Topic: model predictions

Foundation models and synthetic data are increasingly used in scientific applications, yet the reliability of their outputs is often uncertain. This workshop investigates how model predictions and generated data can be incorporated into statistical inference without losing trustworthy uncertainty estimates. Researchers from several disciplines will study the implications of black-box models for inference and decision-making and develop methods for assessing and improving reliability.

Develop sequence-based deep-learning models of gene regulation and aging. Research predicts gene expression and chromatin accessibility from DNA, interprets genetic variation and connects model predictions with experiments, causal mechanisms and possible intervention points.