Dr Sara Wade is a Reader in Statistics and Data Science who uses flexible data-driven methods in healthcare applications. Sara's research interests include statistics, machine learning, Bayesian analysis, with a focus on flexible methodology and efficient inference for complex data. Her specific interests are nonparametrics, mixtures, clustering, regression, and dimension reduction, along with scalable methods and algorithms for complex, high-dimensional data, and interdisciplinary applications, particularly in biomedical studies. Sara's AI research is applicable to genomics, imputation, diagnosis, imaging and longitudinal data. Current AI projectsUnveiling Brain Connectivity: novel statistical frameworks for high-throughput neuroanatomyWe are establishing a high-throughput neuroanatomy pipeline for large-scale, single-neuron resolution data to uncover structural changes in connectivity underlying brain disorders.Awards and fellowshipsAwarded a Turing Fellowship 2024-2026Co-Investigator for the UK Hub on Probabilistic AIGet in touchPlease visit Sara's research website:Sara Wade | Owlstown Recent publications using AI techniquesMapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences | Science AdvancesHBMAP: Bayesian inference of neural circuits from DNA barcoded projection mapping | bioRxiv Leveraging Variational Autoencoders for Multiple Data Imputation | Joint European Conference on Machine Learning and Knowledge Discovery in DatabasesUnderstanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks | Proceedings of the ECML PKDD Variational Bayesian Bow tie Neural Networks with Shrinkage | arXiv This article was published on 2025-09-04
