Sizhe Liu

This paper develops a foundation model for microbial growth dynamics using self-supervised learning on diverse datasets, enabling accurate reconstruction and prediction of growth patterns and antibiotic responses.

This paper introduces a framework that uses large language models to generate and analyze spatial transcriptomics data, improving the simulation of cell behavior and prediction of interactions.

The paper integrates mechanistic modeling with machine learning to accelerate the discovery of design rules for pattern formation in living cells.