Machine Learning
Professor David Blei, with co-authors Matthew Hoffman and Francis Bach, is recognized with a Test of Time Award at NeurIPS, the world’s top machine learning conference, for scaling his topic modeling algorithm to billions of documents.
Topic models are algorithms that uncover hidden thematic structures in document collections. They help develop new ways to search, browse and summarize large archives of texts.
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).
About
The group does research on foundational aspects of machine learning — including causal inference, probabilistic modeling, and sequential decision making — as well as on applications in computational biology, computer vision, natural language and spoken language processing, and robotics.
It is part of a broader machine learning community at Columbia that spans multiple departments, schools, and institutes. Activities include seminars on statistical machine learning, several student-led reading groups and social hours, and participation in local events such as the New York Academy of Sciences Machine Learning Symposium.
Causal Artificial Intelligence Lab 
Machine Learning @ Columbia 
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