Flor de María is a Senior Data Scientist and PhD candidate at the Research Center in Mathematics (CIMAT), specializing in machine learning and predictive modeling. With over 10 years of experience, her work combines statistical rigor with practical applications in public policy, health, environmental systems, and market research.
She is actively involved in the mathematics community, sharing her data science expertise at events for organizations like the Sociedad Matemática Mexicana. Flor de María also dedicates time to mentoring students, guiding them through real-world data challenges in collaboration with industry partners.
Her doctoral research focuses on advanced statistical learning, specifically on kernel methods for distributional data and optimal transport.
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