David M. Blei

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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.
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