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9 ICML Papers You Should Read

Did you miss ICML? Here are some cool papers from this year's conference. Read More ›

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About Our Lab

We work on a wide range of machine learning problems that are motivated by challenges from modeling messy data in the wild. We develop new algorithms and theories in deep learning, unsupervised learning, robust ML, adaptive data analysis, etc. We are also very interested in applications in genomics, health and biotech. Our group is affiliated with Biomedical Data Science, CS and EE at Stanford and is a part of the Chan-Zuckerberg Biohub.