Scientists often seek to draw causal inferences from structured data that is not independently and identically distributed, such as spatial data, network data, or molecular data. We develop geometric ...

We examine what becomes possible when AI controls the full loop of data generation and model training. Using MESA, our dataset of 20.9 billion antibody–pHLA interactions, we trained Vista transformer ...

For decades, drug discovery has been constrained by a simple fact: experiments are expensive, so you have to guess well. We built a system where you don't have to guess — testing a billion distinct m...

Biological machine learning is often bottlenecked by a lack of scaled data. One promising route to relieving data bottlenecks is through high throughput screens, which can experimentally test the acti...

A fundamental lesson of modern AI is that scale is essential: training bigger models on bigger datasets unlocks new capabilities. A fundamental lesson of AI engineering is that scaling up isn't trivia...

Advance large scale data generation for chemical and biological AI in a 3 year PhD. Work on the frontier of active learning, and develop novel probabilistic machine learning techniques for experiment ...

Calendar of the Machine Learning Department at Carnegie Mellon University