Chris J. Maddison
Assistant Professor, University of TorontoCIFAR AI Chair, Vector Institute
The success of large language models is driven by the abundance and natural structure of text. What does this tell us about our universe and ourselves? How can we use these insights to advance applications in other domains when data is scarce? I want to understand how the statistical structure of real-world data influences the emergence of capabilities in AIs as they train on vast, heterogeneous datasets. I also want to develop data-efficient learning systems and, with them, build foundational AIs for medicine and biology. I tend to publish at machine learning conferences (NeurIPS, ICML, ICLR).
Group Members
Prospective trainees, please read this.
- Margarita Colberg (Postdoc with Matthieu Schapira)
- Nikita Dhawan (PhD with Roger Grosse)
- Ayoub El Hanchi (PhD with Murat Erdogdu)
- Chuning Li (PhD)
- Yujia Ma (PhD with Rahul Krishnan)
- Anvith Thudi (PhD with Nicolas Papernot)
Former Group Members
For former group members that are not PhDs or postdocs, please read this.
- Dami Choi (former PhD) Member of Technical Staff at Transluce
- Honghua Dong (former PhD) HUAWEI TopMinds
- Haonan Duan (former PhD) Research Scientist at Isomorphic Labs
- Daniel Johnson (former PhD) Member of Technical Staff at Transluce
- Max Paulus (former PhD) Member of Technical Staff at Magic AI
- Yangjun Ruan (former PhD) Member of Technical Staff at Thinking Machines
- Leo Cotta (former postdoc) Research Scientist at Ellison Institute of Technology
- Karen Ullrich (former postdoc) Research Scientist at Facebook AI Research
- Giulia Zarpellon (former postdoc) Data Scientist at Eurecat