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
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.
- Systems biology for biomedicine: mathematical model of biological systems, cellular information processing, Bayesian experimental design