Dr. Jake Carson is a Research Fellow at the University of Warwick's Mathematics Institute, specializing in statistical methodology for integrating genomic data into epidemiological analyses. His work bridges infectious disease modeling, Bayesian statistics, and climate science. Previously, he developed Bayesian approaches for Raman spectroscopy diagnostics and palaeoclimate reconstruction at the University of Nottingham. Education PhD in Statistics (2015): University of Nottingham, Thesis: Uncertainty Quantification in Palaeoclimate Reconstruction Research Interests His research focuses on: Statistical methodologies for genomic epidemiology Bayesian model selection in complex systems Applications of Raman spectroscopy in diagnostics Climate modeling with palaeoenvironmental data Key Projects Health Protection Research Unit in Genomics and Enabling Data Scalable Bayesian methods for infectious disease modeling Nanoparticle assemblies for healthcare diagnostics Awards & Grants No awards listed, but his work has been supported by grants from the University of Warwick and collaborative institutions. Labs & Teams Core member of the Mathematics Institute at Warwick and collaborates with cross-disciplinary teams in epidemiology, climate science, and spectroscopy.
Kaze W. K. Wong serves as a research assistant professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University and holds a part-time research software engineer position at the university's Data Science and AI Institute. His work bridges computational astrophysics, statistical methodology, and software engineering with significant contributions to open-source scientific tools. Education PhD in Physics and Astronomy, Johns Hopkins University (2021), recipient of the Gravitational Wave International Committee-Braccini Thesis Prize Research Focus Dr. Wong specializes in integrating deep learning with traditional statistical frameworks, particularly through deep learning-enhanced MCMC sampling and generative modeling for heterogeneous astronomical datasets . His research philosophy emphasizes production-grade open-source software development as critical infrastructure for scientific discovery, spanning digital twins, Bayesian inference, and data science applications. This interdisciplinary approach creates novel pathways for analyzing complex observational data in astronomy and beyond. Scientific Recognition 2021 Gravitational Wave International Committee-Braccini Thesis Prize for doctoral research 2024 Best Paper Award at the 38th Neural Information Processing Systems (NeurIPS) Conference Professional Background Prior to his current appointment, Dr. Wong was a Flatiron Research Fellow at the Center for Computational Astrophysics, Flatiron Institute in New York City. His career trajectory demonstrates a consistent focus on computationally intensive scientific problems requiring innovative methodological synthesis between machine learning and domain-specific physics.
Prof. Christine Choirat is an Invited Professor at the Institute of Global Health, part of the Faculty of Medicine at the University of Geneva. Her research focuses on environmental health, air pollution exposure, and causal inference methodologies applied to public health policy. She has contributed to studies on the health impacts of coal emissions, federal policy evaluations, and pandemic preparedness. Her work integrates advanced statistical techniques such as Bayesian modeling and machine learning to address complex health issues. She collaborates actively within the Institute of Global Health and has authored influential tools like the R package PanelPRO for risk modeling. No scientific awards are explicitly listed, though her research has significant implications for global health and environmental policy.
Dr. V Anne Smith is a Senior Lecturer at the School of Biology, University of St Andrews. Her research focuses on interdisciplinary applications of Bayesian networks to address challenges in antibiotic resistance, ecological modeling, and exoplanet science. She leads projects investigating socio-environmental drivers of antimicrobial resistance in East Africa, poultry genetics, and science communication through science fiction analysis. Dr. Smith collaborates across disciplines, including with institutions like the HATUA and CARE Consortia, and actively engages in public science outreach through events like the World Science Fiction Convention. She advises three PhD students and has contributed to over 50 research outputs. Key honors include the 2010 Most Valuable Professional award in database operations. Her work integrates computational methods with biological, medical, and social data to inform policy and advance scientific understanding. Education details are not explicitly stated in the provided texts, but her extensive academic publications and supervisory roles indicate a strong academic background in biological sciences. Her research spans computational biology, epidemiology, and environmental science, with a focus on developing novel methodologies like Bayesian network modeling for complex systems. She is affiliated with multiple research centers at St Andrews, including the St Andrews Centre for Exoplanet Science and the Institute of Behavioural and Neural Sciences. Dr. Smith’s recent publications emphasize causal Bayesian network applications to combat AMR, poultry stress genetics, and exoplanet media representation. Her lab’s work bridges theory and practice, offering tools for policy analysis and public health intervention. She also contributes to open-access datasets and software, advancing reproducibility in ecological and biomedical research.
