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The success of large language models is driven by the abundance and natural structure of data. What does this tell us about our universe and ourselves? How can we use these insights to advance applications in critical fields like drug discovery? Chris asks questions like these and is interested in building machine learning systems that learn in stubbornly complex settings relevant to medicine. He received the Corcoran Memorial Prize for his Oxford thesis and has received a number of paper awards at the top machine learning conferences. Chris is known for his gradient estimation techniques, which are now standard tools in the deep learning toolbox, and for his role as a founding member of the AlphaGo project, which was the first computer program to defeat a world champion in the game of Go.