Skills

R

90%

Software carpentry

90%

Statistics

70%

Immunology

70%

Infectious diseases

70%

Experience

 
 
 
 
 

Postdoctoral Researcher - Computational Genomics

University of Oxford

Sep 2017 – Present Oxford
Responsibilities include:

  • Study the role of thymic epithelial cells (TEC) in shaping the T vell receptor (TCR) repertoire (consortium funded by Wellcome Trust Strategic Award)
  • Study contest-specific changes in immune cell abundance and phenotype that underlie chronic inflammation (collaboration with the Powrie group)
  • Write and maintain software pipelines and programs for data analysis
 
 
 
 
 

Postdoctoral Researcher - Computational Biology

University of Oxford

Dec 2016 – Sep 2017 Oxford
Responsibilities include:

  • Management, quality control, and integration of pan-genomic datasets encompassingn RNA-seq, ChIP-seq, DNAse/FAIRE/ATAC-seq, Capture-C, microarrays, whole-genome sequencing and targeted re-sequencing
  • Study transcriptional and epigenetic responses to hypoxia and their relation to cancer biology
  • Experimental design, pipeline development, and publication
 
 
 
 
 

Research Associate - Bioinformatics / Biostatistics

Imperial College London

Sep 2015 – Nov 2016 London
Responsibilities include:

  • Analyze genetic, proteomic and metabolomics data and associated phenotype data
  • Integrate and analysis of large-scale, complex, multi-sources medical data and multi-variate data
  • Generate new knowledge on idiopathic pulmonary arterial hypertension (IPAH) disease aetiology, underlying biochemical pathways and mechanisms, to improve patient stratification, preventive strategies and treatments

Accomplish­ments

Machine Learning

Machine learning is the science of getting computers to act without being explicitly programmed.
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Python for Genomic Data Science

This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University.
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Command Line Tools for Genomic Data Science

Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
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Bioconductor for Genomic Data Science

Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.
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Statistics for Genomic Data Science

An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
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Network Analysis in Systems Biology

An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction.
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