David Blei, a Princeton University computer science associate professor who will soon be taking his talents to Columbia University, has been named the winner of the 2013 ACM-Infosys Foundation Award in Computing Sciences.
An expert in machine learning and Bayesian statistics, Blei has developed methods for analyzing large collections of data that has applications for everything from email archives to computational biology to social networks and robotics.
The ACM-Infosys Foundation Award recognizes the recent innovations by young scientists and system developers in the computing field and includes a $175,000 prize.
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Associaton for Computing Machinery (ACM) President Vint Cerf in a statement said that Blei’s contributions provided a basic framework for an entire generation of researchers to develop statistical modeling approaches. “His topic modeling algorithms go beyond the search and links approach to information retrieval. In an era of explosive data on the Internet, he saw the advantage of discovering the latent themes that underlie documents, and identifying how each document exhibits these themes. In fact, he changed the way machine learning researchers think about modeling text and other objects in the digital realm.”
Blei led research that resulted in a model dubbed LDA (Latent Dirichlet Allocation), a powerful tool for discovering and exploiting hidden "topics” or semantic themes among billions of documents with thousands of themes.
Blei will join Columbia University in the fall of 2014 as Professor of Statistics and 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.
Upon winning the Blavatnik Award, Blei said: “My goal is to build new statistical tools for discovering and exploiting the hidden patterns that pervade modern real-world data sets, enabling experts to quickly summarize, navigate and understand them.”
Blei earned a B.S. degree in Computer Science and Mathematics from Brown University, and a Ph.D. in Computer Science from the University of California, Berkeley.