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Yang Ning is an Associate Professor in the Department of Statistics and Data Science at Cornell University. His research focuses on developing statistical methods and theories to quantify uncertainty, particularly in modern data sets characterized by high dimensionality, complexity, and heterogeneity. He enjoys working at the interface of mathematical statistics and machine learning, as well as stochastic optimization. Currently, he is particularly interested in developing a statistical foundation for machine learning methods and causal inference, bridging the gap between statistical theory and its application in machine learning.
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