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What do you do with Data Science?

Much of Andrew's work has been broadly within data science, including both its statistical and algorithmic aspects. This includes his work on determination of minimax rates of estimation, on properties of the minimum description-length principle, in artificial neural network approximation and estimation, and in statistical formulations of communication channel coding and decoding. Current interests explore the evolution of the distribution of parameters used in fitting deep nets and mixture models, by both deterministic and stochastic transition rules. The aim is to find rules of parameter evolution that are provably fast and have provably accurate generalization.

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