics, transcriptomics, proteomics, metabolomics) with clinical and behavioral phenotypes to unravel mechanisms of obesity, insulin resistance, and diabetes. His laboratory investigates pancreatic beta-cell metabolism, insulin secretion dynamics, and adult pancreatic progenitor cells for beta-cell regeneration. As director of the Michigan Metabolomics and Obesity Center (MMOC), he oversees the NIH-funded Nutrition Obesity Research Center and Michigan Regional Comprehensive Metabolomics Resource Core, providing critical infrastructure for metabolic disease research. The MMOC also coordinates the Investigational Weight Management Clinic, which combines clinical care with phenotypic tracking to study diabetes prevention and weight regain mechanisms in formerly obese patients. Analysis of his recent publications reveals dominant themes in multi-omic exercise physiology, causal metabolite-disease relationships, and tissue-specific metabolic adaptations. His work consistently emphasizes sexual dimorphism, temporal dynamics, and molecular mechanisms in metabolic disorders, particularly through large-scale consortia like MoTrPAC. Key focus areas include lipid metabolism in disease contexts, adipose tissue remodeling, and mitochondrial function across tissues. While no specific scientific awards beyond his endowed chair are listed, his leadership of major NIH-funded centers demonstrates significant recognition. His grant portfolio includes infrastructure support through the MMOC, NORC, and Metabolomics Resource Core, enabling extensive human and animal studies in metabolic diseases. Dr. Burant directs the MMOC's integration of research and clinical care through the Investigational Weight Management Clinic. His laboratory maintains active programs in insulin resistance mechanisms using animal models and explores pancreatic progenitor cell applications for diabetes therapy, representing forward-looking approaches to metabolic disease treatment.
Thomas Nagler is a Professor at the Department of Statistics, Faculty of Mathematics, Computer Science and Statistics at Ludwig Maximilian University of Munich (LMU Munich). He also serves as a principal investigator at the Munich Center for Machine Learning (MCML), where he leads research at the intersection of mathematical statistics and machine learning. Nagler received his academic training at Technical University of Munich (TU Munich), earning a BSc in Mathematics (2009-2012), followed by an MSc in Mathematical Finance (2012-2014), and ultimately a PhD in Mathematical Statistics (2014-2018). Prior to his current position at LMU Munich, he held assistant professor positions at TU Delft (2021-2022) and Leiden University (2019-2021). Professor Nagler's research focuses on developing novel statistical methods with theoretical guarantees and scalable algorithms. His work spans high-dimensional dependence modeling, particularly using vine copulas, statistical machine learning, time series and functional data analysis, and statistical computing. He emphasizes creating methods that can be practically implemented and applied to solve real-world problems across diverse domains. An analysis of Nagler's recent publications reveals a strong emphasis on vine copula methodology, uncertainty quantification in machine learning, and applications to climate science and epidemiology. His work bridges theoretical statistics with practical implementation, often resulting in open-source software tools that make advanced statistical methods accessible to practitioners. The interdisciplinary nature of his research is evident in collaborations spanning climate modeling, healthcare, and finance. While specific awards are not detailed in the available information, Nagler's research impact is evident through his significant contributions to statistical methodology and his active engagement with the research community through open-source software development. As a principal investigator at MCML and Professor at LMU Munich, Nagler leads a research group focused on advancing statistical methodology for complex data analysis. His GitHub profile indicates active collaboration with students and researchers, with several followers from LMU Munich and other institutions. His research program appears to be well-funded through the MCML and university resources, supporting both methodological development and application-focused projects. Nagler maintains strong ties with the computational statistics community through his leadership of the VineCopula and pyvinecopulib projects, which provide essential tools for dependence modeling. His work with the Munich Center for Machine Learning positions him at the forefront of interdisciplinary research combining statistical theory with practical machine learning applications.
