We develop machine learning models based on statistical mechanics. The design of these models, which are resource efficient, is largely interpretable. Unlike black box models, they unveil the complex patterns that emerge from biological data.

Our models are based on biological data, and reversely enrich biological insights with quantitative and computational methods. They can be used to engineer artificial biomolecules, trace evolutionary paths, or guide directed evolution experiments.