About

The experience behind TensorOmics

Nearly two decades across pharma, genomics research, and academia.

Narayanan Raghupathy, PhD

Founder, TensorOmics LLC

I'm a computational biologist with nearly two decades of post-PhD experience spanning pharma, genomics research, and academia. I founded TensorOmics to help pharma and biotech teams turn complex biological data into actionable insights for drug discovery.

Most recently, I served as Senior Principal Scientist at Bristol Myers Squibb, where I supported discovery programs in cardiovascular, fibrosis, and immunology, and translational and reverse translational efforts in immunoscience and neuroscience — using internal, collaborative, biobank, and real-world datasets.

Before BMS, I was at The Jackson Laboratory — first developing new statistical genetics and genomics methods for RNA-seq, single-cell RNA-seq, and genotype data with Gary Churchill, then identifying causal mutations in naturally-occurring mouse mutants that arise during production at JAX — animal models of human rare diseases — using complementary sequencing technologies in the Genetic Resource Science group. Earlier, I worked with John Storey at Princeton University on statistical genomics, developing integrative methods for understanding the genetic architecture of gene expression in human populations.

I hold a PhD in Computational Biology from Carnegie Mellon University.

Selected publications

  • Hierarchical analysis of RNA-seq reads improves the accuracy of allele-specific expression (EMASE)Bioinformatics, 2018.
  • Defining the consequences of genetic variation on a proteome-wide scaleNature, 2016.
  • A Bayesian mixture model for the analysis of allelic expression in single cellsNature Communications, 2019.

Full publication list on Google Scholar →

What I bring to consulting

Pharma R&D8 years at BMS across translational bioinformatics, immuno-science, and neuroscience
Multi-OmicsDeep expertise in RNA-seq, scRNA-seq, proteomics, GWAS/PheWAS, eQTL/pQTL analysis
AI & Machine LearningApplying LLMs, generative AI, and ML to drug discovery and scientific workflows
Methods DevelopmentPublished novel algorithms (EMASE, scBASE, GBRS) used by the genomics community
PublicationsPublished in Nature, Cell Stem Cell, Genome Research, Nature Communications, and more