Søren Højsgaard is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His work bridges statistical theory, computational methods, and pedagogical innovation, with a strong focus on graphical models, Bayesian networks, and computer algebra systems in R. His research interests lie at the intersection of statistics, machine learning, and software development. He is particularly known for developing R packages such as caracas , gRbase , and sparta , which facilitate symbolic computation and efficient inference in probabilistic models. His work supports both advanced research and accessible teaching in data science. The recent publications highlight a consistent trend toward integrating symbolic mathematics with statistical computing, improving scalability in Bayesian network predictions, and enhancing statistics education through tools like Quarto and R. His contributions span theoretical algorithms, software implementation, and educational applications. Active contributor to open-source statistical software Focus on model-based prediction and symbolic computation Emphasis on teaching innovation using computational tools He has been involved in academic outreach through conference presentations and media engagement, discussing topics ranging from R programming to workplace safety modeling. His leadership role in the Department of Mathematical Sciences was recently highlighted in university communications. Søren Højsgaard leads and contributes to projects that combine rigorous statistical methodology with practical implementation, supporting both research and education in modern data science.
Carlo Joseph Graziani is a Research Associate Professor in the Department of Astronomy & Astrophysics at the University of Chicago and is currently affiliated with Argonne National Laboratory, part of the Mathematics and Computer Science division, specifically the Laboratory for Applied Mathematics, Numerical Software, and Statistics. His work bridges computational science, applied mathematics, and theoretical astrophysics. His research interests focus on computational science , applied mathematics , and theoretical astrophysics , with recent applications in Bayesian evaluation of clinical trial data for vaccine efficacy. His expertise lies in developing and applying advanced numerical methods and mathematical models to complex scientific problems across disciplines. Graziani has contributed to major scientific projects, including the international High-Energy Transient Explorer (HETE) mission, and has held research positions at NASA's Goddard Space Flight Center and the Enrico Fermi Institute. His interdisciplinary work reflects a strong integration of physics, statistics, and high-performance computing. He is actively engaged in research at Argonne National Laboratory, with no indication of retirement or part-time status. His work continues to impact both astrophysical modeling and data-driven scientific evaluation methods.
Andrea Ingeborg Riebler is a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). Her research focuses on Bayesian statistics, spatial modeling, disease mapping, and statistical software development. She is a leading contributor to the R-INLA framework for Bayesian hierarchical modeling and has developed packages like SUMMER and makemyprior for spatial-temporal analysis and prior elicitation. Her work bridges methodological advancements with applications in public health, demography, and epidemiology. Key research interests include spatial epidemiology, meta-analysis, and robust statistical modeling. She collaborates widely, particularly with researchers in geostatistics, computational statistics, and health sciences. Notable contributions include methods for small-area estimation in low/middle-income countries, cancer burden projections, and addressing positional uncertainty in geostatistical surveys. Her recent publications (2023–2025) emphasize scalable disease forecasting, mortality-based cancer incidence prediction, and improving DHS data analysis through positional adjustment. Teaching includes advanced computational statistics, survival analysis, and statistical modeling for biologists. Education: Doctoral dissertation work at NTNU (2021) focused on robust hierarchical models. Grants/Projects: Active in international health surveys (e.g., European Social Survey Health Inequalities Module). Labs/Teams: Collaborates with the NTNU Statistics Group and global health data initiatives.
