Sarah Filippi

Sarah Filippi

Statistical machine learning and Bayesian statistics motivated by applications in biomedicine

I am a Lecturer in Biostatistics at Imperial College London. I hold a joint position between the Statistic Section of the Department of Mathematics and the Department of Epidemiology and Biostatistics. The core of my research lies in statistical machine learning and computational statistics methodology motivated by applications in and around computational biology and biomedical genetics. I am particularly interested in addressing how novel statistical and computational approaches and algorithms can aid in the analysis of large-scale real-world biomedical data.



  • Quin F. Wills, Esther Mellado-Gomez, Rory Nolan, Damien Warner, Eshita Sharma, John Broxholme, Benjamin Wright, Helen Lockstone, William James, Mark Lynch, Michael Gonzales, Jay West, Anne Leyrat, Sergi Padilla-Parra, Sarah Filippi, Chris Holmes, Michael D. Moore, Rory Bowden, The nature and nurture of cell heterogeneity: accounting for macrophage gene-environment interactions with single-cell RNA-Seq, BMC Genomics, 2017.
  • Q. Zhang, S. Filippi, A. Gretton, D. Sejdinovic, Large-Scale Kernel Methods for Independence Testing, Statistics and Computing, to appear, 2017.
    Project: bigbayes


  • Sarah Filippi, Chris P Barnes, Paul D W Kirk, Takamasa Kudo, Katsuyuki Kunida, Siobhan S McMahon, Takaho Tsuchiya, Takumi Wada, Shinya Kuroda, Michael P H Stumpf, Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling, CellReports, 2016.
  • Sarah Filippi, Chris C Holmes, Luis E. Nieto-Barajas, Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures, Electronic Journal of Statistics, 2016.
  • Sarah Filippi, Chris C Holmes, A Bayesian Nonparametric Approach to Testing for Dependence Between Random Variables, Bayesian Analysis, 2016.
  • S. Flaxman, D. Sejdinovic, J.P. Cunningham, S. Filippi, Bayesian Learning of Kernel Embeddings, in Uncertainty in Artificial Intelligence (UAI), 2016, 182–191.
    Project: bigbayes


  • Siobhan S Mc Mahon, Oleg Lenive, Sarah Filippi, Michael P H Stumpf, Information processing by simple molecular motifs and susceptibility to noise, Journal of The Royal Society Interface, 2015.


  • Siobhan S Mc Mahon, Aaron Sim, Sarah Filippi, Robert Johnson, Juliane Liepe, Dominic Smith, Michael P H Stumpf, Information theory and signal transduction systems: From molecular information processing to network inference., Seminars in cell & developmental biology, 2014.
  • Adam L MacLean, Sarah Filippi, Michael P H Stumpf, The ecology in the hematopoietic stem cell niche determines the clinical outcome in chronic myeloid leukemia, PNAS, 2014.
  • Juliane Liepe, Paul Kirk, Sarah Filippi, Tina Toni, Chris P Barnes, Michael P H Stumpf, A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation., Nature Protocols, 2014.


  • Daniel Silk, Sarah Filippi, Michael P H Stumpf, Optimizing threshold-schedules for sequential approximate Bayesian computation: applications to molecular systems., Statistical Applications in Genetics and Molecular Biology, 2013.
  • Sarah Filippi, Chris P Barnes, Julien Cornebise, Michael P H Stumpf, On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo., Statistical Applications in Genetics and Molecular Biology, 2013.
  • Juliane Liepe, Sarah Filippi, Michał Komorowski, Michael P H Stumpf, Maximizing the Information Content of Experiments in Systems Biology, PLoS computational biology, 2013.


  • Anindita Roy, Gillian Cowan, Adam J Mead, Sarah Filippi, Georg Bohn, Aristeidis Chaidos, Oliver Tunstall, Jerry K Y Chan, Mahesh Choolani, Phillip Bennett, Sailesh Kumar, Deborah Atkinson, Josephine Wyatt-Ashmead, Ming Hu, Michael P H Stumpf, Katerina Goudevenou, David O’Connor, Stella T Chou, Mitchell J Weiss, Anastasios Karadimitris, Sten Eirik Jacobsen, Paresh Vyas, Irene Roberts, Perturbation of fetal liver hematopoietic stem and progenitor cell development by trisomy 21., Proceedings of the National Academy of Sciences, 2012.
  • Chris P Barnes, Sarah Filippi, Michael P H Stumpf, Thomas Thorne, Considerate approaches to constructing summary statistics for ABC model selection, Statistics and Computing, 2012.


  • Sarah Filippi, Olivier Cappe, Aurelien Garivier, Optimally Sensing a Single Channel Without Prior Information: The Tiling Algorithm and Regret Bounds, IEEE Journal of Selected Topics in Signal Processing, 2011.


  • S Filippi, O Cappe, A Garivier, C Szepesvari, Parametric bandits: The generalized linear case, in Neural Information Processing Systems (NIPS’2010), 2010.
  • Sarah Filippi, Olivier Cappe, Aurelien Garivier, Optimism in reinforcement learning and Kullback-Leibler divergence, in 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2010.


  • Sarah Filippi, Olivier Cappe, Fabrice Clerot, Eric Moulines, A Near Optimal Policy for Channel Allocation in Cognitive Radio, in Lecture Notes in Computer Science, Recent Advances in Reinforcement Learning, Springer, 2008.