Solène Chabanier is a Quantitative Data Researcher based in Berkeley with eight years of experience applying computational science and machine learning to cosmology and large-scale data. Trained with a PhD from Université Paris-Saclay and strong computer-science credentials from Telecom ParisTech and Imperial College, she combines high-performance computing (CPU/GPU) expertise with Bayesian inference, Gaussian processes, and deep learning for scientific parameter inference. At Berkeley Lab she ran hundreds of hydrodynamical simulations (600k GPU hours, 30M CPU hours) and analyzed petabyte-scale outputs to refine models of dark matter, dark energy and the intergalactic medium. She co-led a 40+ person DESI working group and built production-ready pipelines and CNN-based tools to improve measurements from massive observational datasets. Now at Citadel, she brings that track record of designing scalable, validated ML and simulation-driven inference systems to quantitative research in finance. Beyond the numbers, she’s practiced at translating complex physics models into robust, auditable code and communicating results to diverse technical and public audiences.
8 years of coding experience
Master of Science (M.Sc.), Fundamental Physics, Distinction, Master of Science (M.Sc.), Fundamental Physics, Distinction at Imperial College London
Doctor of Philosophy - PhD, Computational Science, Astrophysics, Doctor of Philosophy - PhD, Computational Science, Astrophysics at Université Paris-Saclay
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