For pioneering the area of topic modeling, which has had profound influence on machine learning foundations as well as industrial practice.

David Blei is a leader in the area of large data analysis through machine learning. One of his central contributions is the rigorous treatment of the area of topic modeling, wherein a large data set is mathematically decomposed into latent themes. His approach is based on hierarchical Bayesian models using hidden variables, together with efficient computational methods for estimating model parameters. This work has inspired the work of many others, resulting in new research programs, workshops, and graduate courses. Blei is especially well known for his leadership on the landmark paper (with Jordan and Ng) on Latent Dirichlet Allocation. This work has been cited thousands of times and is used in a range of applications including document summarization, indexing, genomics and image database analysis. It has had definitive impact in a number of web applications at companies ranging from startups to internet giants.

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