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.
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).
Kaffes was selected as part of the inaugural cohort in recognition of the impact and potential of his work on tail-latency scheduling.
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 
Add to GMail