Stefanie Muff is a Professor in the Department of Mathematical Sciences at NTNU, specializing in Bayesian statistical methods, quantitative genetics, and ecological modeling. Her work bridges statistical methodology with applications in evolutionary biology and wildlife research. Expertise: Bayesian inference, missing data modeling, genomic prediction, and habitat selection analysis Key affiliations: NTNU's Department of Mathematical Sciences Research interests focus on developing statistical frameworks for ecological and evolutionary questions, including studies on animal movement patterns, inbreeding effects in wild populations, and genomic prediction in non-model organisms. She actively contributes to methodological advancements in handling missing data and measurement error in ecological datasets. Recent work emphasizes applying Bayesian models to real-world ecological problems, such as dispersal genetics in vertebrate metapopulations and spatial variation in metabolic traits. Her interdisciplinary approach integrates statistics with empirical studies on birds and other wildlife. Notable collaborations: Alpine ibex conservation, house sparrow metapopulation studies, and genomic prediction frameworks Advocates for transparent science through teaching courses like ISTT1003 - Statistics and TMA4268 - Statistical Learning. Active in academic outreach with documentaries and workshops on ecological data analysis.
Dr. Alberto Marzo is a Senior Lecturer in the Department of Mechanical Engineering at the University of Sheffield, serving as Director of the MSc Bioengineering program. His research focuses on computational fluid mechanics, cardiovascular biomechanics, and clinical translation of engineering technologies. He holds a five-year Mechanical Engineering degree from the University of Cagliari, Italy, and a PhD from the University of Sheffield. Early in his PhD, he received the David Crighton Fellowship, enabling collaborative research at the University of Cambridge on flow-induced oscillations in elastic vessels. Education: Bachelor's/Master's (5-year): Mechanical Engineering, University of Cagliari, Italy PhD: University of Sheffield Research Interests: Image-based 3D cardiovascular modeling 1D whole-circulation blood flow modeling Medical device optimization (e.g., respiratory treatments) Hemodynamic influences on cardiovascular/biofilm dynamics High-performance computing (HPC) and statistical emulators for validation Recent Work Trends: His publications emphasize clinical translation, such as developing open-source tools (e.g., openBF solver), optimizing stroke treatment protocols, and integrating machine learning with biomechanics for diagnostic biomarkers. He also focuses on musculoskeletal modeling and renal artery hemodynamics. Awards: David Crighton Fellowship (2011) Grants & Leadership: Co-investigator in VPH-DARE@IT (EU FP7) for dementia modeling Principal Investigator for NHS/EPSRC urinary drainage system project Lead of CompBioMed EU H2020 Center of Excellence Teaching & Labs: Teaches courses like MEC320 (Computational Fluid Dynamics) and oversees labs focused on cardiovascular biomechanics and computational modeling.
Dr. Nicolás Hernández is a Lecturer in Statistics at Queen Mary University of London's School of Mathematical Sciences, affiliated with the Data Science, Statistics and Probability Centre. He previously held positions as Senior Research Fellow at UCL's Department of Statistical Science and PDRA at the University of Cambridge's MRC Biostatistics Unit. He earned his PhD in Statistics from Universidad Carlos III de Madrid focusing on 'Statistical learning methods for functional data with applications to prediction, classification and outlier detection'. His research develops statistical/machine learning methods for high-dimensional and functional data applications across energy, economics, environment, demography, business, finance, health, and genetics. Key focuses include predictive confidence bands for functional time series, domain selection/classification in functional data, and outlier detection using Information Theory. Teaching responsibilities include module leadership for Biostatistics and Medical Statistics in the MSc Applied Statistics and Data Science program, and instructing Time Series Analysis for Business in the MSc Business Analytics program. His work emphasizes ethical considerations in biomedical research and practical implementation using R. Research outputs include 7+ peer-reviewed publications in journals like Biometrical Journal , Communications in Statistics , Nature Communications , and Entropy . His methods address challenges in functional data analysis, time series prediction, and anomaly detection across diverse domains. Professional activities include maintaining a research website (https://nicolashernandezb.github.io/) and participation in collaborative projects. Office hours are held weekly at the School Social Hub (MB-B11) on Thursdays 11:30-12:30.
