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.

Press Release

An associate professor at Princeton University, David Blei has written extensively on topic modeling and his pursuit of new statistical tools for discovering and exploiting the hidden patterns that pervade modern, real-world data sets. He will join Columbia University in the fall of 2014 as a Professor of Statistics and Computer Science. He will also be a member of Columbia's Institute for Data Sciences and Engineering.

Blei is a recipient of an NSF CAREER Award, an Alfred P. Sloan Fellowship, and the NSF Presidential Early Career Award for Scientists and Engineers. His recognitions also include the Office of Naval Research Young Investigator Award and the New York Academy of Sciences Blavatnik Award for Young Scientists. Blei earned a B.Sc. degree in Computer Science and Mathematics from Brown University, and a Ph.D. degree in Computer Science from the University of California, Berkeley.