معرفی
Sarah Filippi is a Professor and Reader in Statistical Machine Learning at the Department of Mathematics, Imperial College London, and Joint Director of the EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML) at Imperial and Oxford. She holds affiliations with multiple interdisciplinary networks, including the CNRS-Imperial Abraham de Moivre UMI, AI for Healthcare, and the Wound Healing and Regeneration Network. Her research focuses on Bayesian methods, nonparametric approaches, and their applications in biomedical problems, including systems biology, pharmacology, and epidemiology.
Dr. Filippi earned her Ph.D. in Statistical Machine Learning from Télécom ParisTech in 2010. She has held roles at Imperial College London (2011–2014 as an MRC Fellow, 2017–present in various senior positions) and the University of Oxford (2014–2017). Her work bridges statistical theory and practical biomedical challenges, emphasizing uncertainty quantification and scalable algorithms.
Her research group develops methods for causal inference, kernel-based learning, and decision-making under uncertainty. Recent publications highlight contributions to clustering algorithms, reinforcement learning, and epidemiological studies. She has received the Medical Research Council Fellowship (2011–2014) and leads collaborative projects with clinicians and computational biologists.
Dr. Filippi advises a vibrant research group, including Ph.D. students working on continual learning, Bayesian optimization, and healthcare applications. Her interdisciplinary collaborations span computational biology, chronic disease modeling, and material flow analysis, reflecting her commitment to translational research.
