Machine Learning
Bareinboim will develop a new framework that will lead to more efficient and robust decision-making; Kroer will develop new algorithms based on game theory to solve real-world problems with multiple actors.
Kaffes was selected as part of the inaugural cohort in recognition of the impact and potential of his work on tail-latency scheduling.
This year’s award recognizes his work on “Efficient and Safe Agentic AI Infrastructure.”
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