Machine Learning
Richard Zemel and Toniann Pitassi were recognized for their paper, "Learning Fair Representations," which established the subfield of machine learning–machine learning and fairness.
Researchers from our department showcased their work at NeurIPS 2024.
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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