Sarah Filippi

Dr. Sarah Filippi joined the Department of Statistics of Oxford University in June 2014. She previously held a Medical Research Council Fellowship (2011-2104) in the Theoretical Systems Biology group at Imperial College London where she worked on a range of topics in computational statistics focused on understanding biological processes and their relation to disease. Prior to this she studied mathematics and stochastic processes at the University Denis Diderot in Paris (France) and completed her PhD in 2010 in reinforcement learning and parametric bandit models at LTCI, a joint lab of TELECOM ParisTech and CNRS.

Dr. Filippi’s main research interests are related to the use of mathematical modelling, statistical machine learning and computational statistics to gain insight into biological processes and their role in diseases. She has a particular interest in addressing computational and statistical challenges in the analysis of genomic, transcriptomic and proteomic data at a single-cell level.

Selected Publications

• S. Flaxman, D. Sejdinovic, J. P. Cunningham, and S. Filippi, Bayesian Learning of Kernel Embeddings, in Uncertainty in Artificial Intelligence (UAI), 2016.
• MacLean, A.L., Filippi, S. and Stumpf, M.P.H. (2014) ‘The ecology in the hematopoietic stem cell niche determines the clinical outcome in chronic myeloid leukemia’, Proceedings of the National Academy of Sciences, 111(10), pp. 3883–3888.
• Silk, D., Filippi, S. and Stumpf, M.P.H. (2013) ‘Optimizing threshold-schedules for sequential approximate Bayesian computation: Applications to molecular systems’, Statistical Applications in Genetics and Molecular Biology, 12(5).
• Filippi, S., Barnes, C.P., Cornebise, J. and Stumpf, M.P.H. (2013) ‘On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo’, Statistical Applications in Genetics and Molecular Biology, 12(1), pp. 87–107. doi: 10.1515/sagmb-2012-0069.
• Liepe, J., Filippi, S., Komorowski, M. and Stumpf, M.P.H. (2013) ‘Maximizing the information content of experiments in systems biology’, PLoS Computational Biology, 9(1), pp. 1–13. doi: 10.1371/journal.pcbi.1002888.
• Filippi, S., Cappé, O., Garivier, A. and Szepesvári, C. (2010) ‘Parametric bandits: The generalized linear case’, Neural Information Processing Systems.