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Chongli Qin is a research scientist at DeepMind. Her primary interest is in building safer, more reliable, and more trustworthy machine learning algorithms. Over the past several years, she has contributed in developing algorithms to make neural networks more robust and capable of handling noise. Key parts of her research focuses on functional analysis: properties of neural network that can naturally enhance robustness. She has also contributed in building mathematical frameworks to verify/guarantee that certain properties hold for neural networks. Prior to DeepMind, Chongli studied in Cambridge, where she focused on the mathematics tripos and scientific computing before doing a PhD in bioinformatics.