Chris J. Maddison

Chris J. Maddison

Assistant Professor, University of Toronto
CIFAR AI Chair, Vector Institute

I study algorithms that learn to make good predictions in stubbornly complex settings like medicine or drug discovery. In domains like these, it is especially important to study methods that work when data is scarce and verification is expensive. That is my current focus. I tend to publish at machine learning conferences (NeurIPS, ICML, ICLR).

The success of large language models is driven by the abundance and natural structure of data. What does this tell us about our universe and ourselves? How can we use these insights to advance applications in other domains? I am interested in understanding how the statistical structure of real-world data influences the emergence of capabilities in AIs as they train on vast, heterogeneous datasets.

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