Research
Relational learning, reasoning, generative AI, AI for mathematics, and AI for science, from theoretical foundations to applications in the life sciences.
Graphs & Relational Learning
Learning and reasoning over graphs, knowledge graphs, and relational databases, from the expressive power of graph neural networks to graph foundation models that transfer across graphs and tasks.
Learn MoreGenerative Modeling
Flow matching and related generative models for discrete, geometric, and topological data.
Learn MoreAI for Science
Machine learning for physical, chemical, and biological systems, with a particular focus on the life sciences.
Learn MoreAI for Mathematics
Machine learning for formal mathematics and theorem proving.
Learn MoreReasoning
How learning systems reason, from continuous reasoning in language models to neural model counting and logical query answering.
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