Guillaume Treut is a physicist-turned-senior scientist based in San Francisco with eight years of experience building numerical algorithms and production-grade software at the intersection of computational physics, biology, and machine learning. He has a proven track record of turning advanced mathematical models into scalable code— from C++ gradient-descent solvers and OpenMP-parallelized reconstructions of 50,000+ variable systems to U-Net based computer vision pipelines that processed tens of thousands of cells. At Chan Zuckerberg Biohub he led novel stochastic-bridge methods for cell-fate inference and epidemic flux modeling that informed mitigation policy, and he continues to apply that blend of theory and implementation at Quantum-Si. Comfortable on HPC clusters and in production environments, he mentors early-career researchers and brings an uncommon appetite for biological problems despite a deep physics pedigree.
8 years of coding experience
3 years of employment as a software developer
Master of sciences and executive engineering Engineering, Master of sciences and executive engineering Engineering at Mines Paris - PSL
Mathematics ans Physics, Mathematics ans Physics at Lycée Saint-Geneviève
Doctor of Philosophy - PhD Physics, Doctor of Philosophy - PhD Physics at Université Paris-Saclay
Simulations of a single semi-flexible polymer using either the Monte-Carlo algorithm or Langevin dynamics.
Contributions:1 review, 1 PR, 20 pushes in 3 years 10 months
physicssemisimulationssimulationmonte-carlo
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