About

I am a Scientific Investigator for Computational Biology at GlaxoSmithKline

Management Skills

  • Cross-functional team leadership: I have worked in Matrix teams across multiple projects as the Computational Biology lead and provided overall co-leadership to interdisciplinary projects. Communicated key insights to senior leadership to impact decision-making on early target portfolios

  • Strategic planning & Project Management: Set clear objectives and deliverables. Planned & executed computational biology experiments spread over > 1 year timescale. Managed talent resourcing, resolved blockers and met delivery timelines.

  • Scientific communication: All my projects have involved effectively communicating Computational Biology derived insights to leadership to influence decision making

Technical Skills

  • Single cell analysis: Expertise in single-cell RNAseq & ATACseq data analysis. Have performed data analyses for data QC, cell type annotation, data integration, trajectory inference, cell marker identification, differential gene expression analysis, transcriptional activity & gene regulation analysis

  • Genome scale pooled & Arrayed Perturbation screening data analysis: Expertise in high-throughput screening technologies like PerturbSeq/CROPseq, DRUGseq, PARSE, 10x & CRISPR-FACS-based functional genomics screens. Have performed data analyses for Hit identification, Target mechanism of action evaluation, Perturbation signature scoring etc

  • Analysis of data generated from large public genomic and epigenomic datasets: Expertise in analyzing data from RNAseq, CHIPseq, ATACseq, HiC, Methylation array, Microarray etc. Have worked extensively with data generated by consortium like TCGA, GTeX, ENCODE, BLUEPRINT, DepMap etc

  • Multi-omic data interpretation & integration: Expertise in analysing data generated from various NGS technologies - Data clustering, Pattern identification, Differential expression/activity/abundance analysis, Pathway analysis, Regulatory network inference, Multi-modal data integration, Biomarker discovery, Scoring molecular signatures, Patient stratification, Associating signatures to clinical outcomes, Data visualization etc

  • Disease Genotype-to-Phenotype association studies: Have expertise in integrating genetics (GWAS) and (epi-)genomics data using tools like sLDSC, MAGMA & scLinker to infer disease associated mechanisms

  • Machine Learning: Expertise in applying supervised and unsupervised machine learning to NGS datasets - Non-negative matrix factorization, data classification & feature selection etc

  • Reproducible research: Have experience in Git, Github, Quarto, Rmarkdown, Plotly, Shiny, Nextflow, Snakemake & Docker

  • Coding/Tools: Have experience in R statistical programming, Bioconductor, Python, Bash & Conda

Academic background

Postdoctoral Research

My postdoctoral research focused on identifying the enhancer-driven regulatory subtypes in Neuroblastoma and understanding its developmental origins

Doctoral Research (PhD)

My doctoral research was on understanding the reprogramming of the metabolic landscape in tumors

I was also involved in 5 different collaborations with experimental groups as lead computational biologist in topics related to non-coding RNA, immunotherapy, biomarker discovery and mechanism of action of a chemotherapeutic drug

MSc Genomics

For my Master’s thesis, I worked on the structure-based rational design of a peptide inhibitor against HIF1alpha-HRE binding and its structural studies. I performed computational protein structure modeling and molecular dynamics studies

For my internship project, I worked on the expression, purification, crystallization and in-silico modeling of the FadD9 protein from Mycobacterium tuberculosis. I performed cloning & transformation assays, protein isolation, western blotting, protein purification using chromatography techniques, protein crystallization and computational protein structure modeling