Richard Zemel
Also known as Richard S. Zemel, Rich Zemel
As of October 2026, Richard Zemel is a professor of computer science at Columbia University, where he directs the NSF AI Institute for Artificial and Natural Intelligence. He earned his doctorate at the University of Toronto in 1994, and his doctoral work developed the autoencoder, a method that trains a neural network to predict its own inputs. In 2013 he co-wrote Learning Fair Representations, a learning algorithm for fair classification that ICML honored with its Test of Time Award in 2023. He became the inaugural research director of the Vector Institute for Artificial Intelligence in 2017 and joined Columbia University in 2021.
Timeline
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1984–1994
From Harvard to Toronto
He completes a B.A. at Harvard in 1984 and then studies computer science at Toronto under Geoffrey Hinton. There his doctoral work develops the autoencoder, which trains a neural network to predict its own inputs.
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1994
Arizona faculty after postdocs
He receives his Ph.D. from Toronto in 1994, and after postdocs at the Salk Institute and Carnegie Mellon University, he joins the University of Arizona faculty.
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2013
Learning fair representations
He co-authors Learning Fair Representations, a paper that appears in 2013. The paper proposes a learning algorithm for fair classification.
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2017
Inaugural research director at Vector Institute
He is appointed the inaugural research director of the Vector Institute for Artificial Intelligence in 2017.
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2021
Joining Columbia University
He takes a leave from the University of Toronto to join Columbia University in 2021.
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May 2023
An NSF grant for ARNI
In May 2023, a Columbia-led team wins a $20M NSF grant to develop the AI Institute for Artificial and Natural Intelligence.
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2023
Honours from ICML and CAIAC
ICML gives its 2023 Test of Time Award to Learning Fair Representations. CAIAC gives him its 2023 Lifetime Achievement Award.
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As of October 2026
Columbia professor and ARNI director
He is a professor of computer science at Columbia University and directs the NSF AI Institute for Artificial and Natural Intelligence.
Sources and credits
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