Machine Learning
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.
David Blei and Chong Wang were named winners of the Test of Time Award for Research at the 27th SIGKDD Conference on Knowledge Discovery and Data Mining. The duo was recognized for their 2011 paper, "Collaborative topic modeling for recommending scientific articles."
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 
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