ProfessorSarah Filippi
Professor of Statistical Machine Learning
Department of Mathematics - Faculty of Natural Sciences
Orcid identifier0000-0001-8652-358X (opens in a new tab)
- Professor of Statistical Machine LearningDepartment of Mathematics - Faculty of Natural Sciences
- 020 7594 8562 (Work)
- 523, Huxley Building, South Kensington Campus, United Kingdom
RESEARCH
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
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
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
COMPUTATIONAL STATISTIC METHODS
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
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