David M. Blei

Digital Library

ACM AAAI Allen Newell Award

USA - 2023

citation

For significant contributions to machine learning, information retrieval, and statistics, especially in the areas of topic models and approximate inference.

Within machine learning, David Blei has made important and numerous contributions to probabilistic graphical models, approximate posterior inference, causal inference, information retrieval, text processing, and Bayesian nonparametric statistics. He is best known for his seminal work on topic models (also known as latent Dirichlet allocation models). He has brought these techniques to the attention of a wide audience in computer science, in areas such as information retrieval, Web search, recommender systems, and computer vision. This work has also gained (and has retained) widespread attention in research areas outside computer science, particularly in the digital humanities and in computational social science. Topic models are, for example, routinely used to classify newspaper articles, as well as to perform sentiment analysis, and categorize emails.

David Blei has also had a major impact on the field of statistics. His research has brought approximate inference ideas from machine learning to the forefront, allowing statisticians to think about inference in a much broader context than in the past. In particular, he has promoted the use of optimization-based variational inference methods in addition to the usual methods such as Monte Carlo sampling. This has led to great gains in inference efficiency that has enabled innovative and widespread applications in research and industry in areas ranging from astrophysics, such as modelling the orbits of exoplanets, to financial forecasting.

 

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ACM Fellows

USA - 2015

citation

For contributions to the theory and practice of probabilistic topic modeling and Bayesian machine learning.

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ACM Prize in Computing

USA - 2013

citation

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

2023 ACM - AAAI Allen Newell Award

David Blei of Columbia University receives the ACM - AAAI Allen Newell Award.

Blei is recognized for significant contributions to machine learning, information retrieval, and statistics. His signature accomplishment is in the machine learning area of “topic modeling", which he pioneered in the foundational paper “Latent Dirichlet Allocation” (LDA). The applications of topic modelling can be found throughout the social, physical, and biological sciences, in areas such as medicine, finance, political science, commerce, and the digital humanities.

Blei has also been a leader in variational inference (VI), another research area that connects computer science to statistics. VI is an optimization-based methodology for approximate probabilistic inference. Blei’s major contribution to VI has been to develop a novel framework—stochastic variational inference (SVI)—that yielded a quantum leap in the size of problems that can be solved with VI. SVI is in wide use in the AI industry and across the sciences.

Additionally, in his work on discrete choice modelling, Blei has developed a methodology for answering counterfactual queries about changes in prices, which helps to identify complimentary and substitutable pairs of products. This work has built a bridge between computer science and econometrics and has been cited for its impactful use of machine learning modeling.

ACM Fellows Named For Computing Innovations That Are Advancing Technology In The Digital Age

ACM has recognized 42 of its members for their significant contributions to the development and application of computing in areas from data management and spoken-language processing to robotics and cryptography. The achievements of the 2015 ACM Fellows are fueling advances in computing that are driving the growth of the global digital economy.

"Whether they work in leading universities, corporations, or research laboratories, these newly minted ACM Fellows are responsible for the breakthroughs and industrial innovations that are transforming society at every level," explains ACM President Alexander L. Wolf.  "At times, the contributions of a Fellow may include enhancements to a device that immediately impacts our daily lives. At other times, new research discoveries lead to theoretical advances that, while perhaps not immediately perceptible, have substantial long-term impacts."

The 2015 ACM Fellows have been cited for contributions to key computing fields including software research, data mining, computer graphics, computer and mobile systems, system security, multiprocessor and memory architecture design, and research in sensor networks.

ACM will formally recognize the 2015 ACM Fellows at its annual Awards Banquet in June 2016 in San Francisco, California.

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."

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Background

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.