Diana Moreira is an Assistant Professor at the University of California, Davis, Department of Economics. She is also a Lemann Fellow and a faculty affiliate at CEGA (Center for Effective Global Action). Ph.D. in Business Economics from Harvard University (2017) M.A. in Economics from PUC-Rio (2009) B.A. in Economics from Ibmec (2007) Diana's research focuses on development economics , particularly its intersection with public economics and organizational economics . Her work examines policy design, bureaucratic efficiency, and the impact of institutional reforms in Brazil and other regions, with a strong emphasis on empirical evidence and randomized experiments. Diana's recent publications highlight trends in public administration reform , meritocracy , and corruption effects . Her studies on civil service practices, political turnover, and state capacity offer insights into governance structures and policy effectiveness in developing economies. Lemann Fellowship Affiliate, CEGA (Center for Effective Global Action) At UC Davis, Diana teaches Development Economics at the Ph.D. level and Analysis of Economics Data at the undergraduate level. Her methodology bridges economics, political science, and organizational theory, focusing on causal inference and policy evaluation.
Christoph Breunig is a Professor in the Department of Economics at the University of Bonn. His work bridges theoretical econometrics with empirical applications, focusing on nonparametric methods, instrumental variable modeling, and causal inference. His research addresses challenges in high-dimensional data, missingness mechanisms, and treatment effect estimation. University: University of Bonn Department: Economics Academic Rank: Professor Email: cbreunig@uni-bonn.de Research Trends: Nonparametric and semiparametric estimation techniques Applications of instrumental variables in causal inference Handling missing data and measurement error High-dimensional statistical models with economic applications Specification testing in complex regression frameworks Connections between microeconomic theory and empirical methods
Christy L. Avery is a Professor in the Department of Epidemiology at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. She serves as Program Leader for Cardiovascular Disease Epidemiology. Her research focuses on genomic epidemiology, gene-environment interactions, metabolomics, and assessing cardiovascular disease burden in diverse populations across the life course. Her work integrates longitudinal data and multi-level factors (cellular, individual, contextual) to study health risks like hypertension and hypercholesterolemia, as well as behaviors such as smoking, physical activity, and obesity. She investigates genetic diversity in health risks among multi-ethnic U.S. populations and leads studies on metabolomic correlates of health and disease. Research Themes include: Genomic epidemiology of cardiovascular diseases Gene-environment interactions and omic biomarkers Multi-ethnic population studies Epidemiological methods for life-course analysis Metabolomics in disease prevention Recent Publications highlight trends in: Genetic risk scores for cardiovascular disease Multi-ethnic genome-wide association studies (GWAS) Metabolomic biomarkers in lifespan health Pharmacogenomics and drug-gene interactions Environmental health and exposomics Statistical genetics and causal inference She contributes to trans-ethnic genomics , refining genetic loci and improving precision medicine frameworks. Her collaborations span consortia like the Trans-Omics for Precision Medicine (TOPMed) and the Population Architecture Using Genomics and Epidemiology (PAGE) study.
