Scholar
Sarah Filippi
Google Scholar ID: HhMJevQAAAAJ
Reader, Imperial College London
Citations & Impact
Citations
2,593
H-index
24
i10-index
36
Publications
20
Co-authors
23
list available
Publications
Resume (English only)
Research Experience
- Professor of Statistical Machine Learning at Imperial College London
- Leads a research group developing Bayesian nonparametric approaches for causal inference, pattern recognition, variable importance, and model selection in high-dimensional settings
- Works on decision-making under uncertainty, including reinforcement learning, experimental design, and Bayesian optimization
- Develops kernel methods and scalable Gaussian processes
- Collaborates with domain experts (e.g., biologists, epidemiologists, clinicians) to adapt state-of-the-art methods to real biomedical applications such as identifying survival-related genes, relevant OTUs in microbiome, building complex longitudinal models, and incorporating uncertainty quantification in pharmacological chemometrics
- Focuses on understanding mechanisms of chronic diseases (e.g., cancer, diabetes, cardiovascular and respiratory diseases like asthma) and identifying causal (genetic or environmental) risk factors or diagnostic tools
Background
- Professor of Statistical Machine Learning, Department of Mathematics, Imperial College London
- Joint Director of the EPSRC Centre for Doctoral Training in Statistics and Machine Learning at Imperial and Oxford
- Broad research interests in statistical modelling and machine learning methods, their theoretical properties, and applications to biomedical problems
- Particularly interested in Bayesian statistics and nonparametric methods
- Research group focuses on developing statistical machine learning and computational statistics methods motivated by applications in computational biology, biomedical genetics, pharmacology, epidemiology, and clinical studies
- Aims to address large-scale real-world health problems and understand complex biological systems in health and disease through novel statistical and computational approaches
