Summary
Saikat Banerjee is a Staff Scientist in computational genomics with 10+ years of experience translating biobank-scale genetic and multi-omics data into quantitative evidence for disease mechanism discovery and decision-making. He combines deep expertise in statistical genetics, Bayesian modeling, optimization and regression with hands-on work on UK Biobank, All of Us, GTEx, bulk and single-cell transcriptomics, and neuropsychiatric and cardiovascular disease cohorts. His background spans theory and practice—from a PhD in statistical mechanics and molecular modeling to developing variational empirical Bayes and Bayesian multiple regression methods during postdoctoral work and staff roles. Saikat has maintained Linux HPC systems, built reproducible Snakemake and cloud-ready pipelines, contributed to open-source tools and high-impact publications, and advised a genomics startup on genetically informed patient stratification. He is notable for bridging rigorous Bayesian theory with scalable pipelines that move post-GWAS insights toward translational impact.
10 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Statistical Mechanics and Computational Chemistry, Doctor of Philosophy (Ph.D.), Statistical Mechanics and Computational Chemistry at Indian Institute of Science
B.Sc, Chemistry, B.Sc, Chemistry at Ramakrishna Mission Vidyamandira, University of Calcutta
English, Bengali, Hindi, German