Tristan Brown is a Senior Machine Learning Researcher in San Jose with a decade of cross-disciplinary experience marrying inorganic chemistry and data science to solve hard scientific problems. He transitioned from air-sensitive chemical synthesis and DFT thermodynamics—work that produced a patent and a major DOE grant—into building and deploying ML models for proteomics at Seer. Comfortable across the stack, Tristan has written production scientific software and ETL tooling, containerized pipelines with CI/CD, and applied deep learning to connect chemical intuition with biological data. He brings a rare combination of hands-on laboratory discovery (including uncovering a new Ru2 ground state) and practical engineering for scalable data systems, making him adept at turning surprising experimental results into predictive, reproducible models.
10 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy (PhD) Inorganic Chemistry, Doctor of Philosophy (PhD) Inorganic Chemistry at University of Wisconsin-Madison
Contributions:206 pushes, 4 branches, 10 tags in 4 years 3 months
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