Greater Philadelphia
• 20+ years of experience in developing, implementing, and applying computational and statistical methods for high-throughput genetics, genomics, proteomics, and metabolomics data analyses in molecular biology research and drug discovery • Collaborating with other scientists in a matrix environment supporting all phases of the drug discovery pipeline and communicating computational and statistical methods, analyses and results to diverse project teams • Strong technical background in computational biology, biostatistics, and computer science with extended experience in multiple therapeutic areas including skin, muscle, metabolic, cancer, and inflammatory diseases • Technical skills include programming languages (R/Bioconductor, Perl, Java, SQL), ‘omics analysis techniques (transcriptomics, proteomics, metabolomics), pathway and network methods, Bayesian modeling, machine learning • Over 35 peer-reviewed publications • PhD in bioinformatics
Leading multi-disciplinary biomarker analytics team based in U.S. and U.K. to support clinical trials and translational research at GSK.
Collaborating with other scientists in a matrix environment supporting all phases of the drug discovery pipeline across multiple therapeutic areas with particular focus on metabolic, muscle, skin, and inflammatory diseases; managing direct reports; designing and implementing integrative statistical analyses of high-throughput genetic, genomics, proteomics, and metabolomics data from internal and external sources, and interpreting and communicating results; literature mining and integration of large-scale biomedical data for prioritization of new drug targets and biomarkers; determine potential new disease indications using text mining and connectivity map approaches; engaging with external academic collaborators in multiple early-phase target discovery and validation projects.
Statistical analyses of high-throughput genomics and metabolomics data, with particular focus on metabolic and enteroendocrine disease areas.
Statistical methods for joint analyses of high-throughput genomics data, including of gene expression microarray and DNA sequencing data with particular focus on stem cell maintenance and lineage specific genes in mouse embryonic stem cells; Bioinformatics analyses of microarray and ChIP-Seq data in collaborative research projects.
Statistical methods for functional enrichment analysis, Bayesian infinite mixture models for clustering of high-throughput genomics data, differential gene co-expression, statistical analyses of microarray and DNA sequencing data
Microarray platform comparison, statistical analyses of DNA microarray data, probe re-annotation, pre-processing strategies
Tools for microarray probe sequence validation and mapping, improving statistical models to decode BeadArray probe addresses