Machine Learning
Blei is recognized for his groundbreaking work in machine learning, in particular his field-defining contributions in the areas of topic models and stochastic variational inference.
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.
Papers from CS researchers have been accepted to the 38th International Conference on Machine Learning (ICML 2021).
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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