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Machine Learning Foundations

Our overarching goal is to lay the foundations for AI that contribute to the scientific understanding of medicine and therapeutic design, eventually enabling AI to learn on its own and acquire knowledge autonomously.

We focus on foundational innovation in artificial intelligence and machine learning with an emphasis on AI systems that are informed by geometry, structure, and grounded in medical knowledge. This involves building AI models, including pre-trained, self-supervised, multi-purpose, and multi-modal models trained at scale to enable broad generalization.

AI for Medicine | Individualized Diagnosis and Treatment

The state of a person is described with increasing precision incorporating modalities like genetic code, cellular atlases, molecular datasets, and therapeutics—the challenge is how to reason over these data to develop powerful disease diagnostics and empower new kinds of therapies. Our research creates new avenues for fusing knowledge and patient data to give the right patient the right treatment at the right time and have medicinal effects that are consistent from person to person and with results in the laboratory.

AI for Science | Scientific Discovery and Therapeutic Science

For centuries, the method of discovery—the fundamental practice of science that scientists use to explain the natural world systematically and logically—has remained largely the same. We are using AI to change that. The natural world is interconnected, from the various facets of genome regulation to the molecular and organismal levels. These interactions across different levels yield a bewildering degree of complexity. Our research seeks to disentangle this complexity, developing AI models that advance drug design and help develop new kinds of therapies.