For contributions to the theory and practice of probabilistic topic modeling and Bayesian machine learning.Scroll Up
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.Scroll Up
ACM And Infosys Foundation Honor Leader In Machine Learning
David Blei is the recipient of the 2013 ACM-Infosys Foundation Award in the Computing Sciences. He initiated an approach to analyzing large collections of data using innovative statistical methods, known as "topic modeling," that make it possible to organize and summarize digital archives at a scale that would be impossible by human annotation. His work is scalable to collections of billions of documents and has inspired new research programs across multiple disciplines, with applications for email archives, natural language processing, information retrieval, computational biology, social networks, and robotics as well as computational social sciences and digital humanities.
ACM President Vint Cerf 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."
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.