Bryan E. Shepherd serves as Professor of Biostatistics and Biomedical Informatics and Vice Chair of Faculty Affairs in the Department of Biostatistics at Vanderbilt University. As a primary faculty member, he leads methodological research while overseeing departmental academic operations within the Vanderbilt University Medical Center ecosystem. His foundational education includes a PhD in Biostatistics from the University of Washington, establishing his technical expertise in advanced statistical methodologies. This training underpins his dual focus on theoretical innovation and real-world health applications. Dr. Shepherd's research centers on solving complex data challenges through novel biostatistical frameworks, particularly causal inference under measurement error, two-phase sampling optimization, and longitudinal analysis of error-prone clinical data. His work bridges theoretical statistics and urgent public health needs, with HIV research forming a major application domain where he addresses disparities in treatment access, care continuum outcomes, and comorbid conditions across global populations. Recent methodological breakthroughs include R packages for semiparametric likelihood estimation and rank correlation analysis. Analysis of his 2023-2025 publications reveals a dominant trend toward methodological solutions for data imperfections (measurement error, selective sampling) combined with high-impact applications in HIV epidemiology. His Latin American and African cohort studies consistently examine sex disparities, treatment efficacy, and social determinants of health, while his statistical innovations focus on efficiency gains in causal estimation and correlation modeling for clustered data. No specific scientific awards are documented in the available institutional profile. Though student advising details are unlisted, his extensive publication record—particularly collaborative international studies—implies active mentorship of junior researchers. His leadership as Vice Chair of Faculty Affairs suggests significant administrative responsibilities alongside research. Grant activity is inferred from multi-country HIV studies involving Vanderbilt's Center for Quantitative Sciences and Data Coordinating Center. Dr. Shepherd operates within Vanderbilt's collaborative research infrastructure, notably the Center for Quantitative Sciences (CQS) and Biostatistics Data Coordinating Center (VBDCC). His work leverages these resources for large-scale analyses of HIV cohorts across Latin America and Nigeria, focusing on treatment outcomes, genetic risk factors, and health system barriers. The Vanderbilt Nigeria Biostatistics Training Program (VN-BioStat) reflects his commitment to global capacity building in biostatistics.
Fabrizia Mealli is a Full-time Professor in the Department of Economics at the European University Institute (EUI), serving as Director of Graduate Studies. She holds a concurrent leave position as Professor of Statistics at the University of Florence's Department of Statistics, Informatics, Applications (DiSIA). Her research focuses on causal inference, econometric methods, Bayesian analysis, and missing data, with applications in social and biomedical sciences. Education & Academic Roles: Professor of Statistics, University of Florence (on leave) Full-time Professor, EUI Department of Economics (since 2023) Visiting positions at Harvard University (2001, 2015, 2017) Research Interests: Her work centers on causal inference methodologies, including principal stratification, interference, and bipartite settings. She develops statistical techniques for observational and experimental studies, Bayesian approaches, and applications in policy evaluation and network effects. Awards & Editorial Roles: Elected Fellow of the American Statistical Association (ASA) President-elect of the Society for Causal Inference (SCI) Associate Editor for Biometrika , Journal of the American Statistical Association , and Annals of Applied Statistics Grants & Collaborations: Her research has been applied to policy evaluation (e.g., hospital readmission programs, immigration policies) and public health interventions (e.g., air pollution effects). Labs & Teams: Leads initiatives on causal inference under interference and network analysis, collaborating with global institutions and researchers in statistics and econometrics.
Sylvain Arlot is a Professor at the Mathematics Department of Université Paris-Saclay, affiliated with the Probability and Statistics team at Laboratoire de Mathématiques d'Orsay. He leads the Celeste INRIA Saclay project-team and is a junior member of the Institut Universitaire de France (IUF) since 2020. His research focuses on statistical learning theory, non-parametric methods, model selection, and change-point detection. Arlot has contributed to foundational work on cross-validation, penalization techniques, and random forests. He co-organizes the Séminaire Palaisien and serves as an associate editor for the Annales de l'Institut Henri Poincaré B. Education: PhD in Mathematics from Université Paris-Sud (2007), HDR (Habilitation) from Université Paris Diderot (2014). Research Interests: Core areas include statistical learning theory, resampling methods (e.g., cross-validation and bootstrap), and applications in high-dimensional data analysis. His work bridges theoretical guarantees with practical algorithm design, emphasizing data-driven model selection and robust estimation techniques. Grants & Projects: Leads the PEPR IA Project Causali-t-AI (2023–2028) and was a member of the ANR Fast-Big project (2018–2023). He coordinates the math-AI program under Labex Mathématique Hadamard. Awards: Junior IUF membership (2020–2025). Labs/Teams: Heads the Celeste team at INRIA Saclay, collaborating on statistical machine learning and data science challenges.