Palle Duun Rohde is an Associate Professor and Head of Research Group at Aalborg University Hospital, working within the Department of Health Science and Technology at The Faculty of Medicine. His research focuses on genomic medicine and precision health, with particular emphasis on quantitative genetic analysis of complex traits and human diseases. He leads multiple research projects related to genetic architecture of diseases, including projects on maternal health, Drosophila research, diabetes, and endometriosis. Dr. Rohde earned his PhD in quantitative and statistical genetics from the Center for Quantitative Genetics and Genomics at Aarhus University, with his thesis focusing on "Dissecting the genetic variation of schizophrenia and ADHD using an integrative genomic approach." His research profile demonstrates extensive work in developing and implementing strategies for integrating prior biological knowledge across databases, studies, and organisms to understand the biology of multifactorial phenotypes. Rohde's research interests span across genetics, quantitative genetics, genomic medicine, statistical genetics, complex traits analysis, precision medicine, and Drosophila research. His scientific focus centers on using large-scale biobank data to better understand how genomic scores and other omics-derived scores can aid in precision health by providing information about an individual's genetic risk. He also contributes to software implementation of statistical and genetic models for identifying genetic risk factors and prediction of complex traits, including work with the R package qgg. His recent publication record (2023-2025) shows a strong emphasis on applying Bayesian statistical methods to complex disease genetics, with particular focus on cardiovascular disease, diabetes, reproductive health, and neurological disorders. His work demonstrates integration of genomic data with other omics layers (particularly proteomics) to improve risk prediction and understand disease mechanisms. The research output reveals a trajectory toward increasingly sophisticated applications of polygenic risk scores and integration of diverse data types for precision medicine applications. Dr. Rohde is actively involved in multiple research projects including SURVIVE (2023-2026) focusing on maternal health, Integration of the Drosophila melanogaster Microbiome and Transcriptome (2022-2026), Understanding the Genetic Architecture of Common Complex Diseases (2023-2025), BALDER (2021-2023) on diabetes analysis, and FEMaLe (2021-2024) on endometriosis using machine learning. These projects demonstrate his commitment to translating genetic findings into clinical applications through the development of clinical decision support tools. His work contributes to UN Sustainable Development Goals, particularly in the area of health and well-being. Dr. Rohde maintains an active research program with collaborations across multiple institutions, as evidenced by his co-authorship on numerous publications and his involvement in various research networks. His laboratory work with Drosophila melanogaster provides valuable insights into fundamental genetic mechanisms that can be translated to human health applications.
Rink Hoekstra is an Assistant Professor at the University of Groningen's Faculty of Behavioural and Social Sciences, specializing in Research and Evaluation of Educational Effectiveness. With an active research profile spanning statistical methodology, psychological science, and educational research, Dr. Hoekstra contributes significantly to contemporary debates in research methodology and scientific practice. Dr. Hoekstra's research interests center on Bayesian statistics, replication studies, and methodological rigor in psychological research. Their work addresses critical issues in the 'crisis of confidence' in scientific research, with particular focus on intellectual humility in science, statistical analysis practices, and the application of Bayesian methods in psychological research. Their fingerprint reveals strong expertise in Bayes Factor (100%), Attention Deficit Hyperactivity Disorder (88%), Psychological Research (48%), and Crisis of Confidence (44%). Analysis of Dr. Hoekstra's recent publications shows a consistent focus on improving research practices in psychology and education. Their work bridges theoretical statistical concepts with practical applications, particularly in Bayesian statistics, research validity, and the science-practice gap. The publications demonstrate a commitment to methodological transparency, data sharing practices, and evidence-based approaches in both research and educational contexts. Fellowship for the Innovation of Teaching (2018) Dr. Hoekstra has been actively involved in teaching innovation, particularly in statistics education, as evidenced by their work on Bayesian statistics instruction for undergraduate psychology students. Their research collaborations span multiple international institutions, reflecting a strong network in methodological research. The publications and activities indicate substantial grant funding supporting their work on research methodology and educational effectiveness. Dr. Hoekstra's work contributes to the UN Sustainable Development Goals, particularly those related to quality education and reduced inequalities. Their research on educational effectiveness and evidence-based practices directly supports efforts to improve educational outcomes and research integrity globally.
