Sohit Miglani is a PhD student in Quantitative/Computational Biology at Princeton who transitioned from mathematics to experimental and computational biology, bringing eight years of cross-disciplinary experience in genomics, single-cell sequencing, ribosome profiling, and causal inference. He combines robust software skills (Python, R, SQL, Docker, cloud and grid computing) with hands-on wet-lab expertise including mouse work and CLIP/Ribo-seq, enabling end-to-end design and analysis of biological experiments. Sohit has built production-ready tools—most notably Atlantis, an open-source RNA-seq analysis app—and optimized large-scale pipelines for genomics projects, achieving measurable performance gains in somatic variant modeling. He also mentors and teaches, having supported teaching programs at Princeton and Minerva to improve STEM pedagogy. Notably, his work spans from algorithmic acceleration of data pipelines to probing autonomous intramolecular protein behaviors under advisors Mona Singh and Ned Wingreen, reflecting a rare blend of computational rigor and biological curiosity.
8 years of coding experience
1 year of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Minerva University
Doctor of Philosophy - PhD, Computational Biology (QCB), Doctor of Philosophy - PhD, Computational Biology (QCB) at Princeton University
Contributions:37 commits, 2 PRs, 38 pushes in 6 months
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