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.

Publications

2017

  • Q. F. Wills, E. Mellado-Gomez, R. Nolan, D. Warner, E. Sharma, J. Broxholme, B. Wright, H. Lockstone, W. James, M. Lynch, M. Gonzales, J. West, A. Leyrat, S. Padilla-Parra, S. Filippi, C. Holmes, M. D. Moore, R. Bowden, The nature and nurture of cell heterogeneity: accounting for macrophage gene-environment interactions with single-cell RNA-Seq, BMC Genomics, 2017.
  • S. Filippi, C. C. Holmes, . others, A Bayesian nonparametric approach to testing for dependence between random variables, Bayesian Analysis, 2017.
  • Q. Zhang, S. Filippi, S. Flaxman, D. Sejdinovic, Feature-to-Feature Regression for a Two-Step Conditional Independence Test, in Uncertainty in Artificial Intelligence (UAI), 2017.
    Project: bigbayes
  • Q. Zhang, S. Filippi, A. Gretton, D. Sejdinovic, Large-Scale Kernel Methods for Independence Testing, Statistics and Computing, to appear, 2017.
    Project: bigbayes

2016

  • S. Filippi, C. P. Barnes, P. D. W. Kirk, T. Kudo, K. Kunida, S. S. McMahon, T. Tsuchiya, T. Wada, S. Kuroda, M. P. H. Stumpf, Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling, CellReports, 2016.
  • S. Filippi, C. C. Holmes, L. E. Nieto-Barajas, Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures, Electronic Journal of Statistics, 2016.
  • S. Filippi, C. C. Holmes, A Bayesian Nonparametric Approach to Testing for Dependence Between Random Variables, Bayesian Analysis, 2016.
  • S. Filippi, C. C. Holmes, L. E. Nieto-Barajas, . others, Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures, Electronic Journal of Statistics, vol. 10, no. 2, 3338–3354, 2016.
  • S. Flaxman, D. Sejdinovic, J. Cunningham, S. Filippi, Bayesian Learning of Kernel Embeddings, in Uncertainty in Artificial Intelligence (UAI), 2016, 182–191.
    Project: bigbayes

2015

  • S. S. M. Mahon, O. Lenive, S. Filippi, M. P. H. Stumpf, Information processing by simple molecular motifs and susceptibility to noise, Journal of The Royal Society Interface, 2015.

2014

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

2013

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

2012

  • A. Roy, G. Cowan, A. J. Mead, S. Filippi, G. Bohn, A. Chaidos, O. Tunstall, J. K. Y. Chan, M. Choolani, P. Bennett, S. Kumar, D. Atkinson, J. Wyatt-Ashmead, M. Hu, M. P. H. Stumpf, K. Goudevenou, D. O’Connor, S. T. Chou, M. J. Weiss, A. Karadimitris, S. E. Jacobsen, P. Vyas, I. Roberts, Perturbation of fetal liver hematopoietic stem and progenitor cell development by trisomy 21., Proceedings of the National Academy of Sciences, 2012.
  • C. P. Barnes, S. Filippi, M. P. H. Stumpf, T. Thorne, Considerate approaches to constructing summary statistics for ABC model selection, Statistics and Computing, 2012.

2011

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

2010

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

2008

  • S. Filippi, O. Cappe, F. Clerot, E. Moulines, A Near Optimal Policy for Channel Allocation in Cognitive Radio, in Lecture Notes in Computer Science, Recent Advances in Reinforcement Learning, Springer, 2008.