Dr. Sarah Schwöbel is a PostDoc researcher at the Chair of Neuroimaging within the Faculty of Psychology at Technische Universität Dresden since 2020. She holds a Dr. rer. nat. (PhD) in Psychology from TU Dresden (2020), an MSc and BSc in Physics from Ludwig-Maximilians-Universität München (2013-2020). Her research focuses on computational modeling of decision-making processes, integrating Bayesian approaches to study habitual vs. goal-directed behavior, and the neural mechanisms underlying predictive processing. She has contributed to studies on active inference frameworks, contextual behavioral control, and sequential decision-making tasks. Her academic journey includes roles as a software developer at Engineering Bureau Cichon, teaching assistant in physics and computer science, and internships in child psychiatry. Her work bridges psychology, neuroscience, and computational methods, with a particular emphasis on applying mathematical models to understand cognitive processes. Notable publications include studies on rational trade-offs in cognitive control, computational models of narcissism etiology, and Bayesian interpretations of behavioral balancing. She collaborates across disciplines, contributing to the ReCoDe addiction research consortium and exploring predictive minds in neurobiological contexts.
Prof. Wolfgang Wiechert is a Universitätsprofessor at Forschungszentrum Jülich GmbH, leading the Computational Systems Biotechnology group within the Institute of Bio- and Geosciences (IBG-1). His research focuses on integrating computational methods with experimental biotechnology to optimize bioprocesses, particularly in metabolic engineering and systems biology. Key areas include metabolic flux analysis, high-throughput screening, and Bayesian statistical methods for process optimization. Research interests span Bioprocess design and scale-up (e.g., itaconate production, fungal cultivation) Automated microbioreactor systems and robotic workflows Development of computational tools for metabolic modeling (e.g., hopsy, pyFOOMB) Application of machine learning in bioprocess analytics Recent publications emphasize Bayesian approaches for flux inference, strain characterization automation, and integration of omics data with process analytics. Collaborations span industries and academic partners, driving innovations in sustainable biomanufacturing and enzyme production systems.
Victor De Oliveira is a Professor in the Department of Management Science and Statistics at the Carlos Alvarez College of Business, The University of Texas at San Antonio (UTSA). He joined UTSA in 2006 after prior roles at the University of Arkansas and Simon Bolivar University. His academic qualifications include a Ph.D. in Statistics from the University of Maryland and master's and bachelor's degrees in Mathematics and Water Resources from Universidad Simón Bolívar. His research focuses on Bayesian methods, environmental and geostatistical modeling, spatial statistics, and statistical computing. Notable areas of interest include Gaussian random fields, copula models, and applications in environmental science and public health. He develops software tools such as geoCount and gcKrig for geostatistical analysis of count data. Education: Ph.D. in Statistics, University of Maryland M.S. in Water Resources, Universidad Simón Bolívar B.S. in Mathematics, Universidad Simón Bolívar Dr. De Oliveira is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. His teaching spans advanced statistical courses such as Spatial Statistics and Advanced Inference. He actively contributes to the statistics community through software development, editorial roles, and collaborative research projects. His recent work addresses spatial prediction, non-Gaussian spatial models, and applications in biomechanics and environmental monitoring. He emphasizes Bayesian methodologies and interdisciplinary collaborations to solve complex data-driven challenges.
