David Blei is a highly respected figure in the field of computer science. He obtained his BSc in Computer Science and Mathematics from Brown University and his Ph.D. in Computer Science from the University of California, Berkeley. He is best known for his pioneering work on probabilistic topic models, and machine-learning techniques used to uncover the underlying topics in a collection of documents. Blei and his collaborators developed the widely used latent Dirichlet allocation (LDA) algorithm over ten years ago. Since then, Blei's research group has significantly expanded the scope of topic modeling. He and his students have developed robust methods for simultaneously analyzing documents and user behavior, such as scientists reading articles or lawmakers voting on bills. These new methods capture interpretable readership patterns and can be used to recommend documents, characterize readers, and organize collections according to content and consumption.

In addition to his work on topic modeling, Blei has also made significant contributions to other areas of computer science. He has developed models of social networks, music and audio, images and computer vision, and neuroscience and brain activity.

He and his students have recently developed generic and efficient algorithms to fit a broad class of statistical models to massive data sets. This has changed the scale at which we can apply sophisticated methods for data science, making it possible to analyze and draw insights from massive amounts of data that would have been impossible to process before. Blei's work has profoundly impacted computer science and paved the way for many essential breakthroughs in machine learning and data science.