Chen Li is a bioinformatics scientist with 8 years of experience translating single-cell multi-omic data into mechanistic insights about gene regulation. With a Ph.D. in Computational Medicine & Bioinformatics from the University of Michigan, Chen developed ODE- and deep learning–based dynamical models and a cVAE RNA velocity framework that improve cell fate prediction and enable in silico perturbation analysis across many tissues. He pairs method development with practical engineering, building scalable Nextflow and Snakemake pipelines and applying them to real-world problems such as discovering HIV integration events from mixed human/HIV read mapping. Skilled in Python, R, PyTorch, and modern ML tooling, he bridges computational and experimental teams to turn complex datasets into testable biological stories. Notably, his work quantifies time lags between chromatin accessibility and transcription and produces Bayesian tests for dynamic gene properties, revealing temporal regulatory patterns where transcription factor expression often precedes motif accessibility. Now based in Palo Alto and working at Boston Children’s Hospital, he focuses on reproducible, impactful bioinformatics that drives biological discovery.
8 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at University of Michigan
University of California, San Diego
Associate of Arts, Natural Sciences, Associate of Arts, Natural Sciences at Pasadena City College
Doctor of Philosophy, Medicine, Doctor of Philosophy, Medicine at University of Michigan - Ann Arbor
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