Guadalupe Gonzalez is a Senior Machine Learning Scientist with eight years of experience specializing in graph deep learning and causal inference for drug discovery, currently on the Prescient Design team at Genentech/Roche. She holds a PhD from Imperial College London where she applied geometric ML to therapeutic lead discovery, and she has bridged academia and industry through a visiting stint at Harvard Medical School. Guadalupe focuses on causal graph models that span molecular to patient-level data, translating cutting-edge research into cross-functional drug discovery efforts. She’s also a committed advocate for women’s health, leading endometriosis initiatives across Genentech and Roche. Notably, her background blends top-of-class engineering training with hands-on biomedical data analysis, from RNA-seq earlier in her career to state-of-the-art geometric deep learning today.
9 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - Computing Graph deep learning for therapeutic lead discovery, Doctor of Philosophy - Computing Graph deep learning for therapeutic lead discovery at Imperial College London
Bachelor's Degree in Biomedical Engineering, Bachelor's Degree in Biomedical Engineering at Universidad Politécnica de Madrid
Contributions:24 commits, 18 pushes, 1 branch in 6 months
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