Richard J. Chen

This paper introduces TRACE, a deep learning model for automated liver histopathology assessment in preclinical drug safety, achieving higher concordance than pathologists in lesion scoring and dose-response analysis.

This paper introduces GEESE, an AI model that imputes morphomolecular signatures in toxicology data, linking gene expression changes to drug-induced liver injuries.

The paper shows that task-evoked activations inflate task-state functional connectivity estimates and demonstrates that flexible modeling of hemodynamic responses effectively corrects this inflation.