Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Reader in Statistical Machine Learning



+44 (0)20 7594 8562s.filippi




523Huxley BuildingSouth Kensington Campus




Research area

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.


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In terms of statistical methods, my research interests include:

  • Measures of association and causality using Non-parametric Bayesian statistics and kernel mean embedding: Polya Tree, Dirichlet process mixtures, Reproducing kernel Hilbert space
  • Bayesian inference procedures: Sequential Monte-Carlo methods, intractable likelihood, Approximate Bayesian Computation
  • Decision processes under uncertainty: exploration-exploitation trade-off, stochastic bandit problems, policies based on upper confidence bounds, reinforcement learning, optimism in face of uncertainty


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Instances of application to molecular biology and clinical data:

  • Single-cell genomics: cellular heterogeneity functional analysis and causality
  • Stem cell differentiation process in health and disease: perturbation of haematopoietic stem and progenitor cell development by trisomy 21, ecology of the stem cell niche in cancer
  • Personalised medicine: diagnostic, prognostic and response to treatment.
  • Epidemiology
  • Systems biology for biomedicine: mathematical model of biological systems, cellular information processing, Bayesian experimental design