Imperial College London

DrSarahFilippi

Faculty of Natural SciencesDepartment of Mathematics

Senior Lecturer in Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8562s.filippi

 
 
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Location

 

523Huxley BuildingSouth Kensington Campus

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Summary

 

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.

COMPUTATIONAL STATISTIC METHODS

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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

BIOMEDICAL PROBLEMS

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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